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Methodology
← Optimization & Theory
Machine Learning
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Optimization & Theory
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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
Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
NIPS 2023
Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model
NIPS 2023
Leveraging the two-timescale regime to demonstrate convergence of neural networks
NIPS 2023
What do neural networks learn in image classification? A frequency shortcut perspective
ICCV 2023
Leveraging Demonstrations to Improve Online Learning: Quality Matters
ICML 2023
Generalization Analysis for Contrastive Representation Learning
ICML 2023
PAC Generalization via Invariant Representations
ICML 2023
The Power of Uniform Sampling for k-Median
ICML 2023
Maximal Initial Learning Rates in Deep ReLU Networks
ICML 2023
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources
ICML 2023
Differentiable Tree Operations Promote Compositional Generalization
ICML 2023
The SSL Interplay: Augmentations, Inductive Bias, and Generalization
ICML 2023
On the Role of Attention in Prompt-tuning
ICML 2023
Atari-5: Distilling the Arcade Learning Environment down to Five Games
ICML 2023
On Single-Index Models beyond Gaussian Data
NIPS 2023
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation
ICML 2023
Does a Neural Network Really Encode Symbolic Concepts?
ICML 2023
Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents
ICML 2023
Do Machine Learning Models Learn Statistical Rules Inferred from Data?
ICML 2023
Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation
ICML 2023
On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation
ICML 2023
Predictive Flows for Faster Ford-Fulkerson
ICML 2023
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models
ICML 2023
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks
NIPS 2023
Understanding Backdoor Attacks through the Adaptability Hypothesis
ICML 2023
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