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Optimization & Theory
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Sample Complexity
97 directly classified papers
Papers per year
2006: 1
2008: 2
2010: 2
2012: 1
2013: 1
2014: 1
2015: 2
2016: 4
2017: 3
2018: 6
2019: 9
2020: 14
2021: 13
2022: 9
2023: 11
2024: 14
2025: 4
Papers
Learnability of Parameter-Bounded Bayes Nets
AAAI 2025
Sample Complexity of Linear Regression Models for Opinion Formation in Networks
AAAI 2025
Learning Accurate and Interpretable Decision Trees (Extended Abstract)
IJCAI 2025
On the Asymptotic Optimality of Confidence Interval Based Algorithms for Fixed Confidence MABs
AAAI 2025
Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms
NIPS 2024
Near-Optimal Distributionally Robust Reinforcement Learning with General $L_p$ Norms
NIPS 2024
Improved Sample Complexity for Multiclass PAC Learning
NIPS 2024
Fast Rates for Bandit PAC Multiclass Classification
NIPS 2024
Statistical Efficiency of Distributional Temporal Difference Learning
NIPS 2024
Replicability in Learning: Geometric Partitions and KKM-Sperner Lemma
NIPS 2024
Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs
NIPS 2024
Preference-based Pure Exploration
NIPS 2024
The Power of Resets in Online Reinforcement Learning
NIPS 2024
Sample Complexity of Interventional Causal Representation Learning
NIPS 2024
Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random
NIPS 2024
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning
NIPS 2024
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
NIPS 2024
Worst-Case Offline Reinforcement Learning with Arbitrary Data Support
NIPS 2024
A Parameterized Theory of PAC Learning
AAAI 2023
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models
NIPS 2023
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms
NIPS 2023
When is Agnostic Reinforcement Learning Statistically Tractable?
NIPS 2023
KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training
NIPS 2023
Revisiting Sample Size Determination in Natural Language Understanding
ACL 2023
Multi-task Representation Learning for Pure Exploration in Bilinear Bandits
NIPS 2023
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