Stephan Mandt

ORCID

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 127

Top Venues

Publications

Kushagra Pandey, Maja Rudolph, Stephan Mandt
Towards Fast Stochastic Sampling in Diffusion Generative Models. CoRR (2024)
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR (2024)
Sungduk Yu, Walter M. Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles Stern, Tom Beucler, Bryce E. Harrop, Benjamin R. Hillman, Andrea M. Jenney, Savannah L. Ferretti, Nana Liu, Animashree Anandkumar, Noah D. Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, Laure Zanna, Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Christopher S. Bretherton, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Mark Taylor, Nathan M. Urban, Janni Yuval, Guang Zhang, Mike Pritchard
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. NeurIPS (2023)
Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt
Understanding Pathologies of Deep Heteroskedastic Regression. CoRR (2023)
Justus C. Will, Andrea M. Jenney, Kara D. Lamb, Michael S. Pritchard, Colleen Kaul, Po-Lun Ma, Kyle Pressel, Jacob Shpund, Marcus van Lier-Walqui, Stephan Mandt
Understanding and Visualizing Droplet Distributions in Simulations of Shallow Clouds. CoRR (2023)
Kushagra Pandey, Stephan Mandt
A Complete Recipe for Diffusion Generative Models. ICCV (2023)
Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt
Zero-Shot Anomaly Detection via Batch Normalization. NeurIPS (2023)
Kushagra Pandey, Stephan Mandt
Generative Diffusions in Augmented Spaces: A Complete Recipe. CoRR (2023)
Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt
Zero-Shot Anomaly Detection without Foundation Models. CoRR (2023)
Yibo Yang, Stephan Mandt, Lucas Theis
An Introduction to Neural Data Compression. Found. Trends Comput. Graph. Vis. 15 (2) (2023)
Aodong Li, Chen Qiu, Padhraic Smyth, Marius Kloft, Stephan Mandt, Maja Rudolph
Deep Anomaly Detection under Labeling Budget Constraints. CoRR (2023)
Yibo Yang, Stephan Eckstein, Marcel Nutz, Stephan Mandt
Estimating the Rate-Distortion Function by Wasserstein Gradient Descent. CoRR (2023)
Yibo Yang, Stephan Mandt
Asymmetrically-powered Neural Image Compression with Shallow Decoders. CoRR (2023)
Sungduk Yu, Walter M. Hannah, Liran Peng, Mohamed Aziz Bhouri, Ritwik Gupta, Jerry Lin, Björn Lütjens, Justus C. Will, Tom Beucler, Bryce E. Harrop, Benjamin R. Hillman, Andrea M. Jenney, Savannah L. Ferretti, Nana Liu, Anima Anandkumar, Noah D. Brenowitz, Veronika Eyring, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Carl Vondrick, Rose Yu, Laure Zanna, Ryan P. Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Gunnar Behrens, Christopher S. Bretherton, Julius J. M. Busecke, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Akshay Subramaniam, Mark A. Taylor, et al.
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. CoRR (2023)
Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt
Insights From Generative Modeling for Neural Video Compression. IEEE Trans. Pattern Anal. Mach. Intell. 45 (8) (2023)
Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth
Inference for mark-censored temporal point processes. UAI (2023)
Prakhar Srivastava, Ruihan Yang, Gavin Kerrigan, Gideon Dresdner, Jeremy McGibbon, Christopher S. Bretherton, Stephan Mandt
Probabilistic Precipitation Downscaling with Optical Flow-Guided Diffusion. CoRR (2023)
Metod Jazbec, Patrick Forré, Stephan Mandt, Dan Zhang, Eric T. Nalisnick
Anytime-Valid Confidence Sequences for Consistent Uncertainty Estimation in Early-Exit Neural Networks. CoRR (2023)
Kushagra Pandey, Maja Rudolph, Stephan Mandt
Efficient Integrators for Diffusion Generative Models. CoRR (2023)
Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes. CoRR (2023)
Fabian Hartung, Billy Joe Franks, Tobias Michels, Dennis Wagner, Philipp Liznerski, Steffen Reithermann, Sophie Fellenz, Fabian Jirasek, Maja Rudolph, Daniel Neider, Heike Leitte, Chen Song, Benjamin Klöpper, Stephan Mandt, Michael Bortz, Jakob Burger, Hans Hasse, Marius Kloft
Deep Anomaly Detection on Tennessee Eastman Process Data. CoRR (2023)
Yibo Yang, Stephan Eckstein, Marcel Nutz, Stephan Mandt
Estimating the Rate-Distortion Function by Wasserstein Gradient Descent. NeurIPS (2023)
Vincent Fortuin, Yingzhen Li, Kevin Murphy, Stephan Mandt, Laura Manduchi
Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072). Dagstuhl Reports 13 (2) (2023)
Ruihan Yang, Prakhar Srivastava, Stephan Mandt
Diffusion Probabilistic Modeling for Video Generation. Entropy 25 (10) (2023)
Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes. ICML (2023)
Ruihan Yang, Stephan Mandt
Lossy Image Compression with Conditional Diffusion Models. NeurIPS (2023)
Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja Rudolph
Deep Anomaly Detection under Labeling Budget Constraints. ICML (2023)
Yibo Yang, Stephan Mandt
Computationally-Efficient Neural Image Compression with Shallow Decoders. ICCV (2023)
Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth
Probabilistic Querying of Continuous-Time Event Sequences. AISTATS (2023)
Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt
SC2: Supervised Compression for Split Computing. CoRR (2022)
Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph
Raising the Bar in Graph-level Anomaly Detection. CoRR (2022)
Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph
Raising the Bar in Graph-level Anomaly Detection. IJCAI (2022)
Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt
Latent Outlier Exposure for Anomaly Detection with Contaminated Data. CoRR (2022)
Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt
Latent Outlier Exposure for Anomaly Detection with Contaminated Data. ICML (2022)
Yibo Yang, Stephan Mandt
Towards Empirical Sandwich Bounds on the Rate-Distortion Function. ICLR (2022)
Anji Liu, Stephan Mandt, Guy Van den Broeck
Lossless Compression with Probabilistic Circuits. ICLR (2022)
Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth
Probabilistic Querying of Continuous-Time Event Sequences. CoRR (2022)
Joseph Marino, Lei Chen, Jiawei He, Stephan Mandt
Improving sequential latent variable models with autoregressive flows. Mach. Learn. 111 (4) (2022)
Yibo Yang, Stephan Mandt, Lucas Theis
An Introduction to Neural Data Compression. CoRR (2022)
Antonios Alexos, Alex J. Boyd, Stephan Mandt
Structured Stochastic Gradient MCMC. ICML (2022)
Tim Schneider, Chen Qiu, Marius Kloft, Decky Aspandi-Latif, Steffen Staab, Stephan Mandt, Maja Rudolph
Detecting Anomalies within Time Series using Local Neural Transformations. CoRR (2022)
Ruihan Yang, Prakhar Srivastava, Stephan Mandt
Diffusion Probabilistic Modeling for Video Generation. CoRR (2022)
Alex Boyd, Sam Showalter, Stephan Mandt, Padhraic Smyth
Predictive Querying for Autoregressive Neural Sequence Models. CoRR (2022)
Ruihan Yang, Stephan Mandt
Lossy Image Compression with Conditional Diffusion Models. CoRR (2022)
Alex Boyd, Samuel Showalter, Stephan Mandt, Padhraic Smyth
Predictive Querying for Autoregressive Neural Sequence Models. NeurIPS (2022)
Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt
Supervised Compression for Resource-Constrained Edge Computing Systems. WACV (2022)
Anji Liu, Stephan Mandt, Guy Van den Broeck
Lossless Compression with Probabilistic Circuits. CoRR (2021)
Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt
Insights from Generative Modeling for Neural Video Compression. CoRR (2021)
Yibo Yang, Stephan Mandt
Towards Empirical Sandwich Bounds on the Rate-Distortion Function. CoRR (2021)
Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt
Supervised Compression for Resource-constrained Edge Computing Systems. CoRR (2021)
Chen Qiu, Stephan Mandt, Maja Rudolph
History Marginalization Improves Forecasting in Variational Recurrent Neural Networks. Entropy 23 (12) (2021)
Antonios Alexos, Alex Boyd, Stephan Mandt
Structured Stochastic Gradient MCMC. CoRR (2021)
Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning. NeurIPS (2021)
Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph
Neural Transformation Learning for Deep Anomaly Detection Beyond Images. ICML (2021)
Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt
Hierarchical Autoregressive Modeling for Neural Video Compression. ICLR (2021)
Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch
Scalable Gaussian Process Variational Autoencoders. AISTATS (2021)
Harshini Mangipudi, Griffin Mooers, Mike Pritchard, Tom Beucler, Stephan Mandt
Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs. CoRR (2021)
Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph
Neural Transformation Learning for Deep Anomaly Detection Beyond Images. CoRR (2021)
Metod Jazbec, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch
Scalable Gaussian Process Variational Autoencoders. CoRR (2020)
Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth
User-Dependent Neural Sequence Models for Continuous-Time Event Data. NeurIPS (2020)
Griffin Mooers, Jens Tuyls, Stephan Mandt, Michael S. Pritchard, Tom Beucler
Generative Modeling for Atmospheric Convection. CoRR (2020)
Robert Bamler, Stephan Mandt
Extreme Classification via Adversarial Softmax Approximation. CoRR (2020)
Griffin Mooers, Jens Tuyls, Stephan Mandt, Mike Pritchard, Tom G. Beucler
Generative Modeling of Atmospheric Convection. CI (2020)
Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt
GP-VAE: Deep Probabilistic Time Series Imputation. AISTATS (2020)
Linh Tran, Bastiaan S. Veeling, Kevin Roth, Jakub Swiatkowski, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Sebastian Nowozin, Rodolphe Jenatton
Hydra: Preserving Ensemble Diversity for Model Distillation. CoRR (2020)
Robert Bamler, Stephan Mandt
Extreme Classification via Adversarial Softmax Approximation. ICLR (2020)
Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. CoRR (2020)
Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt
Variational Beam Search for Online Learning with Distribution Shifts. CoRR (2020)
Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth
User-Dependent Neural Sequence Models for Continuous-Time Event Data. CoRR (2020)
Joseph Marino, Lei Chen, Jiawei He, Stephan Mandt
Improving Sequential Latent Variable Models with Autoregressive Flows. CoRR (2020)
Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. ICML (2020)
Yibo Yang, Robert Bamler, Stephan Mandt
Variational Bayesian Quantization. ICML (2020)
Yibo Yang, Robert Bamler, Stephan Mandt
Improving Inference for Neural Image Compression. NeurIPS (2020)
Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt
Hierarchical Autoregressive Modeling for Neural Video Compression. CoRR (2020)
Chen Qiu, Stephan Mandt, Maja Rudolph
Variational Dynamic Mixtures. CoRR (2020)
Yibo Yang, Robert Bamler, Stephan Mandt
Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding. CoRR (2020)
Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
How Good is the Bayes Posterior in Deep Neural Networks Really? ICML (2020)
Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
How Good is the Bayes Posterior in Deep Neural Networks Really? CoRR (2020)
Yibo Yang, Robert Bamler, Stephan Mandt
Improving Inference for Neural Image Compression. CoRR (2020)
Florian Schmidt, Stephan Mandt, Thomas Hofmann
Autoregressive Text Generation Beyond Feedback Loops. CoRR (2019)
Robert Bamler, Stephan Mandt
A Quantum Field Theory of Representation Learning. CoRR (2019)
Robert Bamler, Farnood Salehi, Stephan Mandt
Augmenting and Tuning Knowledge Graph Embeddings. UAI (2019)
Cheng Zhang, Judith Bütepage, Hedvig Kjellström, Stephan Mandt
Advances in Variational Inference. IEEE Trans. Pattern Anal. Mach. Intell. 41 (8) (2019)
Vincent Fortuin, Gunnar Rätsch, Stephan Mandt
Multivariate Time Series Imputation with Variational Autoencoders. CoRR (2019)
Robert Bamler, Farnood Salehi, Stephan Mandt
Augmenting and Tuning Knowledge Graph Embeddings. CoRR (2019)
Joseph Marino, Lei Chen, Jiawei He, Stephan Mandt
Improving Sequential Latent Variable Models with Autoregressive Flows. AABI (2019)
Florian Schmidt, Stephan Mandt, Thomas Hofmann
Autoregressive Text Generation Beyond Feedback Loops. EMNLP/IJCNLP (1) (2019)
Salvator Lombardo, Jun Han, Christopher Schroers, Stephan Mandt
Deep Generative Video Compression. NeurIPS (2019)
Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
Tightening Bounds for Variational Inference by Revisiting Perturbation Theory. CoRR (2019)
Majed El Helou, Stephan Mandt, Andreas Krause, Paul A. Beardsley
Mobile Robotic Painting of Texture. ICRA (2019)
Cheng Zhang, Cengiz Öztireli, Stephan Mandt, Giampiero Salvi
Active Mini-Batch Sampling Using Repulsive Point Processes. AAAI (2019)
Alexander Buchholz, Florian Wenzel, Stephan Mandt
Quasi-Monte Carlo Variational Inference. ICML (2018)
Cheng Zhang, Cengiz Öztireli, Stephan Mandt, Giampiero Salvi
Active Mini-Batch Sampling using Repulsive Point Processes. CoRR (2018)
Robert Bamler, Stephan Mandt
Improving Optimization in Models With Continuous Symmetry Breaking. ICML (2018)
Yingzhen Li, Stephan Mandt
Disentangled Sequential Autoencoder. ICML (2018)
Tianfan Fu, Cheng Zhang, Stephan Mandt
Continuous Word Embedding Fusion via Spectral Decomposition. CoNLL (2018)
Lucas Deecke, Robert A. Vandermeulen, Lukas Ruff, Stephan Mandt, Marius Kloft
Image Anomaly Detection with Generative Adversarial Networks. ECML/PKDD (1) (2018)
Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt
Scalable Generalized Dynamic Topic Models. CoRR (2018)
Joseph Marino, Yisong Yue, Stephan Mandt
Iterative Amortized Inference. ICML (2018)
Joseph Marino, Yisong Yue, Stephan Mandt
Learning to Infer. ICLR (Workshop) (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Stephan Mandt