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Methodology
← Optimization & Theory
Machine Learning
›
Optimization & Theory
›
Theory
4950 directly classified 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
Experimental Designs for Heteroskedastic Variance
NIPS 2023
On permutation symmetries in Bayesian neural network posteriors: a variational perspective
NIPS 2023
A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning
NIPS 2023
One-step differentiation of iterative algorithms
NIPS 2023
On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds
NIPS 2023
A Fractional Graph Laplacian Approach to Oversmoothing
NIPS 2023
Investigating how ReLU-networks encode symmetries
NIPS 2023
A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs
NIPS 2023
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension
NIPS 2023
Window-Based Distribution Shift Detection for Deep Neural Networks
NIPS 2023
The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
NIPS 2023
When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability
NIPS 2023
(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy
NIPS 2023
Causal Component Analysis
NIPS 2023
Demystifying Oversmoothing in Attention-Based Graph Neural Networks
NIPS 2023
PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization
NIPS 2023
Isometric Quotient Variational Auto-Encoders for Structure-Preserving Representation Learning
NIPS 2023
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
NIPS 2023
Critical Initialization of Wide and Deep Neural Networks using Partial Jacobians: General Theory and Applications
NIPS 2023
What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement.
NIPS 2023
Limits, approximation and size transferability for GNNs on sparse graphs via graphops
NIPS 2023
Training Neural Networks is NP-Hard in Fixed Dimension
NIPS 2023
On the Convergence of Black-Box Variational Inference
NIPS 2023
Fine-grained Expressivity of Graph Neural Networks
NIPS 2023
Neural Oscillators are Universal
NIPS 2023
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