learning theory

The Limits of Differential Privacy in Online Learning NeurIPS 2024 The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis EMNLP 2024 Theoretical Analysis of Weak-to-Strong Generalization NeurIPS 2024 The Reliability of OKRidge Method in Solving Sparse Ridge Regression Problems NeurIPS 2024 The Risks of Recourse in Binary Classification AISTATS 2024 The sample complexity of ERMs in stochastic convex optimization AISTATS 2024 The Sample Complexity of Gradient Descent in Stochastic Convex Optimization NeurIPS 2024 Towards a theory of how the structure of language is acquired by deep neural networks NeurIPS 2024 Towards Exact Computation of Inductive Bias IJCAI 2024 Towards the Dynamics of a DNN Learning Symbolic Interactions NeurIPS 2024 Trade-Offs of Diagonal Fisher Information Matrix Estimators NeurIPS 2024 Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective CVPR 2024 Understanding Emergent Abilities of Language Models from the Loss Perspective NeurIPS 2024 Universal Rates for Active Learning NeurIPS 2024 Variance estimation in compound decision theory under boundedness NeurIPS 2024 When Is Inductive Inference Possible? NeurIPS 2024 L_2-Uniform Stability of Randomized Learning Algorithms: Sharper Generalization Bounds and Confidence Boosting NeurIPS 2023 A Case of Exponential Convergence Rates for SVM AISTATS 2023 Active Ranking of Experts Based on their Performances in Many Tasks ICML 2023 Adaptive Data Analysis in a Balanced Adversarial Model NeurIPS 2023 Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness NeurIPS 2023 Agnostically Learning Single-Index Models using Omnipredictors NeurIPS 2023 Algorithmic Collective Action in Machine Learning ICML 2023 A Parameterized Theory of PAC Learning AAAI 2023 A Path to Simpler Models Starts With Noise NeurIPS 2023