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Learning Theory

5312 directly classified papers

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Papers

On the Importance of Feature Separability in Predicting Out-Of-Distribution Error NIPS 2023
A Trichotomy for Transductive Online Learning NIPS 2023
Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation ICML 2023
Optimal Arms Identification with Knapsacks ICML 2023
Bandits with Knapsacks: Advice on Time-Varying Demands ICML 2023
Dynamical Linear Bandits ICML 2023
Multiplier Bootstrap-based Exploration ICML 2023
Agnostically Learning Single-Index Models using Omnipredictors NIPS 2023
Nearest Neighbour with Bandit Feedback NIPS 2023
Robust Lipschitz Bandits to Adversarial Corruptions NIPS 2023
Uniform Convergence with Square-Root Lipschitz Loss NIPS 2023
On Proper Learnability between Average- and Worst-case Robustness NIPS 2023
Near-optimal learning with average Hölder smoothness NIPS 2023
Contextual Stochastic Block Model: Sharp Thresholds and Contiguity JMLR 2023
Learning Curves for Noisy Heterogeneous Feature-Subsampled Ridge Ensembles NIPS 2023
A Bayesian Approach To Analysing Training Data Attribution In Deep Learning NIPS 2023
Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization NIPS 2023
The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit NIPS 2023
Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection NIPS 2023
Time-Independent Information-Theoretic Generalization Bounds for SGLD NIPS 2023
An Adaptive Algorithm for Learning with Unknown Distribution Drift NIPS 2023
Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise NIPS 2023
Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization JMLR 2023
Nonparametric Teaching for Multiple Learners NIPS 2023
Binary Classification with Confidence Difference NIPS 2023