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Methodology
← Optimization & Theory
Machine Learning
›
Optimization & Theory
›
Stochastic Processes
2667 directly classified papers
Papers per year
2003: 4
2004: 1
2005: 2
2006: 9
2007: 11
2008: 17
2009: 18
2010: 30
2011: 36
2012: 37
2013: 50
2014: 56
2015: 60
2016: 77
2017: 132
2018: 154
2019: 211
2020: 244
2021: 311
2022: 279
2023: 376
2024: 326
2025: 157
2026: 69
Papers
Stochastic Fractional Hamiltonian Monte Carlo
IJCAI 2018
Fast Estimation of Causal Interactions using Wold Processes
NIPS 2018
Hierarchical Dirichlet Gaussian Marked Hawkes Process for Narrative Reconstruction in Continuous Time Domain
EMNLP 2018
Analytic Expressions for Probabilistic Moments of PL-DNN With Gaussian Input
CVPR 2018
Deep Spatio-Temporal Random Fields for Efficient Video Segmentation
CVPR 2018
Finite Sample Analysis of Two-Timescale Stochastic Approximation with Applications to Reinforcement Learning
COLT 2018
Batch IS NOT Heavy: Learning Word Representations From All Samples
ACL 2018
Incremental Computation of Infix Probabilities for Probabilistic Finite Automata
EMNLP 2018
Adaptive Methods for Nonconvex Optimization
NIPS 2018
Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates
NIPS 2018
The promises and pitfalls of Stochastic Gradient Langevin Dynamics
NIPS 2018
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models
AISTATS 2018
Time-evolving Text Classification with Deep Neural Networks
IJCAI 2018
First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time
NIPS 2018
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation
COLT 2018
Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling
AISTATS 2018
Online Deep Learning: Learning Deep Neural Networks on the Fly
IJCAI 2018
A Unified Analysis of Stochastic Momentum Methods for Deep Learning
IJCAI 2018
Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning
ACL 2018
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD
AISTATS 2018
Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs
NIPS 2018
On Truly Block Eigensolvers via Riemannian Optimization
AISTATS 2018
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes
NIPS 2018
Analyzing Reaction Time Sequences from Human Participants in Auditory Experiments
INTERSPEECH 2018
Finding Global Optima in Nonconvex Stochastic Semidefinite Optimization with Variance Reduction
AISTATS 2018
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