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Methodology
← Optimization & Theory
Machine Learning
›
Optimization & Theory
›
Learning Theory
5312 directly classified 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
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds
NIPS 2018
On Oracle-Efficient PAC RL with Rich Observations
NIPS 2018
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes
NIPS 2018
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions
NIPS 2018
How Many Samples are Needed to Estimate a Convolutional Neural Network?
NIPS 2018
Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model
NIPS 2018
PAC-learning in the presence of adversaries
NIPS 2018
Evaluating Historical Text Normalization Systems: How Well Do They Generalize?
NAACL 2018
Tight Bounds for Bandit Combinatorial Optimization
COLT 2017
Lower bounds on the robustness to adversarial perturbations
NIPS 2017
SGD Learns the Conjugate Kernel Class of the Network
NIPS 2017
A Learning Theory of Ranking Aggregation
AISTATS 2017
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data
AISTATS 2017
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks
NIPS 2017
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability
NIPS 2017
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference
NIPS 2017
Optimal learning via local entropies and sample compression
COLT 2017
On the Consistency of Ordinal Regression Methods
JMLR 2017
Analyzing Tensor Power Method Dynamics in Overcomplete Regime
JMLR 2017
Bridging Supervised Learning and Test-Based Co-optimization
JMLR 2017
Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds
COLT 2017
On Learning vs. Refutation
COLT 2017
On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities
COLT 2017
Online Learning Without Prior Information
COLT 2017
Towards Instance Optimal Bounds for Best Arm Identification
COLT 2017
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