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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
Good Classification Measures and How to Find Them
NIPS 2021
Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation
NIPS 2021
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning
NIPS 2021
The Implicit Bias of Minima Stability: A View from Function Space
NIPS 2021
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers
NIPS 2021
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning
NIPS 2021
The Pareto Frontier of model selection for general Contextual Bandits
NIPS 2021
Learning curves of generic features maps for realistic datasets with a teacher-student model
NIPS 2021
Training Neural Networks is ER-complete
NIPS 2021
Understanding the Under-Coverage Bias in Uncertainty Estimation
NIPS 2021
Relative Flatness and Generalization
NIPS 2021
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms
NIPS 2021
Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions
NIPS 2021
Adapting to function difficulty and growth conditions in private optimization
NIPS 2021
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity
NIPS 2021
Tighter Expected Generalization Error Bounds via Wasserstein Distance
NIPS 2021
Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics
NIPS 2021
Decoupling the Depth and Scope of Graph Neural Networks
NIPS 2021
Time-independent Generalization Bounds for SGLD in Non-convex Settings
NIPS 2021
Convergence and Alignment of Gradient Descent with Random Backpropagation Weights
NIPS 2021
Optimality of variational inference for stochasticblock model with missing links
NIPS 2021
A Convergence Analysis of Gradient Descent on Graph Neural Networks
NIPS 2021
Implicit Regularization in Matrix Sensing via Mirror Descent
NIPS 2021
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting
NIPS 2021
When Are Solutions Connected in Deep Networks?
NIPS 2021
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