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https://proceedings.mlr.press/v202/feofanov23a.html | https://proceedings.mlr.press/v202/feofanov23a/feofanov23a.pdf | https://openreview.net/forum?id=vlGOVmL8uI | Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption | https://proceedings.mlr.press/v202/feofanov23a.html | Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux | https://proceedings.mlr.press/v202/feofanov23a.html | ICML 2023 | We propose a theoretical framework to analyze semi-supervised classification under the low density separation assumption in a high-dimensional regime. In particular, we introduce QLDS, a linear classification model, where the low density separation assumption is implemented via quadratic margin maximization. The algori... |
https://proceedings.mlr.press/v202/ferber23a.html | https://proceedings.mlr.press/v202/ferber23a/ferber23a.pdf | https://openreview.net/forum?id=sSwN4NrzZr | SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems | https://proceedings.mlr.press/v202/ferber23a.html | Aaron M Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner, Bistra Dilkina, Yuandong Tian | https://proceedings.mlr.press/v202/ferber23a.html | ICML 2023 | Optimization problems with nonlinear cost functions and combinatorial constraints appear in many real-world applications but remain challenging to solve efficiently compared to their linear counterparts. To bridge this gap, we propose $\textbf{\emph{\texttt{SurCo}}}$ that learns linear $\underline{\text{Sur}}$rogate co... |
https://proceedings.mlr.press/v202/fernandes23a.html | https://proceedings.mlr.press/v202/fernandes23a/fernandes23a.pdf | https://openreview.net/forum?id=SVCYSBgFIr | Scaling Laws for Multilingual Neural Machine Translation | https://proceedings.mlr.press/v202/fernandes23a.html | Patrick Fernandes, Behrooz Ghorbani, Xavier Garcia, Markus Freitag, Orhan Firat | https://proceedings.mlr.press/v202/fernandes23a.html | ICML 2023 | In this work, we provide a large-scale empirical study of the scaling properties of multilingual neural machine translation models. We examine how increases in the model size affect the model performance and investigate the role of the individual language pair weights on the scaling behavior. We find that these weights... |
https://proceedings.mlr.press/v202/fichtenberger23a.html | https://proceedings.mlr.press/v202/fichtenberger23a/fichtenberger23a.pdf | https://openreview.net/forum?id=Xqedp0Iu1S | Constant Matters: Fine-grained Error Bound on Differentially Private Continual Observation | https://proceedings.mlr.press/v202/fichtenberger23a.html | Hendrik Fichtenberger, Monika Henzinger, Jalaj Upadhyay | https://proceedings.mlr.press/v202/fichtenberger23a.html | ICML 2023 | We study fine-grained error bounds for differentially private algorithms for counting under continual observation. Our main insight is that the matrix mechanism when using lower-triangular matrices can be used in the continual observation model. More specifically, we give an explicit factorization for the counting matr... |
https://proceedings.mlr.press/v202/fiegel23a.html | https://proceedings.mlr.press/v202/fiegel23a/fiegel23a.pdf | https://openreview.net/forum?id=O1j4uFuSVW | Adapting to game trees in zero-sum imperfect information games | https://proceedings.mlr.press/v202/fiegel23a.html | Côme Fiegel, Pierre Menard, Tadashi Kozuno, Remi Munos, Vianney Perchet, Michal Valko | https://proceedings.mlr.press/v202/fiegel23a.html | ICML 2023 | Imperfect information games (IIG) are games in which each player only partially observes the current game state. We study how to learn $\epsilon$-optimal strategies in a zero-sum IIG through self-play with trajectory feedback. We give a problem-independent lower bound $\widetilde{\mathcal{O}}(H(A_{\mathcal{X}}+B_{\math... |
https://proceedings.mlr.press/v202/finzi23a.html | https://proceedings.mlr.press/v202/finzi23a/finzi23a.pdf | https://openreview.net/forum?id=sdhcjMzhHN | User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems | https://proceedings.mlr.press/v202/finzi23a.html | Marc Anton Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Nunez | https://proceedings.mlr.press/v202/finzi23a.html | ICML 2023 | Diffusion models are a class of probabilistic generative models that have been widely used as a prior for image processing tasks like text conditional generation and inpainting. We demonstrate that these models can be adapted to make predictions and provide uncertainty quantification for chaotic dynamical systems. In t... |
https://proceedings.mlr.press/v202/fontanella23a.html | https://proceedings.mlr.press/v202/fontanella23a/fontanella23a.pdf | https://openreview.net/forum?id=yrVIUwRtzy | ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging | https://proceedings.mlr.press/v202/fontanella23a.html | Alessandro Fontanella, Antreas Antoniou, Wenwen Li, Joanna Wardlaw, Grant Mair, Emanuele Trucco, Amos Storkey | https://proceedings.mlr.press/v202/fontanella23a.html | ICML 2023 | In some medical imaging tasks and other settings where only small parts of the image are informative for the classification task, traditional CNNs can sometimes struggle to generalise. Manually annotated Regions of Interest (ROI) are often used to isolate the most informative parts of the image. However, these are expe... |
https://proceedings.mlr.press/v202/forel23a.html | https://proceedings.mlr.press/v202/forel23a/forel23a.pdf | https://openreview.net/forum?id=4Lk9GHHueJ | Explainable Data-Driven Optimization: From Context to Decision and Back Again | https://proceedings.mlr.press/v202/forel23a.html | Alexandre Forel, Axel Parmentier, Thibaut Vidal | https://proceedings.mlr.press/v202/forel23a.html | ICML 2023 | Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. While a vast body of work is dedicated to interpreting machine learning models in the classification setting, explaining decision pipelines involving learning algorithms... |
https://proceedings.mlr.press/v202/foster23a.html | https://proceedings.mlr.press/v202/foster23a/foster23a.pdf | https://openreview.net/forum?id=8gOvb9PoPC | Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games | https://proceedings.mlr.press/v202/foster23a.html | Dylan J Foster, Noah Golowich, Sham M. Kakade | https://proceedings.mlr.press/v202/foster23a.html | ICML 2023 | We consider the problem of decentralized multi-agent reinforcement learning in Markov games. A fundamental question is whether there exist algorithms that, when run independently by all agents, lead to no-regret for each player, analogous to celebrated convergence results for no-regret learning in normal-form games. Wh... |
https://proceedings.mlr.press/v202/fotiadis23a.html | https://proceedings.mlr.press/v202/fotiadis23a/fotiadis23a.pdf | https://openreview.net/forum?id=PePBaTdFhc | Disentangled Generative Models for Robust Prediction of System Dynamics | https://proceedings.mlr.press/v202/fotiadis23a.html | Stathi Fotiadis, Mario Lino Valencia, Shunlong Hu, Stef Garasto, Chris D Cantwell, Anil Anthony Bharath | https://proceedings.mlr.press/v202/fotiadis23a.html | ICML 2023 | The use of deep neural networks for modelling system dynamics is increasingly popular, but long-term prediction accuracy and out-of-distribution generalization still present challenges. In this study, we address these challenges by considering the parameters of dynamical systems as factors of variation of the data and ... |
https://proceedings.mlr.press/v202/fournier23a.html | https://proceedings.mlr.press/v202/fournier23a/fournier23a.pdf | https://openreview.net/forum?id=qcU9ngAPGC | Can Forward Gradient Match Backpropagation? | https://proceedings.mlr.press/v202/fournier23a.html | Louis Fournier, Stephane Rivaud, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon | https://proceedings.mlr.press/v202/fournier23a.html | ICML 2023 | Forward Gradients - the idea of using directional derivatives in forward differentiation mode - have recently been shown to be utilizable for neural network training while avoiding problems generally associated with backpropagation gradient computation, such as locking and memorization requirements. The cost is the req... |
https://proceedings.mlr.press/v202/foussoul23a.html | https://proceedings.mlr.press/v202/foussoul23a/foussoul23a.pdf | https://openreview.net/forum?id=fnCwNbOs0S | Last Switch Dependent Bandits with Monotone Payoff Functions | https://proceedings.mlr.press/v202/foussoul23a.html | Ayoub Foussoul, Vineet Goyal, Orestis Papadigenopoulos, Assaf Zeevi | https://proceedings.mlr.press/v202/foussoul23a.html | ICML 2023 | In a recent work, Laforgue et al. introduce the model of last switch dependent (LSD) bandits, in an attempt to capture nonstationary phenomena induced by the interaction between the player and the environment. Examples include satiation, where consecutive plays of the same action lead to decreased performance, or depri... |
https://proceedings.mlr.press/v202/francazi23a.html | https://proceedings.mlr.press/v202/francazi23a/francazi23a.pdf | https://openreview.net/forum?id=jNpmHrHVWZ | A Theoretical Analysis of the Learning Dynamics under Class Imbalance | https://proceedings.mlr.press/v202/francazi23a.html | Emanuele Francazi, Marco Baity-Jesi, Aurelien Lucchi | https://proceedings.mlr.press/v202/francazi23a.html | ICML 2023 | Data imbalance is a common problem in machine learning that can have a critical effect on the performance of a model. Various solutions exist but their impact on the convergence of the learning dynamics is not understood. Here, we elucidate the significant negative impact of data imbalance on learning, showing that the... |
https://proceedings.mlr.press/v202/frantar23a.html | https://proceedings.mlr.press/v202/frantar23a/frantar23a.pdf | https://openreview.net/forum?id=gsP05g8IeK | SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot | https://proceedings.mlr.press/v202/frantar23a.html | Elias Frantar, Dan Alistarh | https://proceedings.mlr.press/v202/frantar23a.html | ICML 2023 | We show for the first time that large-scale generative pretrained transformer (GPT) family models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal loss of accuracy. This is achieved via a new pruning method called SparseGPT, specifically designed to work efficiently and accurately ... |
https://proceedings.mlr.press/v202/freed23a.html | https://proceedings.mlr.press/v202/freed23a/freed23a.pdf | https://openreview.net/forum?id=YeTYJz7th5 | Learning Temporally AbstractWorld Models without Online Experimentation | https://proceedings.mlr.press/v202/freed23a.html | Benjamin Freed, Siddarth Venkatraman, Guillaume Adrien Sartoretti, Jeff Schneider, Howie Choset | https://proceedings.mlr.press/v202/freed23a.html | ICML 2023 | Agents that can build temporally abstract representations of their environment are better able to understand their world and make plans on extended time scales, with limited computational power and modeling capacity. However, existing methods for automatically learning temporally abstract world models usually require m... |
https://proceedings.mlr.press/v202/freund23a.html | https://proceedings.mlr.press/v202/freund23a/freund23a.pdf | https://openreview.net/forum?id=laR6abCxIu | A Coupled Flow Approach to Imitation Learning | https://proceedings.mlr.press/v202/freund23a.html | Gideon Joseph Freund, Elad Sarafian, Sarit Kraus | https://proceedings.mlr.press/v202/freund23a.html | ICML 2023 | In reinforcement learning and imitation learning, an object of central importance is the state distribution induced by the policy. It plays a crucial role in the policy gradient theorem, and references to it–along with the related state-action distribution–can be found all across the literature. Despite its importance,... |
https://proceedings.mlr.press/v202/fu23a.html | https://proceedings.mlr.press/v202/fu23a/fu23a.pdf | https://openreview.net/forum?id=HwbKflLo6j | Simple Hardware-Efficient Long Convolutions for Sequence Modeling | https://proceedings.mlr.press/v202/fu23a.html | Daniel Y Fu, Elliot L Epstein, Eric Nguyen, Armin W Thomas, Michael Zhang, Tri Dao, Atri Rudra, Christopher Re | https://proceedings.mlr.press/v202/fu23a.html | ICML 2023 | State space models (SSMs) have high performance on long sequence modeling but require sophisticated initialization techniques and specialized implementations for high quality and runtime performance. We study whether a simple alternative can match SSMs in performance and efficiency: directly learning long convolutions ... |
https://proceedings.mlr.press/v202/fu23b.html | https://proceedings.mlr.press/v202/fu23b/fu23b.pdf | https://openreview.net/forum?id=OTZyQCwgNL | MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Poses | https://proceedings.mlr.press/v202/fu23b.html | Yang Fu, Ishan Misra, Xiaolong Wang | https://proceedings.mlr.press/v202/fu23b.html | ICML 2023 | We propose a generalizable neural radiance fields - MonoNeRF, that can be trained on large-scale monocular videos of moving in static scenes without any ground-truth annotations of depth and camera poses. MonoNeRF follows an Autoencoder-based architecture, where the encoder estimates the monocular depth and the camera ... |
https://proceedings.mlr.press/v202/fu23c.html | https://proceedings.mlr.press/v202/fu23c/fu23c.pdf | https://openreview.net/forum?id=JsAMuzA9o2 | Go Beyond Imagination: Maximizing Episodic Reachability with World Models | https://proceedings.mlr.press/v202/fu23c.html | Yao Fu, Run Peng, Honglak Lee | https://proceedings.mlr.press/v202/fu23c.html | ICML 2023 | Efficient exploration is a challenging topic in reinforcement learning, especially for sparse reward tasks. To deal with the reward sparsity, people commonly apply intrinsic rewards to motivate agents to explore the state space efficiently. In this paper, we introduce a new intrinsic reward design called GoBI - Go Beyo... |
https://proceedings.mlr.press/v202/fu23d.html | https://proceedings.mlr.press/v202/fu23d/fu23d.pdf | https://openreview.net/forum?id=MXuLl38AEm | Specializing Smaller Language Models towards Multi-Step Reasoning | https://proceedings.mlr.press/v202/fu23d.html | Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, Tushar Khot | https://proceedings.mlr.press/v202/fu23d.html | ICML 2023 | The surprising ability of Large Language Models (LLMs) to perform well on complex reasoning with only few-shot chain-of-thought prompts is believed to emerge only in very large-scale models. We show that such abilities can, in fact, be distilled down from GPT-3.5 (≥ 175B) to T5 variants (≤ 11B). We propose model specia... |
https://proceedings.mlr.press/v202/fu23e.html | https://proceedings.mlr.press/v202/fu23e/fu23e.pdf | https://openreview.net/forum?id=yg4k1kYbXe | Accelerated Stochastic Optimization Methods under Quasar-convexity | https://proceedings.mlr.press/v202/fu23e.html | Qiang Fu, Dongchu Xu, Ashia Camage Wilson | https://proceedings.mlr.press/v202/fu23e.html | ICML 2023 | Non-convex optimization plays a key role in a growing number of machine learning applications. This motivates the identification of specialized structure that enables sharper theoretical analysis. One such identified structure is quasar-convexity, a non-convex generalization of convexity that subsumes convex functions.... |
https://proceedings.mlr.press/v202/fu23f.html | https://proceedings.mlr.press/v202/fu23f/fu23f.pdf | https://openreview.net/forum?id=Rg5CRU2M4Z | Meta-learning Parameterized Skills | https://proceedings.mlr.press/v202/fu23f.html | Haotian Fu, Shangqun Yu, Saket Tiwari, Michael Littman, George Konidaris | https://proceedings.mlr.press/v202/fu23f.html | ICML 2023 | We propose a novel parameterized skill-learning algorithm that aims to learn transferable parameterized skills and synthesize them into a new action space that supports efficient learning in long-horizon tasks. We propose to leverage off-policy Meta-RL combined with a trajectory-centric smoothness term to learn a set o... |
https://proceedings.mlr.press/v202/fu23g.html | https://proceedings.mlr.press/v202/fu23g/fu23g.pdf | https://openreview.net/forum?id=cHhGmXDiHp | NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations | https://proceedings.mlr.press/v202/fu23g.html | Yonggan Fu, Ye Yuan, Souvik Kundu, Shang Wu, Shunyao Zhang, Yingyan Celine Lin | https://proceedings.mlr.press/v202/fu23g.html | ICML 2023 | Generalizable Neural Radiance Fields (GNeRF) are one of the most promising real-world solutions for novel view synthesis, thanks to their cross-scene generalization capability and thus the possibility of instant rendering on new scenes. While adversarial robustness is essential for real-world applications, little study... |
https://proceedings.mlr.press/v202/furelos-blanco23a.html | https://proceedings.mlr.press/v202/furelos-blanco23a/furelos-blanco23a.pdf | https://openreview.net/forum?id=qrH8ERUBcE | Hierarchies of Reward Machines | https://proceedings.mlr.press/v202/furelos-blanco23a.html | Daniel Furelos-Blanco, Mark Law, Anders Jonsson, Krysia Broda, Alessandra Russo | https://proceedings.mlr.press/v202/furelos-blanco23a.html | ICML 2023 | Reward machines (RMs) are a recent formalism for representing the reward function of a reinforcement learning task through a finite-state machine whose edges encode subgoals of the task using high-level events. The structure of RMs enables the decomposition of a task into simpler and independently solvable subtasks tha... |
https://proceedings.mlr.press/v202/gadhikar23a.html | https://proceedings.mlr.press/v202/gadhikar23a/gadhikar23a.pdf | https://openreview.net/forum?id=cKYIyT9wvo | Why Random Pruning Is All We Need to Start Sparse | https://proceedings.mlr.press/v202/gadhikar23a.html | Advait Harshal Gadhikar, Sohom Mukherjee, Rebekka Burkholz | https://proceedings.mlr.press/v202/gadhikar23a.html | ICML 2023 | Random masks define surprisingly effective sparse neural network models, as has been shown empirically. The resulting sparse networks can often compete with dense architectures and state-of-the-art lottery ticket pruning algorithms, even though they do not rely on computationally expensive prune-train iterations and ca... |
https://proceedings.mlr.press/v202/gallouedec23a.html | https://proceedings.mlr.press/v202/gallouedec23a/gallouedec23a.pdf | https://openreview.net/forum?id=4TtG42xJvC | Cell-Free Latent Go-Explore | https://proceedings.mlr.press/v202/gallouedec23a.html | Quentin Gallouédec, Emmanuel Dellandrea | https://proceedings.mlr.press/v202/gallouedec23a.html | ICML 2023 | In this paper, we introduce Latent Go-Explore (LGE), a simple and general approach based on the Go-Explore paradigm for exploration in reinforcement learning (RL). Go-Explore was initially introduced with a strong domain knowledge constraint for partitioning the state space into cells. However, in most real-world scena... |
https://proceedings.mlr.press/v202/gammelli23a.html | https://proceedings.mlr.press/v202/gammelli23a/gammelli23a.pdf | https://openreview.net/forum?id=rzN05i4GOE | Graph Reinforcement Learning for Network Control via Bi-Level Optimization | https://proceedings.mlr.press/v202/gammelli23a.html | Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone, Filipe Rodrigues, Francisco C. Pereira | https://proceedings.mlr.press/v202/gammelli23a.html | ICML 2023 | Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional optimization-based approaches do not scale to large networks, and (2) the design of good heuristics or approximation algorithms often requires... |
https://proceedings.mlr.press/v202/ganesh23a.html | https://proceedings.mlr.press/v202/ganesh23a/ganesh23a.pdf | https://openreview.net/forum?id=1d3O0b1rbL | Why Is Public Pretraining Necessary for Private Model Training? | https://proceedings.mlr.press/v202/ganesh23a.html | Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang | https://proceedings.mlr.press/v202/ganesh23a.html | ICML 2023 | In the privacy-utility tradeoff of a model trained on benchmark language and vision tasks, remarkable improvements have been widely reported when the model is pretrained on public data. Some gain is expected as these models inherit the benefits of transfer learning, which is the standard motivation in non-private setti... |
https://proceedings.mlr.press/v202/ganz23a.html | https://proceedings.mlr.press/v202/ganz23a/ganz23a.pdf | https://openreview.net/forum?id=9TbDVDX7de | Do Perceptually Aligned Gradients Imply Robustness? | https://proceedings.mlr.press/v202/ganz23a.html | Roy Ganz, Bahjat Kawar, Michael Elad | https://proceedings.mlr.press/v202/ganz23a.html | ICML 2023 | Adversarially robust classifiers possess a trait that non-robust models do not - Perceptually Aligned Gradients (PAG). Their gradients with respect to the input align well with human perception. Several works have identified PAG as a byproduct of robust training, but none have considered it as a standalone phenomenon n... |
https://proceedings.mlr.press/v202/gao23a.html | https://proceedings.mlr.press/v202/gao23a/gao23a.pdf | https://openreview.net/forum?id=AM1UcqDDDv | Solving Linear Programs with Fast Online Learning Algorithms | https://proceedings.mlr.press/v202/gao23a.html | Wenzhi Gao, Dongdong Ge, Chunlin Sun, Yinyu Ye | https://proceedings.mlr.press/v202/gao23a.html | ICML 2023 | This paper presents fast first-order methods for solving linear programs (LPs) approximately. We adapt online linear programming algorithms to offline LPs and obtain algorithms that avoid any matrix multiplication. We also introduce a variable-duplication technique that copies each variable $K$ times and reduces the op... |
https://proceedings.mlr.press/v202/gao23b.html | https://proceedings.mlr.press/v202/gao23b/gao23b.pdf | https://openreview.net/forum?id=DRMh8mVEav | Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks | https://proceedings.mlr.press/v202/gao23b.html | Yihang Gao, Yiqi Gu, Michael Ng | https://proceedings.mlr.press/v202/gao23b.html | ICML 2023 | The main aim of this paper is to conduct the convergence analysis of the gradient descent for two-layer physics-informed neural networks (PINNs). Here, the loss function involves derivatives of neural network outputs with respect to its inputs, so the interaction between the trainable parameters is more complicated com... |
https://proceedings.mlr.press/v202/gao23c.html | https://proceedings.mlr.press/v202/gao23c/gao23c.pdf | https://openreview.net/forum?id=2F3bt9s0iW | Generalizing Neural Wave Functions | https://proceedings.mlr.press/v202/gao23c.html | Nicholas Gao, Stephan Günnemann | https://proceedings.mlr.press/v202/gao23c.html | ICML 2023 | Recent neural network-based wave functions have achieved state-of-the-art accuracies in modeling ab-initio ground-state potential energy surface. However, these networks can only solve different spatial arrangements of the same set of atoms. To overcome this limitation, we present Graph-learned orbital embeddings (Glob... |
https://proceedings.mlr.press/v202/gao23d.html | https://proceedings.mlr.press/v202/gao23d/gao23d.pdf | https://openreview.net/forum?id=4JCKwAiRPX | On the Impact of Algorithmic Recourse on Social Segregation | https://proceedings.mlr.press/v202/gao23d.html | Ruijiang Gao, Himabindu Lakkaraju | https://proceedings.mlr.press/v202/gao23d.html | ICML 2023 | As predictive models seep into several real-world applications, it has become critical to ensure that individuals who are negatively impacted by the outcomes of these models are provided with a means for recourse. To this end, there has been a growing body of research on algorithmic recourse in recent years. While reco... |
https://proceedings.mlr.press/v202/gao23e.html | https://proceedings.mlr.press/v202/gao23e/gao23e.pdf | https://openreview.net/forum?id=RlqgQXZx6r | DDGR: Continual Learning with Deep Diffusion-based Generative Replay | https://proceedings.mlr.press/v202/gao23e.html | Rui Gao, Weiwei Liu | https://proceedings.mlr.press/v202/gao23e.html | ICML 2023 | Popular deep-learning models in the field of image classification suffer from catastrophic forgetting—models will forget previously acquired skills when learning new ones. Generative replay (GR), which typically consists of a generator and a classifier, is an efficient way to mitigate catastrophic forgetting. However, ... |
https://proceedings.mlr.press/v202/gao23f.html | https://proceedings.mlr.press/v202/gao23f/gao23f.pdf | https://openreview.net/forum?id=M1fd9Z00sj | PAL: Program-aided Language Models | https://proceedings.mlr.press/v202/gao23f.html | Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, Graham Neubig | https://proceedings.mlr.press/v202/gao23f.html | ICML 2023 | Large language models (LLMs) have demonstrated an impressive ability to perform arithmetic and symbolic reasoning tasks, when provided with a few examples at test time ("few-shot prompting"). Much of this success can be attributed to prompting methods such as "chain-of-thought", which employ LLMs for both understanding... |
https://proceedings.mlr.press/v202/gao23g.html | https://proceedings.mlr.press/v202/gao23g/gao23g.pdf | https://openreview.net/forum?id=4SHQv4cp3I | Out-of-Domain Robustness via Targeted Augmentations | https://proceedings.mlr.press/v202/gao23g.html | Irena Gao, Shiori Sagawa, Pang Wei Koh, Tatsunori Hashimoto, Percy Liang | https://proceedings.mlr.press/v202/gao23g.html | ICML 2023 | Models trained on one set of domains often suffer performance drops on unseen domains, e.g., when wildlife monitoring models are deployed in new camera locations. In this work, we study principles for designing data augmentations for out-of-domain (OOD) generalization. In particular, we focus on real-world scenarios in... |
https://proceedings.mlr.press/v202/gao23h.html | https://proceedings.mlr.press/v202/gao23h/gao23h.pdf | https://openreview.net/forum?id=bBLjms8nZE | Scaling Laws for Reward Model Overoptimization | https://proceedings.mlr.press/v202/gao23h.html | Leo Gao, John Schulman, Jacob Hilton | https://proceedings.mlr.press/v202/gao23h.html | ICML 2023 | In reinforcement learning from human feedback, it is common to optimize against a reward model trained to predict human preferences. Because the reward model is an imperfect proxy, optimizing its value too much can hinder ground truth performance, in accordance with Goodhart’s law. This effect has been frequently obser... |
https://proceedings.mlr.press/v202/garcia23a.html | https://proceedings.mlr.press/v202/garcia23a/garcia23a.pdf | https://openreview.net/forum?id=zvCSNsoyKW | The Unreasonable Effectiveness of Few-shot Learning for Machine Translation | https://proceedings.mlr.press/v202/garcia23a.html | Xavier Garcia, Yamini Bansal, Colin Cherry, George Foster, Maxim Krikun, Melvin Johnson, Orhan Firat | https://proceedings.mlr.press/v202/garcia23a.html | ICML 2023 | We demonstrate the potential of few-shot translation systems, trained with unpaired language data, for both high and low-resource language pairs. We show that with only 5 examples of high-quality translation data shown at inference, a transformer decoder-only model trained solely with self-supervised learning, is able ... |
https://proceedings.mlr.press/v202/garg23a.html | https://proceedings.mlr.press/v202/garg23a/garg23a.pdf | https://openreview.net/forum?id=b0xhqwNhez | RLSbench: Domain Adaptation Under Relaxed Label Shift | https://proceedings.mlr.press/v202/garg23a.html | Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton | https://proceedings.mlr.press/v202/garg23a.html | ICML 2023 | Despite the emergence of principled methods for domain adaptation under label shift, their sensitivity to shifts in class conditional distributions is precariously under explored. Meanwhile, popular deep domain adaptation heuristics tend to falter when faced with label proportions shifts. While several papers modify th... |
https://proceedings.mlr.press/v202/garrido23a.html | https://proceedings.mlr.press/v202/garrido23a/garrido23a.pdf | https://openreview.net/forum?id=neTWpgvVbo | RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank | https://proceedings.mlr.press/v202/garrido23a.html | Quentin Garrido, Randall Balestriero, Laurent Najman, Yann Lecun | https://proceedings.mlr.press/v202/garrido23a.html | ICML 2023 | Joint-Embedding Self Supervised Learning (JE-SSL) has seen a rapid development, with the emergence of many method variations but only few principled guidelines that would help practitioners to successfully deploy them. The main reason for that pitfall comes from JE-SSL’s core principle of not employing any input recons... |
https://proceedings.mlr.press/v202/garrido23b.html | https://proceedings.mlr.press/v202/garrido23b/garrido23b.pdf | https://openreview.net/forum?id=2sIVxJ9Hp0 | Self-supervised learning of Split Invariant Equivariant representations | https://proceedings.mlr.press/v202/garrido23b.html | Quentin Garrido, Laurent Najman, Yann Lecun | https://proceedings.mlr.press/v202/garrido23b.html | ICML 2023 | Recent progress has been made towards learning invariant or equivariant representations with self-supervised learning. While invariant methods are evaluated on large scale datasets, equivariant ones are evaluated in smaller, more controlled, settings. We aim at bridging the gap between the two in order to learn more di... |
https://proceedings.mlr.press/v202/gascon23a.html | https://proceedings.mlr.press/v202/gascon23a/gascon23a.pdf | https://openreview.net/forum?id=zN4oRCrlnM | Federated Heavy Hitter Recovery under Linear Sketching | https://proceedings.mlr.press/v202/gascon23a.html | Adria Gascon, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh | https://proceedings.mlr.press/v202/gascon23a.html | ICML 2023 | Motivated by real-life deployments of multi-round federated analytics with secure aggregation, we investigate the fundamental communication-accuracy tradeoffs of the heavy hitter discovery and approximate (open-domain) histogram problems under a linear sketching constraint. We propose efficient algorithms based on loca... |
https://proceedings.mlr.press/v202/gaur23a.html | https://proceedings.mlr.press/v202/gaur23a/gaur23a.pdf | https://openreview.net/forum?id=2azoCxs1jc | On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization | https://proceedings.mlr.press/v202/gaur23a.html | Mudit Gaur, Vaneet Aggarwal, Mridul Agarwal | https://proceedings.mlr.press/v202/gaur23a.html | ICML 2023 | Deep Q-learning based algorithms have been applied successfully in many decision making problems, while their theoretical foundations are not as well understood. In this paper, we study a Fitted Q-Iteration with two-layer ReLU neural network parameterization, and find the sample complexity guarantees for the algorithm.... |
https://proceedings.mlr.press/v202/ge23a.html | https://proceedings.mlr.press/v202/ge23a/ge23a.pdf | https://openreview.net/forum?id=hd8wCvtgIN | A Reinforcement Learning Framework for Dynamic Mediation Analysis | https://proceedings.mlr.press/v202/ge23a.html | Lin Ge, Jitao Wang, Chengchun Shi, Zhenke Wu, Rui Song | https://proceedings.mlr.press/v202/ge23a.html | ICML 2023 | Mediation analysis learns the causal effect transmitted via mediator variables between treatments and outcomes, and receives increasing attention in various scientific domains to elucidate causal relations. Most existing works focus on point-exposure studies where each subject only receives one treatment at a single ti... |
https://proceedings.mlr.press/v202/geffner23a.html | https://proceedings.mlr.press/v202/geffner23a/geffner23a.pdf | https://openreview.net/forum?id=5Q5wD1sAKj | Compositional Score Modeling for Simulation-Based Inference | https://proceedings.mlr.press/v202/geffner23a.html | Tomas Geffner, George Papamakarios, Andriy Mnih | https://proceedings.mlr.press/v202/geffner23a.html | ICML 2023 | Neural Posterior Estimation methods for simulation-based inference can be ill-suited for dealing with posterior distributions obtained by conditioning on multiple observations, as they tend to require a large number of simulator calls to learn accurate approximations. In contrast, Neural Likelihood Estimation methods c... |
https://proceedings.mlr.press/v202/geiping23a.html | https://proceedings.mlr.press/v202/geiping23a/geiping23a.pdf | https://openreview.net/forum?id=2snzoozOWH | Cramming: Training a Language Model on a single GPU in one day. | https://proceedings.mlr.press/v202/geiping23a.html | Jonas Geiping, Tom Goldstein | https://proceedings.mlr.press/v202/geiping23a.html | ICML 2023 | Recent trends in language modeling have focused on increasing performance through scaling, and have resulted in an environment where training language models is out of reach for most researchers and practitioners. While most in the community are asking how to push the limits of extreme computation, we ask the opposite ... |
https://proceedings.mlr.press/v202/geisler23a.html | https://proceedings.mlr.press/v202/geisler23a/geisler23a.pdf | https://openreview.net/forum?id=a7PVyayyfp | Transformers Meet Directed Graphs | https://proceedings.mlr.press/v202/geisler23a.html | Simon Geisler, Yujia Li, Daniel J Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru | https://proceedings.mlr.press/v202/geisler23a.html | ICML 2023 | Transformers were originally proposed as a sequence-to-sequence model for text but have become vital for a wide range of modalities, including images, audio, video, and undirected graphs. However, transformers for directed graphs are a surprisingly underexplored topic, despite their applicability to ubiquitous domains,... |
https://proceedings.mlr.press/v202/genewein23a.html | https://proceedings.mlr.press/v202/genewein23a/genewein23a.pdf | https://openreview.net/forum?id=gyHGzyIuEJ | Memory-Based Meta-Learning on Non-Stationary Distributions | https://proceedings.mlr.press/v202/genewein23a.html | Tim Genewein, Gregoire Deletang, Anian Ruoss, Li Kevin Wenliang, Elliot Catt, Vincent Dutordoir, Jordi Grau-Moya, Laurent Orseau, Marcus Hutter, Joel Veness | https://proceedings.mlr.press/v202/genewein23a.html | ICML 2023 | Memory-based meta-learning is a technique for approximating Bayes-optimal predictors. Under fairly general conditions, minimizing sequential prediction error, measured by the log loss, leads to implicit meta-learning. The goal of this work is to investigate how far this interpretation can be realized by current sequenc... |
https://proceedings.mlr.press/v202/geng23a.html | https://proceedings.mlr.press/v202/geng23a/geng23a.pdf | https://openreview.net/forum?id=gZXFNUcnHd | Towards Reliable Neural Specifications | https://proceedings.mlr.press/v202/geng23a.html | Chuqin Geng, Nham Le, Xiaojie Xu, Zhaoyue Wang, Arie Gurfinkel, Xujie Si | https://proceedings.mlr.press/v202/geng23a.html | ICML 2023 | Having reliable specifications is an unavoidable challenge in achieving verifiable correctness, robustness, and interpretability of AI systems. Existing specifications for neural networks are in the paradigm of data as specification. That is, the local neighborhood centering around a reference input is considered to be... |
https://proceedings.mlr.press/v202/gerstgrasser23a.html | https://proceedings.mlr.press/v202/gerstgrasser23a/gerstgrasser23a.pdf | https://openreview.net/forum?id=IJffiJTLhI | Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning | https://proceedings.mlr.press/v202/gerstgrasser23a.html | Matthias Gerstgrasser, David C. Parkes | https://proceedings.mlr.press/v202/gerstgrasser23a.html | ICML 2023 | Stackelberg equilibria arise naturally in a range of popular learning problems, such as in security games or indirect mechanism design, and have received increasing attention in the reinforcement learning literature. We present a general framework for implementing Stackelberg equilibria search as a multi-agent RL probl... |
https://proceedings.mlr.press/v202/ghadiri23a.html | https://proceedings.mlr.press/v202/ghadiri23a/ghadiri23a.pdf | https://openreview.net/forum?id=XjTcC4EA4P | Approximately Optimal Core Shapes for Tensor Decompositions | https://proceedings.mlr.press/v202/ghadiri23a.html | Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni | https://proceedings.mlr.press/v202/ghadiri23a.html | ICML 2023 | This work studies the combinatorial optimization problem of finding an optimal core tensor shape, also called multilinear rank, for a size-constrained Tucker decomposition. We give an algorithm with provable approximation guarantees for its reconstruction error via connections to higher-order singular values. Specifica... |
https://proceedings.mlr.press/v202/ghamizi23a.html | https://proceedings.mlr.press/v202/ghamizi23a/ghamizi23a.pdf | https://openreview.net/forum?id=320btOVW8R | GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks | https://proceedings.mlr.press/v202/ghamizi23a.html | Salah Ghamizi, Jingfeng Zhang, Maxime Cordy, Mike Papadakis, Masashi Sugiyama, Yves Le Traon | https://proceedings.mlr.press/v202/ghamizi23a.html | ICML 2023 | While leveraging additional training data is well established to improve adversarial robustness, it incurs the unavoidable cost of data collection and the heavy computation to train models. To mitigate the costs, we propose *Guided Adversarial Training * (GAT), a novel adversarial training technique that exploits auxil... |
https://proceedings.mlr.press/v202/ghazi23a.html | https://proceedings.mlr.press/v202/ghazi23a/ghazi23a.pdf | https://openreview.net/forum?id=KfkSyUJyqg | On User-Level Private Convex Optimization | https://proceedings.mlr.press/v202/ghazi23a.html | Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang | https://proceedings.mlr.press/v202/ghazi23a.html | ICML 2023 | We introduce a new mechanism for stochastic convex optimization (SCO) with user-level differential privacy guarantees. The convergence rates of this mechanism are similar to those in the prior work of Levy et al. 2021 and Narayanan et al. 2022, but with two important improvements. Our mechanism does not require any smo... |
https://proceedings.mlr.press/v202/ghosal23a.html | https://proceedings.mlr.press/v202/ghosal23a/ghosal23a.pdf | https://openreview.net/forum?id=s1hrcLUcld | Contextual Reliability: When Different Features Matter in Different Contexts | https://proceedings.mlr.press/v202/ghosal23a.html | Gaurav Rohit Ghosal, Amrith Setlur, Daniel S. Brown, Anca Dragan, Aditi Raghunathan | https://proceedings.mlr.press/v202/ghosal23a.html | ICML 2023 | Deep neural networks often fail catastrophically by relying on spurious correlations. Most prior work assumes a clear dichotomy into spurious and reliable features; however, this is often unrealistic. For example, most of the time we do not want an autonomous car to simply copy the speed of surrounding cars—we don’t wa... |
https://proceedings.mlr.press/v202/ghosh23a.html | https://proceedings.mlr.press/v202/ghosh23a/ghosh23a.pdf | https://openreview.net/forum?id=Ovu1horBiZ | Reinforcement Learning from Passive Data via Latent Intentions | https://proceedings.mlr.press/v202/ghosh23a.html | Dibya Ghosh, Chethan Anand Bhateja, Sergey Levine | https://proceedings.mlr.press/v202/ghosh23a.html | ICML 2023 | Passive observational data, such as human videos, is abundant and rich in information, yet remains largely untapped by current RL methods. Perhaps surprisingly, we show that passive data, despite not having reward or action labels, can still be used to learn features that accelerate downstream RL. Our approach learns f... |
https://proceedings.mlr.press/v202/ghosh23b.html | https://proceedings.mlr.press/v202/ghosh23b/ghosh23b.pdf | https://openreview.net/forum?id=qI0l2VKp7N | Harmonic Neural Networks | https://proceedings.mlr.press/v202/ghosh23b.html | Atiyo Ghosh, Antonio Andrea Gentile, Mario Dagrada, Chul Lee, Seong-Hyok Sean Kim, Hyukgeun Cha, Yunjun Choi, Dongho Kim, Jeong-Il Kye, Vincent Emanuel Elfving | https://proceedings.mlr.press/v202/ghosh23b.html | ICML 2023 | Harmonic functions are abundant in nature, appearing in limiting cases of Maxwell’s, Navier-Stokes equations, the heat and the wave equation. Consequently, there are many applications of harmonic functions from industrial process optimisation to robotic path planning and the calculation of first exit times of random wa... |
https://proceedings.mlr.press/v202/ghosh23c.html | https://proceedings.mlr.press/v202/ghosh23c/ghosh23c.pdf | https://openreview.net/forum?id=0SgBUsL4W0 | Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat | https://proceedings.mlr.press/v202/ghosh23c.html | Shantanu Ghosh, Ke Yu, Forough Arabshahi, Kayhan Batmanghelich | https://proceedings.mlr.press/v202/ghosh23c.html | ICML 2023 | ML model design either starts with an interpretable model or a Blackbox and explains it post hoc. Blackbox models are flexible but difficult to explain, while interpretable models are inherently explainable. Yet, interpretable models require extensive ML knowledge and tend to be less flexible, potentially underperformi... |
https://proceedings.mlr.press/v202/giannou23a.html | https://proceedings.mlr.press/v202/giannou23a/giannou23a.pdf | https://openreview.net/forum?id=fiHVIUkulb | Looped Transformers as Programmable Computers | https://proceedings.mlr.press/v202/giannou23a.html | Angeliki Giannou, Shashank Rajput, Jy-Yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris Papailiopoulos | https://proceedings.mlr.press/v202/giannou23a.html | ICML 2023 | We present a framework for using transformer networks as universal computers by programming them with specific weights and placing them in a loop. Our input sequence acts as a punchcard, consisting of instructions and memory for data read/writes. We demonstrate that a constant number of encoder layers can emulate basic... |
https://proceedings.mlr.press/v202/giuliani23a.html | https://proceedings.mlr.press/v202/giuliani23a/giuliani23a.pdf | https://openreview.net/forum?id=IP5OpHHpgV | Generalized Disparate Impact for Configurable Fairness Solutions in ML | https://proceedings.mlr.press/v202/giuliani23a.html | Luca Giuliani, Eleonora Misino, Michele Lombardi | https://proceedings.mlr.press/v202/giuliani23a.html | ICML 2023 | We make two contributions in the field of AI fairness over continuous protected attributes. First, we show that the Hirschfeld-Gebelein-Renyi (HGR) indicator (the only one currently available for such a case) is valuable but subject to a few crucial limitations regarding semantics, interpretability, and robustness. Sec... |
https://proceedings.mlr.press/v202/globus-harris23a.html | https://proceedings.mlr.press/v202/globus-harris23a/globus-harris23a.pdf | https://openreview.net/forum?id=RrusCGfAZ1 | Multicalibration as Boosting for Regression | https://proceedings.mlr.press/v202/globus-harris23a.html | Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell | https://proceedings.mlr.press/v202/globus-harris23a.html | ICML 2023 | We study the connection between multicalibration and boosting for squared error regression. First we prove a useful characterization of multicalibration in terms of a “swap regret” like condition on squared error. Using this characterization, we give an exceedingly simple algorithm that can be analyzed both as a boosti... |
https://proceedings.mlr.press/v202/gloeckler23a.html | https://proceedings.mlr.press/v202/gloeckler23a/gloeckler23a.pdf | https://openreview.net/forum?id=O7t2ZqUk7y | Adversarial robustness of amortized Bayesian inference | https://proceedings.mlr.press/v202/gloeckler23a.html | Manuel Gloeckler, Michael Deistler, Jakob H. Macke | https://proceedings.mlr.press/v202/gloeckler23a.html | ICML 2023 | Bayesian inference usually requires running potentially costly inference procedures separately for every new observation. In contrast, the idea of amortized Bayesian inference is to initially invest computational cost in training an inference network on simulated data, which can subsequently be used to rapidly perform ... |
https://proceedings.mlr.press/v202/gmelin23a.html | https://proceedings.mlr.press/v202/gmelin23a/gmelin23a.pdf | https://openreview.net/forum?id=kWS8mpioS9 | Efficient RL via Disentangled Environment and Agent Representations | https://proceedings.mlr.press/v202/gmelin23a.html | Kevin Gmelin, Shikhar Bahl, Russell Mendonca, Deepak Pathak | https://proceedings.mlr.press/v202/gmelin23a.html | ICML 2023 | Agents that are aware of the separation between the environments and themselves can leverage this understanding to form effective representations of visual input. We propose an approach for learning such structured representations for RL algorithms, using visual knowledge of the agent, which is often inexpensive to obt... |
https://proceedings.mlr.press/v202/go23a.html | https://proceedings.mlr.press/v202/go23a/go23a.pdf | https://openreview.net/forum?id=ttga7UlrsE | Aligning Language Models with Preferences through $f$-divergence Minimization | https://proceedings.mlr.press/v202/go23a.html | Dongyoung Go, Tomasz Korbak, Germàn Kruszewski, Jos Rozen, Nahyeon Ryu, Marc Dymetman | https://proceedings.mlr.press/v202/go23a.html | ICML 2023 | Aligning language models with preferences can be posed as approximating a target distribution representing some desired behavior. Existing approaches differ both in the functional form of the target distribution and the algorithm used to approximate it. For instance, Reinforcement Learning from Human Feedback (RLHF) co... |
https://proceedings.mlr.press/v202/goibert23a.html | https://proceedings.mlr.press/v202/goibert23a/goibert23a.pdf | https://openreview.net/forum?id=aIEL5ht9Sx | Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues | https://proceedings.mlr.press/v202/goibert23a.html | Morgane Goibert, Clément Calauzènes, Ekhine Irurozki, Stephan Clémençon | https://proceedings.mlr.press/v202/goibert23a.html | ICML 2023 | As the issue of robustness in AI systems becomes vital, statistical learning techniques that are reliable even in presence of partly contaminated data have to be developed. Preference data, in the form of (complete) rankings in the simplest situations, are no exception and the demand for appropriate concepts and tools ... |
https://proceedings.mlr.press/v202/gong23a.html | https://proceedings.mlr.press/v202/gong23a/gong23a.pdf | https://openreview.net/forum?id=hdGyjAnqZG | Learning Distributions over Quantum Measurement Outcomes | https://proceedings.mlr.press/v202/gong23a.html | Weiyuan Gong, Scott Aaronson | https://proceedings.mlr.press/v202/gong23a.html | ICML 2023 | Shadow tomography for quantum states provides a sample efficient approach for predicting the measurement outcomes of quantum systems. However, these shadow tomography procedures yield poor bounds if there are more than two outcomes per measurement. In this paper, we consider a general problem of learning properties fro... |
https://proceedings.mlr.press/v202/gorbunov23a.html | https://proceedings.mlr.press/v202/gorbunov23a/gorbunov23a.pdf | https://openreview.net/forum?id=dvu47LPkEV | Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity | https://proceedings.mlr.press/v202/gorbunov23a.html | Eduard Gorbunov, Adrien Taylor, Samuel Horváth, Gauthier Gidel | https://proceedings.mlr.press/v202/gorbunov23a.html | ICML 2023 | Algorithms for min-max optimization and variational inequalities are often studied under monotonicity assumptions. Motivated by non-monotone machine learning applications, we follow the line of works (Diakonikolas et al., 2021; Lee & Kim, 2021; Pethick et al., 2022; Bohm,2022) aiming at going beyond monotonicity by con... |
https://proceedings.mlr.press/v202/goshtasbpour23a.html | https://proceedings.mlr.press/v202/goshtasbpour23a/goshtasbpour23a.pdf | https://openreview.net/forum?id=x0AppdesIM | Adaptive Annealed Importance Sampling with Constant Rate Progress | https://proceedings.mlr.press/v202/goshtasbpour23a.html | Shirin Goshtasbpour, Victor Cohen, Fernando Perez-Cruz | https://proceedings.mlr.press/v202/goshtasbpour23a.html | ICML 2023 | Annealed Importance Sampling (AIS) synthesizes weighted samples from an intractable distribution given its unnormalized density function. This algorithm relies on a sequence of interpolating distributions bridging the target to an initial tractable distribution such as the well-known geometric mean path of unnormalized... |
https://proceedings.mlr.press/v202/graham23a.html | https://proceedings.mlr.press/v202/graham23a/graham23a.pdf | https://openreview.net/forum?id=HWhaVJA2eb | Formalizing Preferences Over Runtime Distributions | https://proceedings.mlr.press/v202/graham23a.html | Devon R. Graham, Kevin Leyton-Brown, Tim Roughgarden | https://proceedings.mlr.press/v202/graham23a.html | ICML 2023 | When trying to solve a computational problem, we are often faced with a choice between algorithms that are guaranteed to return the right answer but differ in their runtime distributions (e.g., SAT solvers, sorting algorithms). This paper aims to lay theoretical foundations for such choices by formalizing preferences o... |
https://proceedings.mlr.press/v202/grande23a.html | https://proceedings.mlr.press/v202/grande23a/grande23a.pdf | https://openreview.net/forum?id=q2L5r7WEHT | Topological Point Cloud Clustering | https://proceedings.mlr.press/v202/grande23a.html | Vincent Peter Grande, Michael T Schaub | https://proceedings.mlr.press/v202/grande23a.html | ICML 2023 | We present Topological Point Cloud Clustering (TPCC), a new method to cluster points in an arbitrary point cloud based on their contribution to global topological features. TPCC synthesizes desirable features from spectral clustering and topological data analysis and is based on considering the spectral properties of a... |
https://proceedings.mlr.press/v202/grenioux23a.html | https://proceedings.mlr.press/v202/grenioux23a/grenioux23a.pdf | https://openreview.net/forum?id=NfH2HRL8u6 | On Sampling with Approximate Transport Maps | https://proceedings.mlr.press/v202/grenioux23a.html | Louis Grenioux, Alain Oliviero Durmus, Eric Moulines, Marylou Gabrié | https://proceedings.mlr.press/v202/grenioux23a.html | ICML 2023 | Transport maps can ease the sampling of distributions with non-trivial geometries by transforming them into distributions that are easier to handle. The potential of this approach has risen with the development of Normalizing Flows (NF) which are maps parameterized with deep neural networks trained to push a reference ... |
https://proceedings.mlr.press/v202/grigsby23a.html | https://proceedings.mlr.press/v202/grigsby23a/grigsby23a.pdf | https://openreview.net/forum?id=rGL49h4x9h | Hidden Symmetries of ReLU Networks | https://proceedings.mlr.press/v202/grigsby23a.html | Elisenda Grigsby, Kathryn Lindsey, David Rolnick | https://proceedings.mlr.press/v202/grigsby23a.html | ICML 2023 | The parameter space for any fixed architecture of feedforward ReLU neural networks serves as a proxy during training for the associated class of functions - but how faithful is this representation? It is known that many different parameter settings $\theta$ can determine the same function $f$. Moreover, the degree of t... |
https://proceedings.mlr.press/v202/gruntkowska23a.html | https://proceedings.mlr.press/v202/gruntkowska23a/gruntkowska23a.pdf | https://openreview.net/forum?id=kdkkLwyJe1 | EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression | https://proceedings.mlr.press/v202/gruntkowska23a.html | Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik | https://proceedings.mlr.press/v202/gruntkowska23a.html | ICML 2023 | In this work we focus our attention on distributed optimization problems in the context where the communication time between the server and the workers is non-negligible. We obtain novel methods supporting bidirectional compression (both from the server to the workers and vice versa) that enjoy new state-of-the-art the... |
https://proceedings.mlr.press/v202/gu23a.html | https://proceedings.mlr.press/v202/gu23a/gu23a.pdf | https://openreview.net/forum?id=cZZfXm6wZm | NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion | https://proceedings.mlr.press/v202/gu23a.html | Jiatao Gu, Alex Trevithick, Kai-En Lin, Joshua M. Susskind, Christian Theobalt, Lingjie Liu, Ravi Ramamoorthi | https://proceedings.mlr.press/v202/gu23a.html | ICML 2023 | Novel view synthesis from a single image requires inferring occluded regions of objects and scenes whilst simultaneously maintaining semantic and physical consistency with the input. Existing approaches condition neural radiance fields (NeRF) on local image features, projecting points to the input image plane, and aggr... |
https://proceedings.mlr.press/v202/guan23a.html | https://proceedings.mlr.press/v202/guan23a/guan23a.pdf | https://openreview.net/forum?id=9qy9DizMlr | DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design | https://proceedings.mlr.press/v202/guan23a.html | Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu | https://proceedings.mlr.press/v202/guan23a.html | ICML 2023 | Designing 3D ligands within a target binding site is a fundamental task in drug discovery. Existing structured-based drug design methods treat all ligand atoms equally, which ignores different roles of atoms in the ligand for drug design and can be less efficient for exploring the large drug-like molecule space. In thi... |
https://proceedings.mlr.press/v202/guha23a.html | https://proceedings.mlr.press/v202/guha23a/guha23a.pdf | https://openreview.net/forum?id=X9enIC31dY | On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances | https://proceedings.mlr.press/v202/guha23a.html | Aritra Guha, Nhat Ho, Xuanlong Nguyen | https://proceedings.mlr.press/v202/guha23a.html | ICML 2023 | Dirichlet Process mixture models (DPMM) in combination with Gaussian kernels have been an important modeling tool for numerous data domains arising from biological, physical, and social sciences. However, this versatility in applications does not extend to strong theoretical guarantees for the underlying parameter esti... |
https://proceedings.mlr.press/v202/guha23b.html | https://proceedings.mlr.press/v202/guha23b/guha23b.pdf | https://openreview.net/forum?id=u1fhtP15l5 | Conformalization of Sparse Generalized Linear Models | https://proceedings.mlr.press/v202/guha23b.html | Etash Kumar Guha, Eugene Ndiaye, Xiaoming Huo | https://proceedings.mlr.press/v202/guha23b.html | ICML 2023 | Given a sequence of observable variables $\{(x_1, y_1), \ldots, (x_n, y_n)\}$, the conformal prediction method estimates a confidence set for $y_{n+1}$ given $x_{n+1}$ that is valid for any finite sample size by merely assuming that the joint distribution of the data is permutation invariant. Although attractive, compu... |
https://proceedings.mlr.press/v202/guo23a.html | https://proceedings.mlr.press/v202/guo23a/guo23a.pdf | https://openreview.net/forum?id=Otdp5SGQMr | Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design | https://proceedings.mlr.press/v202/guo23a.html | Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Michael Rabbat | https://proceedings.mlr.press/v202/guo23a.html | ICML 2023 | In private federated learning (FL), a server aggregates differentially private updates from a large number of clients in order to train a machine learning model. The main challenge in this setting is balancing privacy with both classification accuracy of the learnt model as well as the number of bits communicated betwe... |
https://proceedings.mlr.press/v202/guo23b.html | https://proceedings.mlr.press/v202/guo23b/guo23b.pdf | https://openreview.net/forum?id=JC05k0E2EM | Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships | https://proceedings.mlr.press/v202/guo23b.html | Yaming Guo, Kai Guo, Xiaofeng Cao, Tieru Wu, Yi Chang | https://proceedings.mlr.press/v202/guo23b.html | ICML 2023 | Out-of-distribution generalization is challenging for non-participating clients of federated learning under distribution shifts. A proven strategy is to explore those invariant relationships between input and target variables, working equally well for non-participating clients. However, learning invariant relationships... |
https://proceedings.mlr.press/v202/guo23c.html | https://proceedings.mlr.press/v202/guo23c/guo23c.pdf | https://openreview.net/forum?id=C7fNCYdptO | FeDXL: Provable Federated Learning for Deep X-Risk Optimization | https://proceedings.mlr.press/v202/guo23c.html | Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang | https://proceedings.mlr.press/v202/guo23c.html | ICML 2023 | In this paper, we tackle a novel federated learning (FL) problem for optimizing a family of X-risks, to which no existing FL algorithms are applicable. In particular, the objective has the form of $\mathbb{E}_{\mathbf{z}\sim \mathcal{S}_1} f(\mathbb{E}_{\mathbf{z}’\sim\mathcal{S}_2} \ell(\mathbf{w}; \mathbf{z}, \mathbf... |
https://proceedings.mlr.press/v202/guo23d.html | https://proceedings.mlr.press/v202/guo23d/guo23d.pdf | https://openreview.net/forum?id=MI5YpKX84O | Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP | https://proceedings.mlr.press/v202/guo23d.html | Jiacheng Guo, Zihao Li, Huazheng Wang, Mengdi Wang, Zhuoran Yang, Xuezhou Zhang | https://proceedings.mlr.press/v202/guo23d.html | ICML 2023 | In this paper, we study representation learning in partially observable Markov Decision Processes (POMDPs), where the agent learns a decoder function that maps a series of high-dimensional raw observations to a compact representation and uses it for more efficient exploration and planning. We focus our attention on the... |
https://proceedings.mlr.press/v202/guo23e.html | https://proceedings.mlr.press/v202/guo23e/guo23e.pdf | https://openreview.net/forum?id=Fry8Yz5Ngl | Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano | https://proceedings.mlr.press/v202/guo23e.html | Chuan Guo, Alexandre Sablayrolles, Maziar Sanjabi | https://proceedings.mlr.press/v202/guo23e.html | ICML 2023 | Differential privacy (DP) is by far the most widely accepted framework for mitigating privacy risks in machine learning. However, exactly how small the privacy parameter $\epsilon$ needs to be to protect against certain privacy risks in practice is still not well-understood. In this work, we study data reconstruction a... |
https://proceedings.mlr.press/v202/guo23f.html | https://proceedings.mlr.press/v202/guo23f/guo23f.pdf | https://openreview.net/forum?id=TAwB7FsoJt | Linkless Link Prediction via Relational Distillation | https://proceedings.mlr.press/v202/guo23f.html | Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh V Chawla, Neil Shah, Tong Zhao | https://proceedings.mlr.press/v202/guo23f.html | ICML 2023 | Graph Neural Networks (GNNs) have shown exceptional performance in the task of link prediction. Despite their effectiveness, the high latency brought by non-trivial neighborhood data dependency limits GNNs in practical deployments. Conversely, the known efficient MLPs are much less effective than GNNs due to the lack o... |
https://proceedings.mlr.press/v202/guo23g.html | https://proceedings.mlr.press/v202/guo23g/guo23g.pdf | https://openreview.net/forum?id=nDKoVwNjMH | FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction | https://proceedings.mlr.press/v202/guo23g.html | Yongxin Guo, Xiaoying Tang, Tao Lin | https://proceedings.mlr.press/v202/guo23g.html | ICML 2023 | Federated Learning (FL) is a way for machines to learn from data that is kept locally, in order to protect the privacy of clients. This is typically done using local SGD, which helps to improve communication efficiency. However, such a scheme is currently constrained by slow and unstable convergence due to the variety ... |
https://proceedings.mlr.press/v202/guo23h.html | https://proceedings.mlr.press/v202/guo23h/guo23h.pdf | https://openreview.net/forum?id=G3vwtUqvrk | Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction | https://proceedings.mlr.press/v202/guo23h.html | Minghao Guo, Veronika Thost, Samuel W Song, Adithya Balachandran, Payel Das, Jie Chen, Wojciech Matusik | https://proceedings.mlr.press/v202/guo23h.html | ICML 2023 | The prediction of molecular properties is a crucial task in the field of material and drug discovery. The potential benefits of using deep learning techniques are reflected in the wealth of recent literature. Still, these techniques are faced with a common challenge in practice: Labeled data are limited by the cost of ... |
https://proceedings.mlr.press/v202/guo23i.html | https://proceedings.mlr.press/v202/guo23i/guo23i.pdf | https://openreview.net/forum?id=UjQIoJv927 | Graph Neural Networks with Learnable and Optimal Polynomial Bases | https://proceedings.mlr.press/v202/guo23i.html | Yuhe Guo, Zhewei Wei | https://proceedings.mlr.press/v202/guo23i.html | ICML 2023 | Polynomial filters, a kind of Graph Neural Networks, typically use a predetermined polynomial basis and learn the coefficients from the training data. It has been observed that the effectiveness of the model is highly dependent on the property of the polynomial basis. Consequently, two natural and fundamental questions... |
https://proceedings.mlr.press/v202/guo23j.html | https://proceedings.mlr.press/v202/guo23j/guo23j.pdf | https://openreview.net/forum?id=6XwCseSnww | LongCoder: A Long-Range Pre-trained Language Model for Code Completion | https://proceedings.mlr.press/v202/guo23j.html | Daya Guo, Canwen Xu, Nan Duan, Jian Yin, Julian Mcauley | https://proceedings.mlr.press/v202/guo23j.html | ICML 2023 | In this paper, we introduce a new task for code completion that focuses on handling long code input and propose a sparse Transformer model, called LongCoder, to address this task. LongCoder employs a sliding window mechanism for self-attention and introduces two types of globally accessible tokens - bridge tokens and m... |
https://proceedings.mlr.press/v202/guo23k.html | https://proceedings.mlr.press/v202/guo23k/guo23k.pdf | https://openreview.net/forum?id=DDwSa7XDxA | Estimating Heterogeneous Treatment Effects: Mutual Information Bounds and Learning Algorithms | https://proceedings.mlr.press/v202/guo23k.html | Xingzhuo Guo, Yuchen Zhang, Jianmin Wang, Mingsheng Long | https://proceedings.mlr.press/v202/guo23k.html | ICML 2023 | Estimating heterogeneous treatment effects (HTE) from observational studies is rising in importance due to the widespread accumulation of data in many fields. Due to the selection bias behind the inaccessibility of counterfactual data, the problem differs fundamentally from supervised learning in a challenging way. How... |
https://proceedings.mlr.press/v202/guo23l.html | https://proceedings.mlr.press/v202/guo23l/guo23l.pdf | https://openreview.net/forum?id=VOcPCpmEnZ | Identifying Useful Learnwares for Heterogeneous Label Spaces | https://proceedings.mlr.press/v202/guo23l.html | Lan-Zhe Guo, Zhi Zhou, Yu-Feng Li, Zhi-Hua Zhou | https://proceedings.mlr.press/v202/guo23l.html | ICML 2023 | The learnware paradigm aims to build a learnware market containing numerous learnwares, each of which is a well-performing machine learning model with a corresponding specification to describe its functionality so that future users can identify useful models for reuse according to their own requirements. With the learn... |
https://proceedings.mlr.press/v202/gupta23a.html | https://proceedings.mlr.press/v202/gupta23a/gupta23a.pdf | https://openreview.net/forum?id=wGgIcftFzm | High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors | https://proceedings.mlr.press/v202/gupta23a.html | Shivam Gupta, Jasper C.H. Lee, Eric Price | https://proceedings.mlr.press/v202/gupta23a.html | ICML 2023 | In location estimation, we are given $n$ samples from a known distribution $f$ shifted by an unknown translation $\lambda$, and want to estimate $\lambda$ as precisely as possible. Asymptotically, the maximum likelihood estimate achieves the Cramér-Rao bound of error $\mathcal N(0, \frac{1}{n\mathcal I})$, where $\math... |
https://proceedings.mlr.press/v202/gupta23b.html | https://proceedings.mlr.press/v202/gupta23b/gupta23b.pdf | https://openreview.net/forum?id=pNi4q28UyI | GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets | https://proceedings.mlr.press/v202/gupta23b.html | Shubham Gupta, Sahil Manchanda, Sayan Ranu, Srikanta J. Bedathur | https://proceedings.mlr.press/v202/gupta23b.html | ICML 2023 | Graph neural networks (GNNs), in general, are built on the assumption of a static set of features characterizing each node in a graph. This assumption is often violated in practice. Existing methods partly address this issue through feature imputation. However, these techniques (i) assume uniformity of feature set acro... |
https://proceedings.mlr.press/v202/gupta23c.html | https://proceedings.mlr.press/v202/gupta23c/gupta23c.pdf | https://openreview.net/forum?id=ZXXPQ8GptX | Online Platt Scaling with Calibeating | https://proceedings.mlr.press/v202/gupta23c.html | Chirag Gupta, Aaditya Ramdas | https://proceedings.mlr.press/v202/gupta23c.html | ICML 2023 | We present an online post-hoc calibration method, called Online Platt Scaling (OPS), which combines the Platt scaling technique with online logistic regression. We demonstrate that OPS smoothly adapts between i.i.d. and non-i.i.d. settings with distribution drift. Further, in scenarios where the best Platt scaling mode... |
https://proceedings.mlr.press/v202/gurulingan23a.html | https://proceedings.mlr.press/v202/gurulingan23a/gurulingan23a.pdf | https://openreview.net/forum?id=LZhwwe7j9l | Multi-Task Structural Learning using Local Task Similarity induced Neuron Creation and Removal | https://proceedings.mlr.press/v202/gurulingan23a.html | Naresh Kumar Gurulingan, Bahram Zonooz, Elahe Arani | https://proceedings.mlr.press/v202/gurulingan23a.html | ICML 2023 | Multi-task learning has the potential to improve generalization by maximizing positive transfer between tasks while reducing task interference. Fully achieving this potential is hindered by manually designed architectures that remain static throughout training. On the contrary, learning in the brain occurs through stru... |
https://proceedings.mlr.press/v202/guth23a.html | https://proceedings.mlr.press/v202/guth23a/guth23a.pdf | https://openreview.net/forum?id=WHVHiOR3XQ | Conditionally Strongly Log-Concave Generative Models | https://proceedings.mlr.press/v202/guth23a.html | Florentin Guth, Etienne Lempereur, Joan Bruna, Stéphane Mallat | https://proceedings.mlr.press/v202/guth23a.html | ICML 2023 | There is a growing gap between the impressive results of deep image generative models and classical algorithms that offer theoretical guarantees. The former suffer from mode collapse or memorization issues, limiting their application to scientific data. The latter require restrictive assumptions such as log-concavity t... |
https://proceedings.mlr.press/v202/gutteridge23a.html | https://proceedings.mlr.press/v202/gutteridge23a/gutteridge23a.pdf | https://openreview.net/forum?id=WEgjbJ6IDN | DRew: Dynamically Rewired Message Passing with Delay | https://proceedings.mlr.press/v202/gutteridge23a.html | Benjamin Gutteridge, Xiaowen Dong, Michael M. Bronstein, Francesco Di Giovanni | https://proceedings.mlr.press/v202/gutteridge23a.html | ICML 2023 | Message passing neural networks (MPNNs) have been shown to suffer from the phenomenon of over-squashing that causes poor performance for tasks relying on long-range interactions. This can be largely attributed to message passing only occurring locally, over a node’s immediate neighbours. Rewiring approaches attempting ... |
https://proceedings.mlr.press/v202/guyomard23a.html | https://proceedings.mlr.press/v202/guyomard23a/guyomard23a.pdf | https://openreview.net/forum?id=XW4R4LVKhw | Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network | https://proceedings.mlr.press/v202/guyomard23a.html | Marie Guyomard, Susana Barbosa, Lionel Fillatre | https://proceedings.mlr.press/v202/guyomard23a.html | ICML 2023 | This paper proposes an understandable neural network whose score function is modeled as an additive sum of univariate spline functions. It extends usual understandable models like generative additive models, spline-based models, and neural additive models. It is shown that this neural network can be approximated by a l... |
https://proceedings.mlr.press/v202/h-zargarbashi23a.html | https://proceedings.mlr.press/v202/h-zargarbashi23a/h-zargarbashi23a.pdf | https://openreview.net/forum?id=zGf8J0bNfX | Conformal Prediction Sets for Graph Neural Networks | https://proceedings.mlr.press/v202/h-zargarbashi23a.html | Soroush H. Zargarbashi, Simone Antonelli, Aleksandar Bojchevski | https://proceedings.mlr.press/v202/h-zargarbashi23a.html | ICML 2023 | Despite the widespread use of graph neural networks (GNNs) we lack methods to reliably quantify their uncertainty. We propose a conformal procedure to equip GNNs with prediction sets that come with distribution-free guarantees – the output set contains the true label with arbitrarily high probability. Our post-processi... |
https://proceedings.mlr.press/v202/ha23a.html | https://proceedings.mlr.press/v202/ha23a/ha23a.pdf | https://openreview.net/forum?id=hbgD1Wdcaq | Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning | https://proceedings.mlr.press/v202/ha23a.html | Seungwoong Ha, Hawoong Jeong | https://proceedings.mlr.press/v202/ha23a.html | ICML 2023 | How have individuals of social animals in nature evolved to learn from each other, and what would be the optimal strategy for such learning in a specific environment? Here, we address both problems by employing a deep reinforcement learning model to optimize the social learning strategies (SLSs) of agents in a cooperat... |
https://proceedings.mlr.press/v202/haider23a.html | https://proceedings.mlr.press/v202/haider23a/haider23a.pdf | https://openreview.net/forum?id=ExwHyYdsmT | Convex Geometry of ReLU-layers, Injectivity on the Ball and Local Reconstruction | https://proceedings.mlr.press/v202/haider23a.html | Daniel Haider, Martin Ehler, Peter Balazs | https://proceedings.mlr.press/v202/haider23a.html | ICML 2023 | The paper uses a frame-theoretic setting to study the injectivity of a ReLU-layer on the closed ball of $\mathbb{R}^n$ and its non-negative part. In particular, the interplay between the radius of the ball and the bias vector is emphasized. Together with a perspective from convex geometry, this leads to a computational... |
https://proceedings.mlr.press/v202/hamman23a.html | https://proceedings.mlr.press/v202/hamman23a/hamman23a.pdf | https://openreview.net/forum?id=gzjK23oK9i | Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees | https://proceedings.mlr.press/v202/hamman23a.html | Faisal Hamman, Erfaun Noorani, Saumitra Mishra, Daniele Magazzeni, Sanghamitra Dutta | https://proceedings.mlr.press/v202/hamman23a.html | ICML 2023 | There is an emerging interest in generating robust counterfactual explanations that would remain valid if the model is updated or changed even slightly. Towards finding robust counterfactuals, existing literature often assumes that the original model $m$ and the new model $M$ are bounded in the parameter space, i.e., $... |
https://proceedings.mlr.press/v202/han23a.html | https://proceedings.mlr.press/v202/han23a/han23a.pdf | https://openreview.net/forum?id=VorD7k3Ldh | Wrapped Cauchy Distributed Angular Softmax for Long-Tailed Visual Recognition | https://proceedings.mlr.press/v202/han23a.html | Boran Han | https://proceedings.mlr.press/v202/han23a.html | ICML 2023 | Addressing imbalanced or long-tailed data is a major challenge in visual recognition tasks due to disparities between training and testing distributions and issues with data noise. We propose the Wrapped Cauchy Distributed Angular Softmax (WCDAS), a novel softmax function that incorporates data-wise Gaussian-based kern... |
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