ICLR Papers
6,124 papers found • Page 107 of 123
On Harmonizing Implicit Subpopulations
Feng Hong, Jiangchao Yao, YUEMING LYU et al.
Online Continual Learning for Interactive Instruction Following Agents
Byeonghwi Kim, Minhyuk Seo, Jonghyun Choi
Online GNN Evaluation Under Test-time Graph Distribution Shifts
Xin Zheng, Dongjin Song, Qingsong Wen et al.
Online Information Acquisition: Hiring Multiple Agents
Federico Cacciamani, Matteo Castiglioni, Nicola Gatti
Online Stabilization of Spiking Neural Networks
Yaoyu Zhu, Jianhao Ding, Tiejun Huang et al.
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling
Aadirupa Saha, Branislav Kveton
On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation
Jeongyeol Kwon, Dohyun Kwon, Stephen Wright et al.
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes
Rishabh Agarwal, Nino Vieillard, Yongchao Zhou et al.
On Representation Complexity of Model-based and Model-free Reinforcement Learning
Hanlin Zhu, Baihe Huang, Stuart Russell
On Stationary Point Convergence of PPO-Clip
Ruinan Jin, Shuai Li, Baoxiang Wang
On the Analysis of GAN-based Image-to-Image Translation with Gaussian Noise Injection
Chaohua Shi, Kexin Huang, Lu Gan et al.
On the Effect of Batch Size in Byzantine-Robust Distributed Learning
Yi-Rui Yang, Chang-Wei Shi, Wu-Jun Li
On the Expressivity of Objective-Specification Formalisms in Reinforcement Learning
Rohan Subramani, Marcus Williams, Max Heitmann et al.
On the Fairness ROAD: Robust Optimization for Adversarial Debiasing
Vincent Grari, Thibault Laugel, Tatsunori Hashimoto et al.
On the Foundations of Shortcut Learning
Katherine Hermann, Hossein Mobahi, Thomas FEL et al.
On the Generalization and Approximation Capacities of Neural Controlled Differential Equations
Linus Bleistein, Agathe Guilloux
On the generalization capacity of neural networks during generic multimodal reasoning
Takuya Ito, Soham Dan, Mattia Rigotti et al.
On the Hardness of Constrained Cooperative Multi-Agent Reinforcement Learning
Ziyi Chen, Yi Zhou, Heng Huang
On the hardness of learning under symmetries
Bobak Kiani, Thien Le, Hannah Lawrence et al.
On the Hardness of Online Nonconvex Optimization with Single Oracle Feedback
Ziwei Guan, Yi Zhou, Yingbin Liang
On the Humanity of Conversational AI: Evaluating the Psychological Portrayal of LLMs
Jen-tse Huang, Wenxuan Wang, Eric John Li et al.
On the Joint Interaction of Models, Data, and Features
Yiding Jiang, Christina Baek, J Kolter
On the Learnability of Watermarks for Language Models
Chenchen Gu, XIANG LI, Percy Liang et al.
On the Limitations of Temperature Scaling for Distributions with Overlaps
Muthu Chidambaram, Rong Ge
On the Markov Property of Neural Algorithmic Reasoning: Analyses and Methods
Montgomery Bohde, Meng Liu, Alexandra Saxton et al.
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting
Runqi Lin, Chaojian Yu, Bo Han et al.
On the Parameterization of Second-Order Optimization Effective towards the Infinite Width
Satoki Ishikawa, Ryo Karakida
On the Posterior Distribution in Denoising: Application to Uncertainty Quantification
Hila Manor, Tomer Michaeli
On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters
Matthias Lanzinger, Pablo Barcelo
On the Provable Advantage of Unsupervised Pretraining
Jiawei Ge, Shange Tang, Jianqing Fan et al.
On the Reliability of Watermarks for Large Language Models
John Kirchenbauer, Jonas Geiping, Yuxin Wen et al.
On the Role of Discrete Tokenization in Visual Representation Learning
Tianqi Du, Yifei Wang, Yisen Wang
On the Role of General Function Approximation in Offline Reinforcement Learning
Chenjie Mao, Qiaosheng Zhang, Zhen Wang et al.
On the Sample Complexity of Lipschitz Constant Estimation
Stephen Roberts, Julien Huang, Jan-Peter Calliess
On the Scalability and Memory Efficiency of Semidefinite Programs for Lipschitz Constant Estimation of Neural Networks
Zi Wang, Bin Hu, Aaron Havens et al.
On the Stability of Expressive Positional Encodings for Graphs
Yinan Huang, William Lu, Joshua Robinson et al.
On the Stability of Iterative Retraining of Generative Models on their own Data
Quentin Bertrand, Joey Bose, Alexandre Duplessis et al.
On the Variance of Neural Network Training with respect to Test Sets and Distributions
Keller Jordan
On the Vulnerability of Adversarially Trained Models Against Two-faced Attacks
Shengjie Zhou, Lue Tao, Yuzhou Cao et al.
On Trajectory Augmentations for Off-Policy Evaluation
Ge Gao, Qitong Gao, Xi Yang et al.
OpenChat: Advancing Open-source Language Models with Mixed-Quality Data
Guan Wang, Sijie Cheng, Xianyuan Zhan et al.
Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy
Simon Ging, Maria A. Bravo, Thomas Brox
OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views
Francis Engelmann, Fabian Manhardt, Michael Niemeyer et al.
OpenTab: Advancing Large Language Models as Open-domain Table Reasoners
Kezhi Kong, Jiani Zhang, Zhengyuan Shen et al.
Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning
Ge Li, Hongyi Zhou, Dominik Roth et al.
OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text
Keiran Paster, Marco Dos Santos, Zhangir Azerbayev et al.
Optimal criterion for feature learning of two-layer linear neural network in high dimensional interpolation regime
Keita Suzuki, Taiji Suzuki
OPTIMAL ROBUST MEMORIZATION WITH RELU NEURAL NETWORKS
Lijia Yu, XIAOSHAN GAO, Lijun Zhang
Optimal Sample Complexity for Average Reward Markov Decision Processes
Shengbo Wang, Jose Blanchet, Peter Glynn
Optimal Sample Complexity of Contrastive Learning
Noga Alon, Dmitrii Avdiukhin, Dor Elboim et al.