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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
The Pareto Frontier of model selection for general Contextual Bandits
NIPS 2021
Learning curves of generic features maps for realistic datasets with a teacher-student model
NIPS 2021
Supervising the Transfer of Reasoning Patterns in VQA
NIPS 2021
Training Neural Networks is ER-complete
NIPS 2021
Decentralized Learning in Online Queuing Systems
NIPS 2021
On the Sample Complexity of Learning under Geometric Stability
NIPS 2021
Taxonomizing local versus global structure in neural network loss landscapes
NIPS 2021
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms
NIPS 2021
Bandit Phase Retrieval
NIPS 2021
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations
NIPS 2021
Support Recovery of Sparse Signals from a Mixture of Linear Measurements
NIPS 2021
Tighter Expected Generalization Error Bounds via Wasserstein Distance
NIPS 2021
Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics
NIPS 2021
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning
NIPS 2021
Stochastic bandits with groups of similar arms.
NIPS 2021
Multi-Armed Bandits with Bounded Arm-Memory: Near-Optimal Guarantees for Best-Arm Identification and Regret Minimization
NIPS 2021
Grounding inductive biases in natural images: invariance stems from variations in data
NIPS 2021
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations
NIPS 2021
Provably efficient multi-task reinforcement learning with model transfer
NIPS 2021
Algorithmic stability and generalization of an unsupervised feature selection algorithm
NIPS 2021
On learning sparse vectors from mixture of responses
NIPS 2021
Hessian Eigenspectra of More Realistic Nonlinear Models
NIPS 2021
On the Provable Generalization of Recurrent Neural Networks
NIPS 2021
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning
NIPS 2021
The best of both worlds: stochastic and adversarial episodic MDPs with unknown transition
NIPS 2021
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