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Optimization & Theory

6,829 papers (27 classified directly here)

Subtopics

Model Compression (1,785) Optimization (1,664) Efficient Computing (1,579) Theory (1,082) Neural Network Optimization (903) Evaluation (509) Loss Functions (123) Interpretability (105) Stochastic Methods (97) Regularization (77) Generalization (24) Sparse Optimization (7)

Papers per year

8
50
642
1173
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Papers (including subtopics)

Implicit Regularization in Deep Tucker Factorization: Low-Rankness via Structured Sparsity AISTATS 2024
Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias AISTATS 2024
Spectrum Extraction and Clipping for Implicitly Linear Layers AISTATS 2024
Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures AISTATS 2024
General Tail Bounds for Non-Smooth Stochastic Mirror Descent AISTATS 2024
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training AISTATS 2024
Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence AISTATS 2024
SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification AISTATS 2024
Benefits of Non-Linear Scale Parameterizations in Black Box Variational Inference through Smoothness Results and Gradient Variance Bounds AISTATS 2024
Length independent PAC-Bayes bounds for Simple RNNs AISTATS 2024
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks AISTATS 2024
Why is parameter averaging beneficial in SGD? An objective smoothing perspective AISTATS 2024
On the Impact of Overparameterization on the Training of a Shallow Neural Network in High Dimensions AISTATS 2024
Manifold-Aligned Counterfactual Explanations for Neural Networks AISTATS 2024
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport AISTATS 2024
An Analytic Solution to Covariance Propagation in Neural Networks AISTATS 2024
Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements AISTATS 2024
Sinkhorn Flow as Mirror Flow: A Continuous-Time Framework for Generalizing the Sinkhorn Algorithm AISTATS 2024
Weight-Sharing Regularization AISTATS 2024
The effect of Leaky ReLUs on the training and generalization of overparameterized networks AISTATS 2024
Improving Robustness via Tilted Exponential Layer: A Communication-Theoretic Perspective AISTATS 2024
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning AISTATS 2024
Sharpened Lazy Incremental Quasi-Newton Method AISTATS 2024
Parameter-Agnostic Optimization under Relaxed Smoothness AISTATS 2024
Layer-wise Regularized Dropout for Neural Language Models COLING 2024