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← Optimization & Theory
Machine Learning
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Optimization & Theory
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Theory
4,950 papers
Papers per year
2000: 1
2001: 2
2002: 3
2003: 3
2004: 9
2005: 4
2006: 32
2007: 25
2008: 31
2009: 25
2010: 37
2011: 37
2012: 45
2013: 76
2014: 66
2015: 72
2016: 102
2017: 156
2018: 246
2019: 353
2020: 447
2021: 567
2022: 646
2023: 741
2024: 670
2025: 426
2026: 128
Papers
Finite-Sample Regret Bound for Distributionally Robust Offline Tabular Reinforcement Learning
AISTATS 2021
Offline detection of change-points in the mean for stationary graph signals.
AISTATS 2021
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders
AISTATS 2021
One-pass Stochastic Gradient Descent in overparametrized two-layer neural networks
AISTATS 2021
On the Memory Mechanism of Tensor-Power Recurrent Models
AISTATS 2021
GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences
AISTATS 2021
Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions
AISTATS 2021
Differentiating the Value Function by using Convex Duality
AISTATS 2021
Subspace Embeddings under Nonlinear Transformations
ALT 2021
Near-tight Closure Bounds for the Littlestone and Threshold Dimensions
ALT 2021
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound
ALT 2021
Submodular combinatorial information measures with applications in machine learning
ALT 2021
Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions
ALT 2021
Learning in Matrix Games can be Arbitrarily Complex
COLT 2021
Optimal Dynamic Regret in Exp-Concave Online Learning
COLT 2021
A Law of Robustness for Two-Layers Neural Networks
COLT 2021
Non-asymptotic approximations of neural networks by Gaussian processes
COLT 2021
Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks
COLT 2021
Convergence rates and approximation results for SGD and its continuous-time counterpart
COLT 2021
Bounded Memory Active Learning through Enriched Queries
COLT 2021
Group testing and local search: is there a computational-statistical gap?
COLT 2021
It was “all” for “nothing”: sharp phase transitions for noiseless discrete channels
COLT 2021
Average-Case Communication Complexity of Statistical Problems
COLT 2021
Almost sure convergence rates for Stochastic Gradient Descent and Stochastic Heavy Ball
COLT 2021
Implicit Regularization in ReLU Networks with the Square Loss
COLT 2021
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