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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
Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes
NIPS 2024
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
NIPS 2024
Generalization Bounds via Conditional $f$-Information
NIPS 2024
Scaling Law for Time Series Forecasting
NIPS 2024
Dissecting the Failure of Invariant Learning on Graphs
NIPS 2024
Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces
NIPS 2024
Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability
NIPS 2024
How Far Can Transformers Reason? The Globality Barrier and Inductive Scratchpad
NIPS 2024
Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective
NIPS 2024
Worst-Case Offline Reinforcement Learning with Arbitrary Data Support
NIPS 2024
The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks
NIPS 2024
In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness
NIPS 2024
Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input
NIPS 2024
Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space
NIPS 2024
Understanding Transformer Reasoning Capabilities via Graph Algorithms
NIPS 2024
Lookback Prophet Inequalities
NIPS 2024
Learning to Understand: Identifying Interactions via the Möbius Transform
NIPS 2024
The Implicit Bias of Gradient Descent toward Collaboration between Layers: A Dynamic Analysis of Multilayer Perceptions
NIPS 2024
Consistency of Neural Causal Partial Identification
NIPS 2024
Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning
CVPR 2024
Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
NIPS 2024
Learning Cut Generating Functions for Integer Programming
NIPS 2024
Neural Redshift: Random Networks are not Random Functions
CVPR 2024
Randomized Strategic Facility Location with Predictions
NIPS 2024
Improving Adaptivity via Over-Parameterization in Sequence Models
NIPS 2024
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