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← Optimization & Theory
Machine Learning
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Optimization & Theory
›
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
Stochastic bandits with groups of similar arms.
NIPS 2021
Multiclass Boosting and the Cost of Weak Learning
NIPS 2021
Understanding the Generalization Benefit of Model Invariance from a Data Perspective
NIPS 2021
A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms
NIPS 2021
Algorithmic stability and generalization of an unsupervised feature selection algorithm
NIPS 2021
Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics
ICML 2021
Linear Bandits on Uniformly Convex Sets
JMLR 2021
Rissanen Data Analysis: Examining Dataset Characteristics via Description Length
ICML 2021
Near-Optimal No-Regret Learning in General Games
NIPS 2021
On Belief Change for Multi-Label Classifier Encodings
IJCAI 2021
Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination
NIPS 2021
Learning Dynamics Models with Stable Invariant Sets
AAAI 2021
How rotational invariance of common kernels prevents generalization in high dimensions
ICML 2021
On the Role of Optimization in Double Descent: A Least Squares Study
NIPS 2021
On the Cryptographic Hardness of Learning Single Periodic Neurons
NIPS 2021
When Is Generalizable Reinforcement Learning Tractable?
NIPS 2021
The Complexity of Bayesian Network Learning: Revisiting the Superstructure
NIPS 2021
Training deep residual networks for uniform approximation guarantees
L4DC 2021
Size and Depth Separation in Approximating Benign Functions with Neural Networks
COLT 2021
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity
ICML 2021
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret
NIPS 2021
Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime
JMLR 2021
Automated Symbolic Law Discovery: A Computer Vision Approach
AAAI 2021
The curious case of adversarially robust models: More data can help, double descend, or hurt generalization
UAI 2021
Efficiently Learning One Hidden Layer ReLU Networks From Queries
NIPS 2021
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