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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
CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration
ICML 2019
Sample-Optimal Parametric Q-Learning Using Linearly Additive Features
ICML 2019
Rademacher Complexity for Adversarially Robust Generalization
ICML 2019
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
ICML 2019
Adaptive Regret of Convex and Smooth Functions
ICML 2019
On Learning Invariant Representations for Domain Adaptation
ICML 2019
Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
ICML 2019
Explore Truthful Incentives for Tasks with Heterogenous Levels of Difficulty in the Sharing Economy
IJCAI 2019
Predict+Optimise with Ranking Objectives: Exhaustively Learning Linear Functions
IJCAI 2019
GANAK: A Scalable Probabilistic Exact Model Counter
IJCAI 2019
STCA: Spatio-Temporal Credit Assignment with Delayed Feedback in Deep Spiking Neural Networks
IJCAI 2019
Guarantees for Sound Abstractions for Generalized Planning
IJCAI 2019
Theoretical Investigation of Generalization Bound for Residual Networks
IJCAI 2019
A Strongly Asymptotically Optimal Agent in General Environments
IJCAI 2019
AdaLinUCB: Opportunistic Learning for Contextual Bandits
IJCAI 2019
Deep Metric Learning: The Generalization Analysis and an Adaptive Algorithm
IJCAI 2019
Conditions on Features for Temporal Difference-Like Methods to Converge
IJCAI 2019
Submodular Batch Selection for Training Deep Neural Networks
IJCAI 2019
Perturbed-History Exploration in Stochastic Multi-Armed Bandits
IJCAI 2019
Multi-Class Learning using Unlabeled Samples: Theory and Algorithm
IJCAI 2019
A Practical Semi-Parametric Contextual Bandit
IJCAI 2019
Unifying the Stochastic and the Adversarial Bandits with Knapsack
IJCAI 2019
Structure Learning for Safe Policy Improvement
IJCAI 2019
Positive and Unlabeled Learning with Label Disambiguation
IJCAI 2019
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Hamming Distance
IJCAI 2019
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