← Optimization & Theory
Machine Learning › Optimization & Theory ›

Stochastic Processes

2667 directly classified papers

Papers per year

Papers

Estimating Training Data Influence by Tracing Gradient Descent NIPS 2020
An Improved Analysis of Stochastic Gradient Descent with Momentum NIPS 2020
Collapsing Bandits and Their Application to Public Health Intervention NIPS 2020
Online Learning for Active Cache Synchronization ICML 2020
Visual Relationship Detection with Low Rank Non-Negative Tensor Decomposition AAAI 2020
Efficient Estimation of Influence of a Training Instance EMNLP 2020
Likelihood-free MCMC with Amortized Approximate Ratio Estimators ICML 2020
Momentum Improves Normalized SGD ICML 2020
Online Learning with Dependent Stochastic Feedback Graphs ICML 2020
The Boomerang Sampler ICML 2020
Batch Stationary Distribution Estimation ICML 2020
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation ICML 2020
Online Multi-Kernel Learning with Graph-Structured Feedback ICML 2020
The Implicit Regularization of Stochastic Gradient Flow for Least Squares ICML 2020
Generating Diverse Translation from Model Distribution with Dropout EMNLP 2020
Is Big Data Performance Reproducible in Modern Cloud Networks? NSDI 2020
Generating Narrative Text in a Switching Dynamical System EMNLP 2020
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference ICML 2020
Convergence Rates for the Stochastic Gradient Descent Method for Non-Convex Objective Functions JMLR 2020
Enhancing Formant Information in Spectrographic Display of Speech INTERSPEECH 2020
Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments INTERSPEECH 2020
Index Tracking with Cardinality Constraints: A Stochastic Neural Networks Approach AAAI 2020
System Identification with Time-Aware Neural Sequence Models AAAI 2020
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics AAAI 2020
Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models JMLR 2020