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Learning Theory

5312 directly classified papers

Papers per year

Papers

Meta-Learning Neural Mechanisms rather than Bayesian Priors ACL 2025
Improving Causal Interventions in Amnesic Probing with Mean Projection or LEACE ACL 2025
Rethinking Reward Model Evaluation Through the Lens of Reward Overoptimization ACL 2025
Lexical Recall or Logical Reasoning: Probing the Limits of Reasoning Abilities in Large Language Models ACL 2025
MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark ACL 2025
Large Language and Reasoning Models are Shallow Disjunctive Reasoners ACL 2025
Circuit Stability Characterizes Language Model Generalization ACL 2025
Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning ACL 2025
P2 Law: Scaling Law for Post-Training After Model Pruning ACL 2025
Supervised and Unsupervised Probing of Shortcut Learning: Case Study on the Emergence and Evolution of Syntactic Heuristics in BERT ACL 2025
VideoVista-CulturalLingo: 360° Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension ACL 2025
PlanningArena: A Modular Benchmark for Multidimensional Evaluation of Planning and Tool Learning ACL 2025
Inverse Reinforcement Learning Meets Large Language Model Alignment ACL 2025
Revisiting Scaling Laws for Language Models: The Role of Data Quality and Training Strategies ACL 2025
Syntactic Blind Spots: How Misalignment Leads to LLMs’ Mathematical Errors EMNLP 2025
LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions CVPR 2025
Learning with Linear Function Approximations in Mean-Field Control JMLR 2025
On the Convergence of Projected Policy Gradient for Any Constant Step Sizes JMLR 2025
An Asymptotically Optimal Coordinate Descent Algorithm for Learning Bayesian Networks from Gaussian Models JMLR 2025
Algorithms for ridge estimation with convergence guarantees JMLR 2025
Circuit Compositions: Exploring Modular Structures in Transformer-Based Language Models ACL 2025
Uncovering Scaling Laws for Large Language Models via Inverse Problems EMNLP 2025
Scaling Laws Are Unreliable for Downstream Tasks: A Reality Check EMNLP 2025
A Theory of Learning Unified Model via Knowledge Integration from Label Space Varying Domains CVPR 2025
Assessing the Limits of In-Context Learning beyond Functions using Partially Ordered Relation AACL 2025