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← Optimization & Theory
Machine Learning
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Optimization & Theory
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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
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC
NIPS 2013
Predictive PAC Learning and Process Decompositions
NIPS 2013
Approximate Gaussian process inference for the drift function in stochastic differential equations
NIPS 2013
Capacity of strong attractor patterns to model behavioural and cognitive prototypes
NIPS 2013
Adaptive Estimation of Measurement Bias in Three-Dimensional Field Sensors with Angular Rate Sensors: Theory and Comparative Experimental Evaluation
RSS 2013
Topology-Constrained Layered Tracking with Latent Flow
ICCV 2013
Approximate inference in latent Gaussian-Markov models from continuous time observations
NIPS 2013
Efficient Variational Inference for Gaussian Process Regression Networks
AISTATS 2013
Toward Optimal Stratification for Stratified Monte-Carlo Integration
ICML 2013
Better Mixing via Deep Representations
ICML 2013
Parsing epileptic events using a Markov switching process model for correlated time series
ICML 2013
Dynamic Covariance Models for Multivariate Financial Time Series
ICML 2013
Gaussian Process Kernels for Pattern Discovery and Extrapolation
ICML 2013
Sparse Subspace Denoising for Image Manifolds
CVPR 2013
On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations
NIPS 2013
Projecting Ising Model Parameters for Fast Mixing
NIPS 2013
Online Learning of Dynamic Parameters in Social Networks
NIPS 2013
Solving inverse problem of Markov chain with partial observations
NIPS 2013
An Adaptive Learning Rate for Stochastic Variational Inference
ICML 2013
Correlations strike back (again): the case of associative memory retrieval
NIPS 2013
Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions
NIPS 2013
Real-Time Inference for a Gamma Process Model of Neural Spiking
NIPS 2013
Computing the Stationary Distribution Locally
NIPS 2013
Thompson Sampling for 1-Dimensional Exponential Family Bandits
NIPS 2013
Tracking Time-varying Graphical Structure
NIPS 2013
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