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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
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
ICML 2018
Regret Minimization for Partially Observable Deep Reinforcement Learning
ICML 2018
Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games
ICML 2018
Feasible Arm Identification
ICML 2018
An Alternative View: When Does SGD Escape Local Minima?
ICML 2018
Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks
ICML 2018
On the Spectrum of Random Features Maps of High Dimensional Data
ICML 2018
The Dynamics of Learning: A Random Matrix Approach
ICML 2018
Towards Black-box Iterative Machine Teaching
ICML 2018
Open Category Detection with PAC Guarantees
ICML 2018
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning
ICML 2018
Bounds on the Approximation Power of Feedforward Neural Networks
ICML 2018
Which Training Methods for GANs do actually Converge?
ICML 2018
Dropout Training, Data-dependent Regularization, and Generalization Bounds
ICML 2018
On Learning Sparsely Used Dictionaries from Incomplete Samples
ICML 2018
Do Outliers Ruin Collaboration?
ICML 2018
Tight Regret Bounds for Bayesian Optimization in One Dimension
ICML 2018
Bounding and Counting Linear Regions of Deep Neural Networks
ICML 2018
Decoupling Gradient-Like Learning Rules from Representations
ICML 2018
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator
ICML 2018
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks
ICML 2018
Probably Approximately Metric-Fair Learning
ICML 2018
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
ICML 2018
Understanding Generalization and Optimization Performance of Deep CNNs
ICML 2018
Neural Networks for Predicting Algorithm Runtime Distributions
IJCAI 2018
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