conftrace_
← Optimization & Theory
Machine Learning › Optimization & Theory ›

Stochastic Methods

1,118 papers

Papers per year

2
18
33
106
45
'05
'10
'15
'20
'25

Papers

Incorporating Behavioral Constraints in Online AI Systems AAAI 2019
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games Using Baselines AAAI 2019
Scalable and Efficient Pairwise Learning to Achieve Statistical Accuracy AAAI 2019
Communication-Efficient Stochastic Gradient MCMC for Neural Networks AAAI 2019
Cogra: Concept-Drift-Aware Stochastic Gradient Descent for Time-Series Forecasting AAAI 2019
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning AAAI 2019
Active Mini-Batch Sampling Using Repulsive Point Processes AAAI 2019
On Testing of Uniform Samplers AAAI 2019
Interleave Variational Optimization with Monte Carlo Sampling: A Tale of Two Approximate Inference Paradigms AAAI 2019
Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing AAAI 2019
Interpolated Spectral NGram Language Models ACL 2019
Proximal Splitting Meets Variance Reduction AISTATS 2019
Risk-Averse Stochastic Convex Bandit AISTATS 2019
Pathwise Derivatives for Multivariate Distributions AISTATS 2019
On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes AISTATS 2019
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron AISTATS 2019
Stochastic Negative Mining for Learning with Large Output Spaces AISTATS 2019
An Information-Theoretic Approach to Minimax Regret in Partial Monitoring COLT 2019
Joint Manifold Diffusion for Combining Predictions on Decoupled Observations CVPR 2019
Online High Rank Matrix Completion CVPR 2019
A Sufficient Condition for Convergences of Adam and RMSProp CVPR 2019
Making Asynchronous Stochastic Gradient Descent Work for Transformers EMNLP 2019
Online Variance Reduction with Mixtures ICML 2019
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances ICML 2019
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number ICML 2019