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← Optimization & Theory
Machine Learning
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Optimization & Theory
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Learning Theory
5,312 papers
Papers per year
2001: 1
2002: 16
2003: 16
2004: 15
2005: 17
2006: 30
2007: 32
2008: 32
2009: 34
2010: 66
2011: 76
2012: 74
2013: 94
2014: 115
2015: 123
2016: 128
2017: 185
2018: 219
2019: 390
2020: 466
2021: 640
2022: 664
2023: 799
2024: 688
2025: 307
2026: 85
Papers
Sample complexity and effective dimension for regression on manifolds
NIPS 2020
Sample Complexity of Uniform Convergence for Multicalibration
NIPS 2020
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks
NIPS 2020
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions
NIPS 2020
Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals
NIPS 2020
Learning Bounds for Risk-sensitive Learning
NIPS 2020
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations
NIPS 2020
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent
NIPS 2020
Mutual exclusivity as a challenge for deep neural networks
NIPS 2020
Prediction with Corrupted Expert Advice
NIPS 2020
Reducing Adversarially Robust Learning to Non-Robust PAC Learning
NIPS 2020
When Do Neural Networks Outperform Kernel Methods?
NIPS 2020
Online learning with dynamics: A minimax perspective
NIPS 2020
Sharp uniform convergence bounds through empirical centralization
NIPS 2020
Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition
NIPS 2020
Information Theoretic Regret Bounds for Online Nonlinear Control
NIPS 2020
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs
NIPS 2020
Kernel Alignment Risk Estimator: Risk Prediction from Training Data
NIPS 2020
Collapsing Bandits and Their Application to Public Health Intervention
NIPS 2020
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses
NIPS 2020
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples
NIPS 2020
On Learning Ising Models under Huber's Contamination Model
NIPS 2020
Conditioning and Processing: Techniques to Improve Information-Theoretic Generalization Bounds
NIPS 2020
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition
NIPS 2020
Finite-Time Analysis for Double Q-learning
NIPS 2020
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