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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
Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-identification
AAAI 2022
Hyperverlet: A Symplectic Hypersolver for Hamiltonian Systems
AAAI 2022
Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model
AAAI 2022
Conditional Loss and Deep Euler Scheme for Time Series Generation
AAAI 2022
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems
AAAI 2022
Hindsight Network Credit Assignment: Efficient Credit Assignment in Networks of Discrete Stochastic Units
AAAI 2022
Structure-Preserving Learning Using Gaussian Processes and Variational Integrators
L4DC 2022
Optimal Control with Learning on the Fly: System with Unknown Drift
L4DC 2022
Time Varying Regression with Hidden Linear Dynamics
L4DC 2022
Online Estimation and Control with Optimal Pathlength Regret
L4DC 2022
Learning-based Moving Horizon Estimation through Differentiable Convex Optimization Layers
L4DC 2022
Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguities
CONLL 2022
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes
ACML 2022
Robust computation of optimal transport by $β$-potential regularization
ACML 2022
Orientation Estimation of Abdominal Ultrasound Images with Multi-Hypotheses Networks
MIDL 2022
Deep Decomposition for Stochastic Normal-Abnormal Transport
CVPR 2022
Global Context With Discrete Diffusion in Vector Quantised Modelling for Image Generation
CVPR 2022
Maximum Consensus by Weighted Influences of Monotone Boolean Functions
CVPR 2022
NeuralEF: Deconstructing Kernels by Deep Neural Networks
ICML 2022
Centroid Approximation for Bootstrap: Improving Particle Quality at Inference
ICML 2022
Hessian-Free High-Resolution Nesterov Acceleration For Sampling
ICML 2022
Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness
ICML 2022
Three-stage Evolution and Fast Equilibrium for SGD with Non-degerate Critical Points
ICML 2022
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning
ICML 2022
Stability Based Generalization Bounds for Exponential Family Langevin Dynamics
ICML 2022
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