Mandt, Stephan

79 publications

ICLR 2025 AstroCompress: A Benchmark Dataset for Multi-Purpose Compression of Astronomical Data Tuan Truong, Rithwik Sudharsan, Yibo Yang, Peter Xiangyuan Ma, Ruihan Yang, Stephan Mandt, Joshua S. Bloom

JMLR 2025 ClimSim-Online: A Large Multi-Scale Dataset and Framework for Hybrid Physics-ML Climate Emulation Sungduk Yu, Zeyuan Hu, Akshay Subramaniam, Walter Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius J. M. Busecke, Nora Loose, Charles I Stern, Tom Beucler, Bryce Harrop, Helge Heuer, Benjamin R Hillman, Andrea Jenney, Nana Liu, Alistair White, Tian Zheng, Zhiming Kuang, Fiaz Ahmed, Elizabeth Barnes, Noah D. Brenowitz, Christopher Bretherton, Veronika Eyring, Savannah Ferretti, Nicholas Lutsko, Pierre Gentine, Stephan Mandt, J. David Neelin, Rose Yu, Laure Zanna, Nathan M. Urban, Janni Yuval, Ryan Abernathey, Pierre Baldi, Wayne Chuang, Yu Huang, Fernando Iglesias-Suarez, Sanket Jantre, Po-Lun Ma, Sara Shamekh, Guang Zhang, Michael Pritchard

UAI 2025 Generative Uncertainty in Diffusion Models Metod Jazbec, Eliot Wong-Toi, Guoxuan Xia, Dan Zhang, Eric Nalisnick, Stephan Mandt

ICLRW 2025 Generative Uncertainty in Diffusion Models Metod Jazbec, Eliot Wong-Toi, Guoxuan Xia, Dan Zhang, Eric Nalisnick, Stephan Mandt

ICLR 2025 Heavy-Tailed Diffusion Models Kushagra Pandey, Jaideep Pathak, Yilun Xu, Stephan Mandt, Michael Pritchard, Arash Vahdat, Morteza Mardani

MLJ 2025 JANET: Joint Adaptive predictioN-Region Estimation for Time-Series Eshant English, Eliot Wong-Toi, Matteo Fontana, Stephan Mandt, Padhraic Smyth, Christoph Lippert

NeurIPS 2025 NoBOOM: Chemical Process Datasets for Industrial Anomaly Detection Dennis Wagner, Fabian Hartung, Justus Arweiler, Aparna Muraleedharan, Indra Jungjohann, Arjun Nair, Steffen Reithermann, Ralf Schulz, Michael Bortz, Daniel Neider, Heike Leitte, Joachim Pfeffinger, Stephan Mandt, Sophie Fellenz, Torsten Katz, Fabian Jirasek, Jakob Burger, Hans Hasse, Marius Kloft

TMLR 2025 On the Challenges and Opportunities in Generative AI Laura Manduchi, Clara Meister, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin

CVPR 2025 One Diffusion to Generate Them All Duong H. Le, Tuan Pham, Sangho Lee, Christopher Clark, Aniruddha Kembhavi, Stephan Mandt, Ranjay Krishna, Jiasen Lu

ICLR 2025 Progressive Compression with Universally Quantized Diffusion Models Yibo Yang, Justus Will, Stephan Mandt

NeurIPS 2025 Transformers for Mixed-Type Event Sequences Felix Draxler, Yang Meng, Kai Nelson, Lukas Laskowski, Yibo Yang, Theofanis Karaletsos, Stephan Mandt

NeurIPS 2025 UMAMI: Unifying Masked Autoregressive Models and Deterministic Rendering for View Synthesis Tung Le, Tuan Pham, Tung Nguyen, Deying Kong, Xiaohui Xie, Stephan Mandt

ICML 2025 Variational Control for Guidance in Diffusion Models Kushagra Pandey, Farrin Marouf Sofian, Felix Draxler, Theofanis Karaletsos, Stephan Mandt

NeurIPSW 2024 Benchmarking Neural Lossless Compression Algorithms on Multi-Purpose Astronomical Image Data Tuan Truong, Rithwik Sudharsan, Yibo Yang, Peter Xiangyuan Ma, Ruihan Yang, Stephan Mandt, Joshua S. Bloom

UAI 2024 Early-Exit Neural Networks with Nested Prediction Sets Metod Jazbec, Patrick Forré, Stephan Mandt, Dan Zhang, Eric Nalisnick

ICLR 2024 Efficient Integrators for Diffusion Generative Models Kushagra Pandey, Maja Rudolph, Stephan Mandt

NeurIPS 2024 Fast Samplers for Inverse Problems in Iterative Refinement Models Kushagra Pandey, Ruihan Yang, Stephan Mandt

ICML 2024 Neural NeRF Compression Tuan Pham, Stephan Mandt

ICML 2024 Position: Bayesian Deep Learning Is Needed in the Age of Large-Scale AI Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David 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

NeurIPS 2024 Precipitation Downscaling with Spatiotemporal Video Diffusion Prakhar Srivastava, Ruihan Yang, Gavin Kerrigan, Gideon Dresdner, Jeremy McGibbon, Christopher Bretherton, Stephan Mandt

NeurIPSW 2024 Towards Scalable Compression with Universally Quantized Diffusion Models Yibo Yang, Justus Will, Stephan Mandt

UAI 2024 Understanding Pathologies of Deep Heteroskedastic Regression Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt

NeurIPS 2024 Unity by Diversity: Improved Representation Learning for Multimodal VAEs Thomas M. Sutter, Yang Meng, Andrea Agostini, Daphné Chopard, Norbert Fortin, Julia E. Vogt, Babak Shahbaba, Stephan Mandt

ICCV 2023 A Complete Recipe for Diffusion Generative Models Kushagra Pandey, Stephan Mandt

ICMLW 2023 Autoencoding Implicit Neural Representations for Image Compression Tuan Pham, Yibo Yang, Stephan Mandt

NeurIPS 2023 ClimSim: A Large Multi-Scale Dataset for Hybrid Physics-ML Climate Emulation Sungduk Yu, Walter 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 Harrop, Benjamin Hillman, Andrea Jenney, Savannah L. Ferretti, Nana Liu, Animashree Anandkumar, Noah 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 Bader, Pierre Baldi, Elizabeth Barnes, Christopher Bretherton, Peter Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David Randall, Sara Shamekh, Mark Taylor, Nathan Urban, Janni Yuval, Guang Zhang, Mike Pritchard

ICCV 2023 Computationally-Efficient Neural Image Compression with Shallow Decoders Yibo Yang, Stephan Mandt

ICML 2023 Deep Anomaly Detection Under Labeling Budget Constraints Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja Rudolph

NeurIPS 2023 Estimating the Rate-Distortion Function by Wasserstein Gradient Descent Yibo Yang, Stephan Eckstein, Marcel Nutz, Stephan Mandt

ICMLW 2023 Estimating the Rate-Distortion Function by Wasserstein Gradient Descent Yibo Yang, Stephan Eckstein, Marcel Nutz, Stephan Mandt

ICML 2023 Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone

UAI 2023 Inference for Mark-Censored Temporal Point Processes Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth

ICMLW 2023 Lossy Image Compression with Conditional Diffusion Model Ruihan Yang, Stephan Mandt

NeurIPS 2023 Lossy Image Compression with Conditional Diffusion Models Ruihan Yang, Stephan Mandt

AISTATS 2023 Probabilistic Querying of Continuous-Time Event Sequences Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth

TMLR 2023 SC2 Benchmark: Supervised Compression for Split Computing Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt

NeurIPS 2023 Zero-Shot Anomaly Detection via Batch Normalization Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt

MLJ 2022 Improving Sequential Latent Variable Models with Autoregressive Flows Joseph Marino, Lei Chen, Jiawei He, Stephan Mandt

ICML 2022 Latent Outlier Exposure for Anomaly Detection with Contaminated Data Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt

ICLR 2022 Lossless Compression with Probabilistic Circuits Anji Liu, Stephan Mandt, Guy Van den Broeck

NeurIPS 2022 Predictive Querying for Autoregressive Neural Sequence Models Alex Boyd, Samuel Showalter, Stephan Mandt, Padhraic Smyth

IJCAI 2022 Raising the Bar in Graph-Level Anomaly Detection Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph

ICML 2022 Structured Stochastic Gradient MCMC Antonios Alexos, Alex J Boyd, Stephan Mandt

WACV 2022 Supervised Compression for Resource-Constrained Edge Computing Systems Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt

ICLR 2022 Towards Empirical Sandwich Bounds on the Rate-Distortion Function Yibo Yang, Stephan Mandt

AISTATS 2021 Scalable Gaussian Process Variational Autoencoders Metod Jazbec, Matt Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch

NeurIPS 2021 Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt

ICLR 2021 Hierarchical Autoregressive Modeling for Neural Video Compression Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt

ICLRW 2021 Lower Bounding Rate-Distortion from Samples Yibo Yang, Stephan Mandt

ICML 2021 Neural Transformation Learning for Deep Anomaly Detection Beyond Images Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph

ICLRW 2021 Scale Space Flow with Autoregressive Priors Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt

ICLR 2020 Extreme Classification via Adversarial SoftMax Approximation Robert Bamler, Stephan Mandt

AISTATS 2020 GP-VAE: Deep Probabilistic Time Series Imputation Vincent Fortuin, Dmitry Baranchuk, Gunnar Raetsch, Stephan Mandt

ICML 2020 How Good Is the Bayes Posterior in Deep Neural Networks Really? Florian Wenzel, Kevin Roth, Bastiaan Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

NeurIPS 2020 Improving Inference for Neural Image Compression Yibo Yang, Robert Bamler, Stephan Mandt

ICML 2020 The K-Tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks Jakub Swiatkowski, Kevin Roth, Bastiaan Veeling, Linh Tran, Joshua Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

NeurIPS 2020 User-Dependent Neural Sequence Models for Continuous-Time Event Data Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth

ICML 2020 Variational Bayesian Quantization Yibo Yang, Robert Bamler, Stephan Mandt

AAAI 2019 Active Mini-Batch Sampling Using Repulsive Point Processes Cheng Zhang, Cengiz Öztireli, Stephan Mandt, Giampiero Salvi

UAI 2019 Augmenting and Tuning Knowledge Graph Embeddings Robert Bamler, Farnood Salehi, Stephan Mandt

NeurIPS 2019 Deep Generative Video Compression Salvator Lombardo, Jun Han, Christopher Schroers, Stephan Mandt

ICML 2018 Disentangled Sequential Autoencoder Li Yingzhen, Stephan Mandt

ECML-PKDD 2018 Image Anomaly Detection with Generative Adversarial Networks Lucas Deecke, Robert A. Vandermeulen, Lukas Ruff, Stephan Mandt, Marius Kloft

ICML 2018 Improving Optimization for Models with Continuous Symmetry Breaking Robert Bamler, Stephan Mandt

ICML 2018 Iterative Amortized Inference Joe Marino, Yisong Yue, Stephan Mandt

ICML 2018 Quasi-Monte Carlo Variational Inference Alexander Buchholz, Florian Wenzel, Stephan Mandt

AISTATS 2018 Scalable Generalized Dynamic Topic Models Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt

UAI 2017 Balanced Mini-Batch Sampling for SGD Using Determinantal Point Processes Cheng Zhang, Hedvig Kjellström, Stephan Mandt

ICML 2017 Dynamic Word Embeddings Robert Bamler, Stephan Mandt

CVPR 2017 Factorized Variational Autoencoders for Modeling Audience Reactions to Movies Zhiwei Deng, Rajitha Navarathna, Peter Carr, Stephan Mandt, Yisong Yue, Iain Matthews, Greg Mori

NeurIPS 2017 Perturbative Black Box Variational Inference Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt

MLJ 2017 Sparse Probit Linear Mixed Model Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft

JMLR 2017 Stochastic Gradient Descent as Approximate Bayesian Inference Stephan Mandt, Matthew D. Hoffman, David M. Blei

ICML 2016 A Variational Analysis of Stochastic Gradient Algorithms Stephan Mandt, Matthew Hoffman, David Blei

NeurIPS 2016 Exponential Family Embeddings Maja Rudolph, Francisco Ruiz, Stephan Mandt, David Blei

ECML-PKDD 2016 Huber-Norm Regularization for Linear Prediction Models Oleksandr Zadorozhnyi, Gunthard Benecke, Stephan Mandt, Tobias Scheffer, Marius Kloft

UAI 2016 Separating Sparse Signals from Correlated Noise in Binary Classification Stephan Mandt, Florian Wenzel, Shinichi Nakajima, Christoph Lippert, Marius Kloft

AISTATS 2016 Variational Tempering Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David M. Blei

NeurIPS 2014 Smoothed Gradients for Stochastic Variational Inference Stephan Mandt, David Blei