← Optimization & Theory
Machine Learning › Optimization & Theory ›

Learning Theory

5312 directly classified papers

Papers per year

Papers

Achieving $\tilde{O}(1/\epsilon)$ Sample Complexity for Constrained Markov Decision Process NIPS 2024
An Analytical Study of Utility Functions in Multi-Objective Reinforcement Learning NIPS 2024
RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning EMNLP 2024
Evaluating Large Language Models along Dimensions of Language Variation: A Systematik Invesdigatiom uv Cross-lingual Generalization EMNLP 2024
Adaptation Odyssey in LLMs: Why Does Additional Pretraining Sometimes Fail to Improve? EMNLP 2024
Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation NIPS 2024
Probably approximately correct stability of allocations in uncertain coalitional games with private sampling L4DC 2024
Can Transformers Learn n-gram Language Models? EMNLP 2024
ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs EMNLP 2024
SPOR: A Comprehensive and Practical Evaluation Method for Compositional Generalization in Data-to-Text Generation ACL 2024
NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes ACL 2024
DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy ACL 2024
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs NIPS 2024
Robustly Improving Bandit Algorithms with Confounded and Selection Biased Offline Data: A Causal Approach AAAI 2024
Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation NIPS 2024
Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion? NAACL 2024
Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs NIPS 2024
How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach NIPS 2024
$\widetilde{O}(T^{-1})$ Convergence to (coarse) correlated equilibria in full-information general-sum Markov games L4DC 2024
Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension COLT 2024
Inherent limitations of dimensions for characterizing learnability of distribution classes COLT 2024
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training JMLR 2024
Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs NIPS 2024
MAP- and MLE-Based Teaching JMLR 2024
Achieving Tractable Minimax Optimal Regret in Average Reward MDPs NIPS 2024