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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
Statistical Foundations of Prior-Data Fitted Networks
ICML 2023
Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature
ICML 2023
Diffusion Models are Minimax Optimal Distribution Estimators
ICML 2023
TRAK: Attributing Model Behavior at Scale
ICML 2023
PAC Generalization via Invariant Representations
ICML 2023
Federated Online and Bandit Convex Optimization
ICML 2023
Brauer’s Group Equivariant Neural Networks
ICML 2023
How Jellyfish Characterise Alternating Group Equivariant Neural Networks
ICML 2023
The Ideal Continual Learner: An Agent That Never Forgets
ICML 2023
Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation
ICML 2023
Certifying Ensembles: A General Certification Theory with S-Lipschitzness
ICML 2023
Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies
ICML 2023
Equivariant Polynomials for Graph Neural Networks
ICML 2023
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions
ICML 2023
The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning
ICML 2023
Escaping saddle points in zeroth-order optimization: the power of two-point estimators
ICML 2023
Dimension-independent Certified Neural Network Watermarks via Mollifier Smoothing
ICML 2023
The Edge of Orthogonality: A Simple View of What Makes BYOL Tick
ICML 2023
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
ICML 2023
Identifiability and Generalizability in Constrained Inverse Reinforcement Learning
ICML 2023
ModelDiff: A Framework for Comparing Learning Algorithms
ICML 2023
Sequential Changepoint Detection via Backward Confidence Sequences
ICML 2023
Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods
ICML 2023
Sequence Modeling with Multiresolution Convolutional Memory
ICML 2023
Quantitative Universal Approximation Bounds for Deep Belief Networks
ICML 2023
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