conftrace_

Optimization & Theory

36,874 papers (3 classified directly here)

Subtopics

Optimization (14,983) Bayesian Inference (5,566) Learning Theory (5,334) Theory (4,955) Statistical Learning (4,117) Neural Network Optimization (3,649) Stochastic Processes (2,667) Loss Functions (1,162) Distributed Learning (1,135) Stochastic Methods (1,118) Online Algorithms (806) Statistics (684) Information Theory (318) Generalization (220) Sparse Optimization (158) Sample Complexity (99) Game Theory (30) Robust Optimization (17) Optimal Transport (14) Approximation Algorithms (10) Distributed Optimization (8) Convex Optimization (8)

Papers per year

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Papers (including subtopics)

Selective Nonparametric Regression via Testing ACML 2023
The Fine Print on Tempered Posteriors ACML 2023
Folded Hamiltonian Monte Carlo for Bayesian Generative Adversarial Networks ACML 2023
Logarithmic regret in communicating MDPs: Leveraging known dynamics with bandits ACML 2023
Intractability of Learning the Discrete Logarithm with Gradient-Based Methods ACML 2023
A Partially Observable Monte Carlo Planning Algorithm Based on Path Modification ACML 2023
Can Infinitely Wide Deep Nets Help Small-data Multi-label Learning? ACML 2023
Revisiting Structured Dropout ACML 2023
BaCaDI: Bayesian Causal Discovery with Unknown Interventions AISTATS 2023
Discovering Many Diverse Solutions with Bayesian Optimization AISTATS 2023
Distance-to-Set Priors and Constrained Bayesian Inference AISTATS 2023
Error Estimation for Random Fourier Features AISTATS 2023
Noisy Low-rank Matrix Optimization: Geometry of Local Minima and Convergence Rate AISTATS 2023
A Tale of Sampling and Estimation in Discounted Reinforcement Learning AISTATS 2023
Safe Sequential Testing and Effect Estimation in Stratified Count Data AISTATS 2023
Particle algorithms for maximum likelihood training of latent variable models AISTATS 2023
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation AISTATS 2023
Implications of sparsity and high triangle density for graph representation learning AISTATS 2023
The Schrödinger Bridge between Gaussian Measures has a Closed Form AISTATS 2023
Data Banzhaf: A Robust Data Valuation Framework for Machine Learning AISTATS 2023
Do Bayesian Neural Networks Need To Be Fully Stochastic? AISTATS 2023
Mode-Seeking Divergences: Theory and Applications to GANs AISTATS 2023
Origins of Low-Dimensional Adversarial Perturbations AISTATS 2023
Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation AISTATS 2023
The Power of Recursion in Graph Neural Networks for Counting Substructures AISTATS 2023