Most Cited ICML 2024 "knowledge cutoff" Papers
2,635 papers found • Page 1 of 14
Conference
Position: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning
Micah Goldblum, Marc Finzi, Keefer Rowan et al.
Time Weaver: A Conditional Time Series Generation Model
Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin et al.
Cascade-CLIP: Cascaded Vision-Language Embeddings Alignment for Zero-Shot Semantic Segmentation
Yunheng Li, Zhong-Yu Li, Quan-Sheng Zeng et al.
Data-efficient Large Vision Models through Sequential Autoregression
Zhiwei Hao, Jianyuan Guo, Chengcheng Wang et al.
Minimum-Norm Interpolation Under Covariate Shift
Neil Mallinar, Austin Zane, Spencer Frei et al.
Understanding Inter-Concept Relationships in Concept-Based Models
Naveen Raman, Mateo Espinosa Zarlenga, Mateja Jamnik
Learning from Integral Losses in Physics Informed Neural Networks
Ehsan Saleh, Saba Ghaffari, Timothy Bretl et al.
Faster Sampling via Stochastic Gradient Proximal Sampler
Xunpeng Huang, Difan Zou, Hanze Dong et al.
Prompt-based Visual Alignment for Zero-shot Policy Transfer
Haihan Gao, Rui Zhang, Qi Yi et al.
Automated Statistical Model Discovery with Language Models
Michael Li, Emily Fox, Noah Goodman
Nash Learning from Human Feedback
REMI MUNOS, Michal Valko, Daniele Calandriello et al.
MusicRL: Aligning Music Generation to Human Preferences
Geoffrey Cideron, Sertan Girgin, Mauro Verzetti et al.
LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery
Pingchuan Ma, Johnson Tsun-Hsuan Wang, Minghao Guo et al.
Kernel-Based Evaluation of Conditional Biological Sequence Models
Pierre Glaser, Steffan Paul, Alissa M. Hummer et al.
Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Collaborative Filtering
Haoxuan Li, Chunyuan Zheng, Shuyi Wang et al.
Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching
Yuchen Zhang, Tianle Zhang, Kai Wang et al.
WARM: On the Benefits of Weight Averaged Reward Models
Alexandre Rame, Nino Vieillard, Léonard Hussenot et al.
Batch and match: black-box variational inference with a score-based divergence
Diana Cai, Chirag Modi, Loucas Pillaud-Vivien et al.
Discovering Environments with XRM
Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim et al.
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
Wei-Lin Chiang, Lianmin Zheng, Ying Sheng et al.
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Tri Dao, Albert Gu
Combinatorial Approximations for Cluster Deletion: Simpler, Faster, and Better
Vicente Balmaseda, Ying Xu, Yixin Cao et al.
MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts
Jianan Zhou, Zhiguang Cao, Yaoxin Wu et al.
Hybrid$^2$ Neural ODE Causal Modeling and an Application to Glycemic Response
Junyi Zou, Matthew Levine, Dessi Zaharieva et al.
Stereographic Spherical Sliced Wasserstein Distances
Huy Tran, Yikun Bai, Abihith Kothapalli et al.
Distribution Alignment Optimization through Neural Collapse for Long-tailed Classification
Jintong Gao, He Zhao, Dandan Guo et al.
How Learning by Reconstruction Produces Uninformative Features For Perception
Randall Balestriero, Yann LeCun
Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing
Youwei Shu, Xi Xiao, Derui Wang et al.
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference
JIAN XU, Delu Zeng, John Paisley
Fool Your (Vision and) Language Model with Embarrassingly Simple Permutations
Yongshuo Zong, Tingyang Yu, Ruchika Chavhan et al.
Consistent Submodular Maximization
PAUL DUETTING, Federico Fusco, Silvio Lattanzi et al.
Calibration Bottleneck: Over-compressed Representations are Less Calibratable
Deng-Bao Wang, Min-Ling Zhang
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay
Natasha Butt, Blazej Manczak, Auke Wiggers et al.
Active Preference Learning for Large Language Models
William Muldrew, Peter Hayes, Mingtian Zhang et al.
InfoNet: Neural Estimation of Mutual Information without Test-Time Optimization
Zhengyang Hu, Song Kang, Qunsong Zeng et al.
Position: A Call for Embodied AI
Giuseppe Paolo, Jonas Gonzalez-Billandon, Balázs Kégl
Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts
Hyunsu Kim, Ye Gon Kim, Hongseok Yang et al.
Incorporating Information into Shapley Values: Reweighting via a Maximum Entropy Approach
Darya Biparva, Donatello Materassi
Position: Why We Must Rethink Empirical Research in Machine Learning
Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger et al.
HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning
Shengchao Hu, Ziqing Fan, Li Shen et al.
Q-value Regularized Transformer for Offline Reinforcement Learning
Shengchao Hu, Ziqing Fan, Chaoqin Huang et al.
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization
Ziqing Fan, Shengchao Hu, Jiangchao Yao et al.
Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints
Yunsheng Tian, Ane Zuniga, Xinwei Zhang et al.
Understanding Reasoning Ability of Language Models From the Perspective of Reasoning Paths Aggregation
Xinyi Wang, Alfonso Amayuelas, Kexun Zhang et al.
Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams
Brian Cho, Kyra Gan, Nathan Kallus
From Vision to Audio and Beyond: A Unified Model for Audio-Visual Representation and Generation
Kun Su, Xiulong Liu, Eli Shlizerman
Multi-group Learning for Hierarchical Groups
Samuel Deng, Daniel Hsu
Fast Decision Boundary based Out-of-Distribution Detector
Litian Liu, Yao Qin
Ensemble Pruning for Out-of-distribution Generalization
Fengchun Qiao, Xi Peng
Diffuse, Sample, Project: Plug-And-Play Controllable Graph Generation
Kartik Sharma, Srijan Kumar, Rakshit Trivedi
On the Asymptotic Distribution of the Minimum Empirical Risk
Jacob Westerhout, TrungTin Nguyen, Xin Guo et al.
RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback
Yufei Wang, Zhanyi Sun, Jesse Zhang et al.
Exploiting Code Symmetries for Learning Program Semantics
Kexin Pei, Weichen Li, Qirui Jin et al.
Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach
Zixiao Wang, AmirEmad Ghassami, Ilya Shpitser
Encodings for Prediction-based Neural Architecture Search
Yash Akhauri, Mohamed Abdelfattah
Chain of Code: Reasoning with a Language Model-Augmented Code Emulator
Chengshu Li, Jacky Liang, Andy Zeng et al.
Towards Compositionality in Concept Learning
Adam Stein, Aaditya Naik, Yinjun Wu et al.
A Minimaximalist Approach to Reinforcement Learning from Human Feedback
Gokul Swamy, Christoph Dann, Rahul Kidambi et al.
Language Agent Tree Search Unifies Reasoning, Acting, and Planning in Language Models
Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman et al.
Image Hijacks: Adversarial Images can Control Generative Models at Runtime
Luke Bailey, Euan Ong, Stuart Russell et al.
TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision
Zhuo Chen, Jacob McCarran, Esteban Vizcaino et al.
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics
Noga Mudrik, Yenho Chen, Eva Yezerets et al.
The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective
Chi-Heng Lin, Chiraag Kaushik, Eva Dyer et al.
Conditional Common Entropy for Instrumental Variable Testing and Partial Identification
Ziwei Jiang, Murat Kocaoglu
On the Maximal Local Disparity of Fairness-Aware Classifiers
Jinqiu Jin, Haoxuan Li, Fuli Feng
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
Chiraag Kaushik, Ran Liu, Chi-Heng Lin et al.
Learning Associative Memories with Gradient Descent
Vivien Cabannnes, Berfin Simsek, Alberto Bietti
QuRating: Selecting High-Quality Data for Training Language Models
Alexander Wettig, Aatmik Gupta, Saumya Malik et al.
On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization
Motahareh Sohrabi, Juan Ramirez, Tianyue Zhang et al.
Benchmarking Deletion Metrics with the Principled Explanations
Yipei Wang, Xiaoqian Wang
Online Matrix Completion: A Collaborative Approach with Hott Items
Dheeraj Baby, Soumyabrata Pal
Scaling Laws for Fine-Grained Mixture of Experts
Jan Ludziejewski, Jakub Krajewski, Kamil Adamczewski et al.
Benign Overfitting in Adversarial Training of Neural Networks
Yunjuan Wang, Kaibo Zhang, Raman Arora
Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee
George Chen
Sample as you Infer: Predictive Coding with Langevin Dynamics
Umais Zahid, Qinghai Guo, Zafeirios Fountas
NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction
Haofan Lu, Christopher Vattheuer, Baharan Mirzasoleiman et al.
Efficient PAC Learnability of Dynamical Systems Over Multilayer Networks
Zirou Qiu, Abhijin Adiga, Madhav Marathe et al.
How Language Model Hallucinations Can Snowball
Muru Zhang, Ofir Press, William Merrill et al.
APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference
Bowen Zhao, Hannaneh Hajishirzi, Qingqing Cao
Extracting Training Data From Document-Based VQA Models
Francesco Pinto, Nathalie Rauschmayr, Florian Tramer et al.
MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data
Paul Scotti, Mihir Tripathy, Cesar Kadir Torrico Villanueva et al.
Prediction Accuracy of Learning in Games : Follow-the-Regularized-Leader meets Heisenberg
Yi Feng, Georgios Piliouras, Xiao Wang
PARCv2: Physics-aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics Modeling
Phong Nguyen, Xinlun Cheng, Shahab Azarfar et al.
Unsupervised Concept Discovery Mitigates Spurious Correlations
Md Rifat Arefin, Yan Zhang, Aristide Baratin et al.
Scalable AI Safety via Doubly-Efficient Debate
Jonah Brown-Cohen, Geoffrey Irving, Georgios Piliouras
GeoMFormer: A General Architecture for Geometric Molecular Representation Learning
Tianlang Chen, Shengjie Luo, Di He et al.
Model Assessment and Selection under Temporal Distribution Shift
Elise Han, Chengpiao Huang, Kaizheng Wang
The Fundamental Limits of Least-Privilege Learning
Theresa Stadler, Bogdan Kulynych, Michael Gastpar et al.
Sequential Disentanglement by Extracting Static Information From A Single Sequence Element
Nimrod Berman, Ilan Naiman, Idan Arbiv et al.
Minimizing $f$-Divergences by Interpolating Velocity Fields
Song Liu, Jiahao Yu, Jack Simons et al.
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models
Louis Sharrock, Jack Simons, Song Liu et al.
Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based Losses
Panagiotis Koromilas, Giorgos Bouritsas, Theodoros Giannakopoulos et al.
Subgoal-based Demonstration Learning for Formal Theorem Proving
Xueliang Zhao, Wenda Li, Lingpeng Kong
Vector Quantization Pretraining for EEG Time Series with Random Projection and Phase Alignment
Haokun Gui, Xiucheng Li, Xinyang Chen
Emergence of In-Context Reinforcement Learning from Noise Distillation
Ilya Zisman, Vladislav Kurenkov, Alexander Nikulin et al.
Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs
Yeonhong Park, Jake Hyun, SangLyul Cho et al.
Robust Universal Adversarial Perturbations
Changming Xu, Gagandeep Singh
Low-Cost High-Power Membership Inference Attacks
Sajjad Zarifzadeh, Philippe Liu, Reza Shokri
Towards Modular LLMs by Building and Reusing a Library of LoRAs
Oleksiy Ostapenko, Zhan Su, Edoardo Ponti et al.
Early Time Classification with Accumulated Accuracy Gap Control
Liran Ringel, Regev Cohen, Daniel Freedman et al.
Evaluating Instrument Validity using the Principle of Independent Mechanisms
Patrick F. Burauel
Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding
Guangyi Liu, Yu Wang, Zeyu Feng et al.
CogBench: a large language model walks into a psychology lab
Julian Coda-Forno, Marcel Binz, Jane Wang et al.
Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks
Akshay Kumar Jagadish, Julian Coda-Forno, Mirko Thalmann et al.
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy
Kirill Vishniakov, Zhiqiang Shen, Zhuang Liu
Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data
Giannis Daras, Alexandros Dimakis, Constantinos Daskalakis
Optimally Improving Cooperative Learning in a Social Setting
Shahrzad Haddadan, Cheng Xin, Jie Gao
$\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts
Guanjie Chen, Xinyu Zhao, Tianlong Chen et al.
Simple linear attention language models balance the recall-throughput tradeoff
Simran Arora, Sabri Eyuboglu, Michael Zhang et al.
Principled Preferential Bayesian Optimization
Wenjie Xu, Wenbin Wang, Yuning Jiang et al.
SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning
Matthias Weissenbacher, Rishabh Agarwal, Yoshinobu Kawahara
On Positivity Condition for Causal Inference
Inwoo Hwang, Yesong Choe, Yeahoon Kwon et al.
Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference
Piotr Nawrot, Adrian Łańcucki, Marcin Chochowski et al.
Linguistic Calibration of Long-Form Generations
Neil Band, Xuechen Li, Tengyu Ma et al.
A Unified Framework for Learning with Nonlinear Model Classes from Arbitrary Linear Samples
Ben Adcock, Juan Cardenas, Nick Dexter
Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices
Nathaniel Cohen, Vladimir Kulikov, Matan Kleiner et al.
The Expressive Power of Path-Based Graph Neural Networks
Caterina Graziani, Tamara Drucks, Fabian Jogl et al.
Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers
Katherine Crowson, Stefan Baumann, Alex Birch et al.
Environment Design for Inverse Reinforcement Learning
Thomas Kleine Buening, Victor Villin, Christos Dimitrakakis
Stable Differentiable Causal Discovery
Achille Nazaret, Justin Hong, Elham Azizi et al.
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error
Haoran Li, Zicheng Zhang, Wang Luo et al.
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective
Wu Lin, Felix Dangel, Runa Eschenhagen et al.
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning
Saber Malekmohammadi, Yaoliang Yu, YANG CAO
A Tale of Tails: Model Collapse as a Change of Scaling Laws
Elvis Dohmatob, Yunzhen Feng, Pu Yang et al.
Adversarial Attacks on Combinatorial Multi-Armed Bandits
Rishab Balasubramanian, Jiawei Li, Tadepalli Prasad et al.
Practical Hamiltonian Monte Carlo on Riemannian Manifolds via Relativity Theory
Kai Xu, Hong Ge
From Coarse to Fine: Enable Comprehensive Graph Self-supervised Learning with Multi-granular Semantic Ensemble
Qianlong Wen, Mingxuan Ju, Zhongyu Ouyang et al.
A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity
Andrew Lee, Xiaoyan Bai, Itamar Pres et al.
Pre-Training Protein Bi-level Representation Through Span Mask Strategy On 3D Protein Chains
Jiale Zhao, Wanru Zhuang, Jia Song et al.
Planning, Fast and Slow: Online Reinforcement Learning with Action-Free Offline Data via Multiscale Planners
Chengjie Wu, Hao Hu, yiqin yang et al.
Graph Attention Retrospective
Kimon Fountoulakis, Amit Levi, Shenghao Yang et al.
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
Haoxin Liu, Harshavardhan Kamarthi, Lingkai Kong et al.
Parameterized Physics-informed Neural Networks for Parameterized PDEs
Woojin Cho, Minju Jo, Haksoo Lim et al.
Sparser, Better, Deeper, Stronger: Improving Static Sparse Training with Exact Orthogonal Initialization
Aleksandra I. Nowak, Łukasz Gniecki, Filip Szatkowski et al.
Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants
Isabel Chien, Wessel Bruinsma, Javier Gonzalez et al.
TIC-TAC: A Framework For Improved Covariance Estimation In Deep Heteroscedastic Regression
Megh Shukla, Mathieu Salzmann, Alexandre Alahi
Challenges in Training PINNs: A Loss Landscape Perspective
Pratik Rathore, Weimu Lei, Zachary Frangella et al.
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova et al.
Position: The Causal Revolution Needs Scientific Pragmatism
Joshua Loftus
A Unified Recipe for Deriving (Time-Uniform) PAC-Bayes Bounds
Ben Chugg, Hongjian Wang, Aaditya Ramdas
RankSEG: A Consistent Ranking-based Framework for Segmentation
Ben Dai, Chunlin Li
PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency
Yeonsung Jung, Heecheol Yun, Joonhyung Park et al.
Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models
Ludwig Winkler, Lorenz Richter, Manfred Opper
Agnostic Interactive Imitation Learning: New Theory and Practical Algorithms
Yichen Li, Chicheng Zhang
Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation
Michelle Pan, Mariah Schrum, Vivek Myers et al.
Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)
Drew Prinster, Samuel Stanton, Anqi Liu et al.
Learning to Continually Learn with the Bayesian Principle
Soochan Lee, Hyeonseong Jeon, Jaehyeon Son et al.
Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data
Xuran Meng, Difan Zou, Yuan Cao
Repoformer: Selective Retrieval for Repository-Level Code Completion
Di Wu, Wasi Ahmad, Dejiao Zhang et al.
Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach
Anton Plaksin, Vitaly Kalev
On the Generalization of Stochastic Gradient Descent with Momentum
Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher et al.
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent
Yingru Li, Jiawei Xu, Lei Han et al.
A Geometric Explanation of the Likelihood OOD Detection Paradox
Hamidreza Kamkari, Brendan Ross, Jesse Cresswell et al.
Leveraging Attractor Dynamics in Spatial Navigation for Better Language Parsing
Xiaolong Zou, Xingxing Cao, Xiaojiao Yang et al.
Disparate Impact on Group Accuracy of Linearization for Private Inference
Saswat Das, Marco Romanelli, Ferdinando Fioretto
SqueezeLLM: Dense-and-Sparse Quantization
Sehoon Kim, Coleman Hooper, Amir Gholaminejad et al.
Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization
Mudit Gaur, Amrit Singh Bedi, Di Wang et al.
An LLM Compiler for Parallel Function Calling
Sehoon Kim, Suhong Moon, Ryan Tabrizi et al.
Unbiased Multi-Label Learning from Crowdsourced Annotations
Mingxuan Xia, Zenan Huang, Runze Wu et al.
SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals
Rahul Thapa, Bryan He, Magnus Ruud Kjaer et al.
Position: Evolving AI Collectives Enhance Human Diversity and Enable Self-Regulation
Shiyang Lai, Yujin Potter, Junsol Kim et al.
Revisiting Context Aggregation for Image Matting
Qinglin Liu, Xiaoqian Lv, Quanling Meng et al.
Learning Scale-Aware Spatio-temporal Implicit Representation for Event-based Motion Deblurring
Wei Yu, Jianing Li, Shengping Zhang et al.
Mind the Boundary: Coreset Selection via Reconstructing the Decision Boundary
Shuo Yang, Zhe Cao, Sheng Guo et al.
Position: Standardization of Behavioral Use Clauses is Necessary for the Adoption of Responsible Licensing of AI
Daniel McDuff, Tim Korjakow, Scott Cambo et al.
Infinite-Horizon Distributionally Robust Regret-Optimal Control
Taylan Kargin, Joudi Hajar, Vikrant Malik et al.
Accelerating Heterogeneous Federated Learning with Closed-form Classifiers
Eros Fanì, Raffaello Camoriano, Barbara Caputo et al.
Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach
Weijia Zhang, Chenlong Yin, Hao Liu et al.
Simplicity Bias via Global Convergence of Sharpness Minimization
Khashayar Gatmiry, Zhiyuan Li, Sashank J. Reddi et al.
Why Do You Grok? A Theoretical Analysis on Grokking Modular Addition
Mohamad Amin Mohamadi, Zhiyuan Li, Lei Wu et al.
An Intrinsic Vector Heat Network
Alexander Gao, Maurice Chu, Mubbasir Kapadia et al.
Mean Field Langevin Actor-Critic: Faster Convergence and Global Optimality beyond Lazy Learning
Kakei Yamamoto, Kazusato Oko, Zhuoran Yang et al.
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model
Mikail Khona, Maya Okawa, Jan Hula et al.
Neural-Kernel Conditional Mean Embeddings
Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic
Stochastic Quantum Sampling for Non-Logconcave Distributions and Estimating Partition Functions
Guneykan Ozgul, Xiantao Li, Mehrdad Mahdavi et al.
Enabling Uncertainty Estimation in Iterative Neural Networks
Nikita Durasov, Doruk Oner, Jonathan Donier et al.
MultiMax: Sparse and Multi-Modal Attention Learning
Yuxuan Zhou, Mario Fritz, Margret Keuper
Improving Neural Additive Models with Bayesian Principles
Kouroche Bouchiat, Alexander Immer, Hugo Yèche et al.
Averaging $n$-step Returns Reduces Variance in Reinforcement Learning
Brett Daley, Martha White, Marlos C. Machado
Implicit meta-learning may lead language models to trust more reliable sources
Dmitrii Krasheninnikov, Egor Krasheninnikov, Bruno Mlodozeniec et al.
Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing
Hongbin Pei, Yu Li, Huiqi Deng et al.
Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning
Michal Nauman, Michał Bortkiewicz, Piotr Milos et al.
Residual-Conditioned Optimal Transport: Towards Structure-Preserving Unpaired and Paired Image Restoration
Xiaole Tang, Hu Xin, Xiang Gu et al.
Prometheus: Out-of-distribution Fluid Dynamics Modeling with Disentangled Graph ODE
Hao Wu, Huiyuan Wang, kun wang et al.
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations
Yanda Chen, Ruiqi Zhong, Narutatsu Ri et al.
Total Variation Distance Meets Probabilistic Inference
Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel et al.
Improving Adversarial Energy-Based Model via Diffusion Process
Cong Geng, Tian Han, Peng-Tao Jiang et al.
A3S: A General Active Clustering Method with Pairwise Constraints
Xun Deng, Junlong Liu, Han Zhong et al.
An Online Optimization Perspective on First-Order and Zero-Order Decentralized Nonsmooth Nonconvex Stochastic Optimization
Emre Sahinoglu, Shahin Shahrampour
Learning a Diffusion Model Policy from Rewards via Q-Score Matching
Michael Psenka, Alejandro Escontrela, Pieter Abbeel et al.
Online Cascade Learning for Efficient Inference over Streams
Lunyiu Nie, Zhimin Ding, Erdong Hu et al.
Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning
Donghu Kim, Hojoon Lee, Kyungmin Lee et al.
Learning from Streaming Data when Users Choose
Jinyan Su, Sarah Dean
Scaling Speech Technology to 1,000+ Languages
Vineel Pratap Konduru, Andros Tjandra, Bowen Shi et al.
Robustness of Nonlinear Representation Learning
Simon Buchholz, Bernhard Schölkopf
Conformal Prediction with Learned Features
Shayan Kiyani, George J. Pappas, Hamed Hassani
Symmetry Induces Structure and Constraint of Learning
Liu Ziyin
Achieving Margin Maximization Exponentially Fast via Progressive Norm Rescaling
Mingze Wang, Zeping Min, Lei Wu
T-Cal: An Optimal Test for the Calibration of Predictive Models
Donghwan Lee, Xinmeng Huang, Hamed Hassani et al.
Measures of diversity and space-filling designs for categorical data
AstraZeneca Pharmaceutica, Emilio Domínguez-Sánchez, Merwan Barlier et al.