Stephan Mandt

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Robert BamlerUniveristy of Tübingen, GermanyVerified email at uni-tuebingen.de
Marius KloftRPTU Kaiserslautern-LandauVerified email at cs.uni-kl.de
Cheng ZhangPrincipal Researcher, Microsoft Research, Cambridge, UKVerified email at kth.se
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
Maja RudolphLead Research Scientist, Bosch Center for AIVerified email at cs.columbia.edu
Florian WenzelAmazon AWSVerified email at amazon.com
Yibo YangUniversity of California, IrvineVerified email at uci.edu
Achim RoschInstitute for Theoretical Physics, University of CologneVerified email at thp.uni-koeln.de
Ruihan YangUniversity of California, IrvineVerified email at uci.edu
Joseph MarinoResearch Scientist, DeepMindVerified email at deepmind.com
Chen QiuResearch Scientist, Bosch Center for AIVerified email at us.bosch.com
Matthew D. HoffmanResearch Scientist, Google ResearchVerified email at google.com
Vincent FortuinResearch group leader, Helmholtz AIVerified email at helmholtz-muenchen.de
Yingzhen LiImperial College LondonVerified email at imperial.ac.uk
Sebastian NowozinGoogle DeepMindVerified email at deepmind.com
Tim SalimansGoogle Brain AmsterdamVerified email at google.com
Bastiaan S. VeelingMicrosoft Research AmsterdamVerified email at uva.nl
Rodolphe JENATTONSenior machine learning scientist at Google DeepMind, BerlinVerified email at google.com
Jasper SnoekGoogle BrainVerified email at google.com
Gunnar RätschProfessor, ETH ZürichVerified email at inf.ethz.ch

Stephan Mandt

Associate Professor, University of California, Irvine

Verified email at uci.edu - Homepage

Machine Learning Variational Inference Neural Data Compression ML for Science Generative Models


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Advances in variational inference C Zhang, J Bütepage, H Kjellström, S Mandt IEEE transactions on pattern analysis and machine intelligence 41 (8), 2008-2026, 2018	713	2018
Stochastic Gradient Descent as Approximate Bayesian Inference S Mandt, MD Hoffman, DM Blei Journal of Machine Learning Research 18, 1-35, 2017	609	2017
Fermionic transport and out-of-equilibrium dynamics in a homogeneous Hubbard model with ultracold atoms U Schneider, L Hackermüller, JP Ronzheimer, S Will, S Braun, T Best, ... Nature Physics 8 (3), 213-218, 2012	502	2012
Dynamic Word Embeddings R Bamler, S Mandt International Conference on Machine Learning 70, 380-389, 2017	430	2017
How good is the bayes posterior in deep neural networks really? F Wenzel, K Roth, BS Veeling, J Świątkowski, L Tran, S Mandt, J Snoek, ... International Conference on Machine Learning, 2020, 2020	309	2020
Disentangled Sequential Autoencoder Y Li, S Mandt International Conference on Machine Learning 80, 5670-5679, 2018	288	2018
Image anomaly detection with generative adversarial networks L Deecke, R Vandermeulen, L Ruff, S Mandt, M Kloft Machine Learning and Knowledge Discovery in Databases: European Conference …, 2019	233	2019
GP-VAE: Deep Probabilistic Time Series Imputation V Fortuin, D Baranchuk, G Rätsch, S Mandt Artificial Intelligence and Statistics (AISTATS), 2020, 2020	205*	2020
Iterative Amortized Inference J Marino, Y Yue, S Mandt International Conference on Machine Learning 80, 3403--3412, 2018	165	2018
A Variational Analysis of Stochastic Gradient Algorithms S Mandt, MD Hoffman, DM Blei International Conference on Machine Learning 48, 354--363, 2016	154	2016
Exponential Family Embeddings MR Rudolph, FJR Ruiz, S Mandt, DM Blei Neural Information Processing Systems, 2016	149	2016
Equilibration rates and negative absolute temperatures for ultracold atoms in optical lattices A Rapp, S Mandt, A Rosch Physical review letters 105 (22), 220405, 2010	103	2010
Diffusion probabilistic modeling for video generation R Yang, P Srivastava, S Mandt Entropy 25 (10), 1469, 2023	94	2023
Deep Generative Video Compression J Han, S Lombardo, C Schroers, S Mandt Neural Information Processing Systems, 2019	85*	2019
Improving inference for neural image compression Y Yang, R Bamler, S Mandt Neural Information Processing Systems, 2020	83	2020
Determinantal Point Processes for Mini-Batch Diversification C Zhang, H Kjellström, S Mandt Uncertainty in Artificial Intelligence, 2017	82	2017
Machine learning in thermodynamics: Prediction of activity coefficients by matrix completion F Jirasek, RAS Alves, J Damay, RA Vandermeulen, R Bamler, M Bortz, ... The journal of physical chemistry letters 11 (3), 981-985, 2020	73	2020
Neural Transformation Learning for Deep Anomaly Detection Beyond Images C Qiu, T Pfrommer, M Kloft, S Mandt, M Rudolph International Conference on Machine Learning, 2021	71	2021
Quasi-Monte Carlo Variational Inference A Buchholz, F Wenzel, S Mandt International Conference on Machine Learning 80, 668-677, 2018	64	2018
Factorized Variational Autoencoders for Modeling Audience Reactions to Movies Z Deng, R Navarathna, P Carr, S Mandt, Y Yue, I Matthews, G Mori Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017	58	2017

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