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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
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks
ICML 2020
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
ICML 2020
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis
ICML 2020
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning
ICML 2020
ConQUR: Mitigating Delusional Bias in Deep Q-Learning
ICML 2020
The Many Shapley Values for Model Explanation
ICML 2020
No-Regret Exploration in Goal-Oriented Reinforcement Learning
ICML 2020
Student Specialization in Deep Rectified Networks With Finite Width and Input Dimension
ICML 2020
Sequential Transfer in Reinforcement Learning with a Generative Model
ICML 2020
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis
ICML 2020
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes
ICML 2020
Disentangling Trainability and Generalization in Deep Neural Networks
ICML 2020
Maximum-and-Concatenation Networks
ICML 2020
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation
ICML 2020
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
ICML 2020
It’s Not What Machines Can Learn, It’s What We Cannot Teach
ICML 2020
Learning Near Optimal Policies with Low Inherent Bellman Error
ICML 2020
Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case
ICML 2020
Learning with Feature and Distribution Evolvable Streams
ICML 2020
Error-Bounded Correction of Noisy Labels
ICML 2020
Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting
ICML 2020
When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment
ICML 2020
Concentration of Distortion: The Value of Extra Voters in Randomized Social Choice
IJCAI 2020
Efficient Algorithms for Learning Revenue-Maximizing Two-Part Tariffs
IJCAI 2020
Deep Learning for Abstract Argumentation Semantics
IJCAI 2020
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