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1705.11140
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Variational Sequential Monte Carlo
International Conference on Artificial Intelligence and Statistics (AISTATS), 2017
31 May 2017
C. A. Naesseth
Scott W. Linderman
Rajesh Ranganath
David M. Blei
BDL
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Papers citing
"Variational Sequential Monte Carlo"
50 / 147 papers shown
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Dynamical system reconstruction from partial observations using stochastic dynamics
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Asymptotically exact variational flows via involutive MCMC kernels
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Sequential Monte Carlo for Policy Optimization in Continuous POMDPs
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344
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22 May 2025
Trust-Region Twisted Policy Improvement
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End-To-End Learning of Gaussian Mixture Priors for Diffusion Sampler
International Conference on Learning Representations (ICLR), 2025
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01 Mar 2025
Deep Variational Sequential Monte Carlo for High-Dimensional Observations
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Wessel L. van Nierop
Stefano Rini
Ruud J. G. van Sloun
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130
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10 Jan 2025
Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics
Hanming Yang
A. Moretti
Sebastian Macaluso
Philippe Chlenski
C. A. Naesseth
I. Pe’er
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363
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03 Jan 2025
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks
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Benjamin Cox
Santiago Segarra
Victor Elvira
402
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23 Nov 2024
Recursive Learning of Asymptotic Variational Objectives
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Alessandro Mastrototaro
Mathias Müller
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210
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04 Nov 2024
Meta-Dynamical State Space Models for Integrative Neural Data Analysis
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Josue Nassar
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436
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07 Oct 2024
Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm
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Hao Wu
Tommaso Salvatori
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Probabilistic Programming with Programmable Variational Inference
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Alexander K. Lew
Xiaoyan Wang
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Mathieu Huot
Martin Rinard
Vikash K. Mansinghka
392
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22 Jun 2024
KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty
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Niklas Freymuth
Gerhard Neumann
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262
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21 Jun 2024
Regime Learning for Differentiable Particle Filters
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Yuhui Luo
Wenwu Wang
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Yunpeng Li
253
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08 May 2024
Revisiting semi-supervised training objectives for differentiable particle filters
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Jiaxi Li
John-Joseph Brady
Xiongjie Chen
Yunpeng Li
157
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Multi-AGV Path Planning Method via Reinforcement Learning and Particle Filters
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27 Mar 2024
Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference
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J. Loper
Jeffrey Regier
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248
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Normalizing Flow-based Differentiable Particle Filters
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Yunpeng Li
208
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Differentiable Particle Filtering using Optimal Placement Resampling
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Olivér Törő
Tamás Bécsi
193
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Provably Scalable Black-Box Variational Inference with Structured Variational Families
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Jacob R. Gardner
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501
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Online Variational Sequential Monte Carlo
Alessandro Mastrototaro
Jimmy Olsson
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306
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Learning Differentiable Particle Filter on the Fly
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Xiongjie Chen
Yunpeng Li
279
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On Feynman--Kac training of partial Bayesian neural networks
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Zheng Zhao
Sebastian Mair
Thomas B. Schön
Jens Sjölund
259
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30 Oct 2023
Using Autodiff to Estimate Posterior Moments, Marginals and Samples
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Sam Bowyer
Thomas Heap
Laurence Aitchison
260
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26 Oct 2023
Variational autoencoder with weighted samples for high-dimensional non-parametric adaptive importance sampling
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François Bachoc
Jérome Morio
Timothé Krauth
275
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13 Oct 2023
Reparameterized Variational Rejection Sampling
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
M. Jankowiak
Du Phan
DRL
BDL
222
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26 Sep 2023
NAS-X: Neural Adaptive Smoothing via Twisting
Neural Information Processing Systems (NeurIPS), 2023
Dieterich Lawson
Michael Y. Li
Scott W. Linderman
231
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Ensemble Kalman Filters with Resampling
Omar Al Ghattas
Jiajun Bao
D. Sanz-Alonso
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Last layer state space model for representation learning and uncertainty quantification
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M. Charbit
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178
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04 Jul 2023
Adaptive Annealed Importance Sampling with Constant Rate Progress
International Conference on Machine Learning (ICML), 2023
Shirin Goshtasbpour
Victor Cohen
Fernando Perez-Cruz
241
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Score-based Data Assimilation
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Sacha Lewin
Gilles Louppe
390
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18 Jun 2023
Massively Parallel Reweighted Wake-Sleep
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Thomas Heap
Gavin Leech
Laurence Aitchison
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136
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18 May 2023
Variational Nonlinear Kalman Filtering with Unknown Process Noise Covariance
IEEE Transactions on Aerospace and Electronic Systems (IEEE T-AES), 2023
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Jinjie Hu
Zengfu Wang
Q. Cheng
168
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Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems
Andreas Look
M. Kandemir
Barbara Rakitsch
Jan Peters
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154
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Resampling Gradients Vanish in Differentiable Sequential Monte Carlo Samplers
Johannes Zenn
Kushagra Pandey
371
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U-Statistics for Importance-Weighted Variational Inference
Javier Burroni
Kenta Takatsu
Justin Domke
Daniel Sheldon
162
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An overview of differentiable particle filters for data-adaptive sequential Bayesian inference
Foundations of Data Science (FDS), 2023
Xiongjie Chen
Yunpeng Li
264
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19 Feb 2023
Reduced-Order Autodifferentiable Ensemble Kalman Filters
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Yuming Chen
D. Sanz-Alonso
Rebecca Willett
159
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27 Jan 2023
Statistical Distance Based Deterministic Offspring Selection in SMC Methods
Oskar Kviman
Hazal Koptagel
Harald Melin
J. Lagergren
148
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23 Dec 2022
Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving
Conference on Robot Learning (CoRL), 2022
Angad Singh
Omar Makhlouf
Maximilian Igl
Joao Messias
Arnaud Doucet
Shimon Whiteson
217
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Nonlinear System Identification: Learning while respecting physical models using a sequential Monte Carlo method
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Johan Wågberg
Fredrik Lindsten
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Thomas B. Schon
170
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Efficient variational approximations for state space models
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Gerhard Neumann
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A Variational Perspective on Generative Flow Networks
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Jan-Willem van de Meent
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Optimization of Annealed Importance Sampling Hyperparameters
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