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
Florian WenzelAmazon AWSVerified email at amazon.com
Maja RudolphLead Research Scientist, Bosch Center for AIVerified email at cs.columbia.edu
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
Matthew D. HoffmanResearch Scientist, Google ResearchVerified email at google.com
Chen QiuResearch Scientist, Bosch Center for AIVerified email at us.bosch.com
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 Brain, BerlinVerified email at google.com
Jasper SnoekGoogle BrainVerified email at google.com
Vincent FortuinResearch group leader, Helmholtz AIVerified email at helmholtz-muenchen.de
Yisong YueCalifornia Institute of Technology; Latitude AIVerified email at caltech.edu

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	673	2018
Stochastic Gradient Descent as Approximate Bayesian Inference S Mandt, MD Hoffman, DM Blei Journal of Machine Learning Research 18, 1-35, 2017	588	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	499	2012
Dynamic Word Embeddings R Bamler, S Mandt International Conference on Machine Learning 70, 380-389, 2017	432	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	289	2020
Disentangled Sequential Autoencoder Y Li, S Mandt International Conference on Machine Learning 80, 5670-5679, 2018	279	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	218	2019
GP-VAE: Deep Probabilistic Time Series Imputation V Fortuin, D Baranchuk, G Rätsch, S Mandt Artificial Intelligence and Statistics (AISTATS), 2020, 2020	187*	2020
Iterative Amortized Inference J Marino, Y Yue, S Mandt International Conference on Machine Learning 80, 3403--3412, 2018	155	2018
Exponential Family Embeddings MR Rudolph, FJR Ruiz, S Mandt, DM Blei Neural Information Processing Systems, 2016	149	2016
A Variational Analysis of Stochastic Gradient Algorithms S Mandt, MD Hoffman, DM Blei International Conference on Machine Learning 48, 354--363, 2016	148	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	101	2010
Deep Generative Video Compression J Han, S Lombardo, C Schroers, S Mandt Neural Information Processing Systems, 2019	83*	2019
Determinantal Point Processes for Mini-Batch Diversification C Zhang, H Kjellström, S Mandt Uncertainty in Artificial Intelligence, 2017	81	2017
Improving inference for neural image compression Y Yang, R Bamler, S Mandt Neural Information Processing Systems, 2020	75	2020
Diffusion probabilistic modeling for video generation R Yang, P Srivastava, S Mandt arXiv preprint arXiv:2203.09481, 2022	71	2022
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	66	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	65	2021
Quasi-Monte Carlo Variational Inference A Buchholz, F Wenzel, S Mandt International Conference on Machine Learning 80, 668-677, 2018	61	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	54	2017

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