Stephan Mandt

According to our database, Stephan Mandt authored at least 95 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Unity by Diversity: Improved Representation Learning in Multimodal VAEs.

Thomas M. Sutter, Yang Meng, Norbert Fortin, Julia E. Vogt, Stephan Mandt

CoRR, 2024

On the Challenges and Opportunities in Generative AI.

Laura Manduchi, 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 T. 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

CoRR, 2024

Towards Fast Stochastic Sampling in Diffusion Generative Models.

Kushagra Pandey, Maja Rudolph, Stephan Mandt

CoRR, 2024

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.

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

CoRR, 2024

2023

Diffusion Probabilistic Modeling for Video Generation.

Ruihan Yang, Prakhar Srivastava, Stephan Mandt

Entropy, October, 2023

Insights From Generative Modeling for Neural Video Compression.

Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt

IEEE Trans. Pattern Anal. Mach. Intell., August, 2023

Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072).

Vincent Fortuin, Yingzhen Li, Kevin Murphy, Stephan Mandt, Laura Manduchi

Dagstuhl Reports, February, 2023

An Introduction to Neural Data Compression.

Yibo Yang, Stephan Mandt, Lucas Theis

Found. Trends Comput. Graph. Vis., 2023

Probabilistic Precipitation Downscaling with Optical Flow-Guided Diffusion.

Prakhar Srivastava, Ruihan Yang, Gavin Kerrigan, Gideon Dresdner, Jeremy McGibbon, Christopher S. Bretherton, Stephan Mandt

CoRR, 2023

Anytime-Valid Confidence Sequences for Consistent Uncertainty Estimation in Early-Exit Neural Networks.

Metod Jazbec, Patrick Forré, Stephan Mandt, Dan Zhang, Eric T. Nalisnick

CoRR, 2023

Understanding and Visualizing Droplet Distributions in Simulations of Shallow Clouds.

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

CoRR, 2023

Efficient Integrators for Diffusion Generative Models.

Kushagra Pandey, Maja Rudolph, Stephan Mandt

CoRR, 2023

Understanding Pathologies of Deep Heteroskedastic Regression.

Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt

CoRR, 2023

ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators.

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 R. 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.

CoRR, 2023

Asymmetrically-powered Neural Image Compression with Shallow Decoders.

Yibo Yang, Stephan Mandt

CoRR, 2023

Deep Anomaly Detection on Tennessee Eastman Process Data.

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

CoRR, 2023

Generative Diffusions in Augmented Spaces: A Complete Recipe.

Kushagra Pandey, Stephan Mandt

CoRR, 2023

Zero-Shot Anomaly Detection without Foundation Models.

Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt

CoRR, 2023

Inference for mark-censored temporal point processes.

Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth

Proceedings of the Uncertainty in Artificial Intelligence, 2023

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation.

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Lossy Image Compression with Conditional Diffusion Models.

Ruihan Yang, Stephan Mandt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Estimating the Rate-Distortion Function by Wasserstein Gradient Descent.

Yibo Yang, Stephan Eckstein, Marcel Nutz, Stephan Mandt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Zero-Shot Anomaly Detection via Batch Normalization.

Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes.

Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone

Proceedings of the International Conference on Machine Learning, 2023

Deep Anomaly Detection under Labeling Budget Constraints.

Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja Rudolph

Proceedings of the International Conference on Machine Learning, 2023

Computationally-Efficient Neural Image Compression with Shallow Decoders.

Yibo Yang, Stephan Mandt

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

A Complete Recipe for Diffusion Generative Models.

Kushagra Pandey, Stephan Mandt

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Probabilistic Querying of Continuous-Time Event Sequences.

Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Improving sequential latent variable models with autoregressive flows.

Joseph Marino, Lei Chen, Jiawei He, Stephan Mandt

Mach. Learn., 2022

SC2: Supervised Compression for Split Computing.

Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt

CoRR, 2022

Detecting Anomalies within Time Series using Local Neural Transformations.

Tim Schneider, Chen Qiu, Marius Kloft, Decky Aspandi-Latif, Steffen Staab, Stephan Mandt, Maja Rudolph

CoRR, 2022

Supervised Compression for Resource-Constrained Edge Computing Systems.

Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

Predictive Querying for Autoregressive Neural Sequence Models.

Alex Boyd, Samuel Showalter, Stephan Mandt, Padhraic Smyth

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Raising the Bar in Graph-level Anomaly Detection.

Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Latent Outlier Exposure for Anomaly Detection with Contaminated Data.

Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt

Proceedings of the International Conference on Machine Learning, 2022

Structured Stochastic Gradient MCMC.

Antonios Alexos, Alex J. Boyd, Stephan Mandt

Proceedings of the International Conference on Machine Learning, 2022

Towards Empirical Sandwich Bounds on the Rate-Distortion Function.

Yibo Yang, Stephan Mandt

Proceedings of the Tenth International Conference on Learning Representations, 2022

Lossless Compression with Probabilistic Circuits.

Anji Liu, Stephan Mandt, Guy Van den Broeck

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

History Marginalization Improves Forecasting in Variational Recurrent Neural Networks.

Chen Qiu, Stephan Mandt, Maja Rudolph

Entropy, 2021

Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs.

Harshini Mangipudi, Griffin Mooers, Mike Pritchard, Tom Beucler, Stephan Mandt

CoRR, 2021

Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning.

Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Neural Transformation Learning for Deep Anomaly Detection Beyond Images.

Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph

Proceedings of the 38th International Conference on Machine Learning, 2021

Hierarchical Autoregressive Modeling for Neural Video Compression.

Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt

Proceedings of the 9th International Conference on Learning Representations, 2021

Scalable Gaussian Process Variational Autoencoders.

Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Variational Beam Search for Online Learning with Distribution Shifts.

Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt

CoRR, 2020

Scalable Gaussian Process Variational Autoencoders.

Metod Jazbec, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch

CoRR, 2020

Variational Dynamic Mixtures.

Chen Qiu, Stephan Mandt, Maja Rudolph

CoRR, 2020

Generative Modeling for Atmospheric Convection.

Griffin Mooers, Jens Tuyls, Stephan Mandt, Michael S. Pritchard, Tom Beucler

CoRR, 2020

Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding.

Yibo Yang, Robert Bamler, Stephan Mandt

CoRR, 2020

Hydra: Preserving Ensemble Diversity for Model Distillation.

Linh Tran, Bastiaan S. Veeling, Kevin Roth, Jakub Swiatkowski, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Sebastian Nowozin, Rodolphe Jenatton

CoRR, 2020

Improving Inference for Neural Image Compression.

Yibo Yang, Robert Bamler, Stephan Mandt

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

User-Dependent Neural Sequence Models for Continuous-Time Event Data.

Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Variational Bayesian Quantization.

Yibo Yang, Robert Bamler, Stephan Mandt

Proceedings of the 37th International Conference on Machine Learning, 2020

How Good is the Bayes Posterior in Deep Neural Networks Really?

Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

Proceedings of the 37th International Conference on Machine Learning, 2020

The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks.

Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

Proceedings of the 37th International Conference on Machine Learning, 2020

Extreme Classification via Adversarial Softmax Approximation.

Robert Bamler, Stephan Mandt

Proceedings of the 8th International Conference on Learning Representations, 2020

Generative Modeling of Atmospheric Convection.

Griffin Mooers, Jens Tuyls, Stephan Mandt, Mike Pritchard, Tom G. Beucler

Proceedings of the CI 2020: 10th International Conference on Climate Informatics, 2020

GP-VAE: Deep Probabilistic Time Series Imputation.

Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Advances in Variational Inference.

Cheng Zhang, Judith Bütepage, Hedvig Kjellström, Stephan Mandt

IEEE Trans. Pattern Anal. Mach. Intell., 2019

Tightening Bounds for Variational Inference by Revisiting Perturbation Theory.

Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt

CoRR, 2019

Multivariate Time Series Imputation with Variational Autoencoders.

Vincent Fortuin, Gunnar Rätsch, Stephan Mandt

CoRR, 2019

A Quantum Field Theory of Representation Learning.

Robert Bamler, Stephan Mandt

CoRR, 2019

Augmenting and Tuning Knowledge Graph Embeddings.

Robert Bamler, Farnood Salehi, Stephan Mandt

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Deep Generative Video Compression.

Salvator Lombardo, Jun Han, Christopher Schroers, Stephan Mandt

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Mobile Robotic Painting of Texture.

Majed El Helou, Stephan Mandt, Andreas Krause, Paul A. Beardsley

Proceedings of the International Conference on Robotics and Automation, 2019

Autoregressive Text Generation Beyond Feedback Loops.

Florian Schmidt, Stephan Mandt, Thomas Hofmann

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

Active Mini-Batch Sampling Using Repulsive Point Processes.

Cheng Zhang, Cengiz Öztireli, Stephan Mandt, Giampiero Salvi

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Deep Probabilistic Video Compression.

Jun Han, Salvator Lombardo, Christopher Schroers, Stephan Mandt

CoRR, 2018

A Deep Generative Model for Disentangled Representations of Sequential Data.

Yingzhen Li, Stephan Mandt

CoRR, 2018

Image Anomaly Detection with Generative Adversarial Networks.

Lucas Deecke, Robert A. Vandermeulen, Lukas Ruff, Stephan Mandt, Marius Kloft

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

Iterative Amortized Inference.

Joseph Marino, Yisong Yue, Stephan Mandt

Proceedings of the 35th International Conference on Machine Learning, 2018

Disentangled Sequential Autoencoder.

Yingzhen Li, Stephan Mandt

Proceedings of the 35th International Conference on Machine Learning, 2018

Quasi-Monte Carlo Variational Inference.

Alexander Buchholz, Florian Wenzel, Stephan Mandt

Proceedings of the 35th International Conference on Machine Learning, 2018

Improving Optimization in Models With Continuous Symmetry Breaking.

Robert Bamler, Stephan Mandt

Proceedings of the 35th International Conference on Machine Learning, 2018

Learning to Infer.

Joseph Marino, Yisong Yue, Stephan Mandt

Proceedings of the 6th International Conference on Learning Representations, 2018

Continuous Word Embedding Fusion via Spectral Decomposition.

Tianfan Fu, Cheng Zhang, Stephan Mandt

Proceedings of the 22nd Conference on Computational Natural Language Learning, 2018

Scalable Generalized Dynamic Topic Models.

Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Sparse probit linear mixed model.

Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft

Mach. Learn., 2017

Stochastic Gradient Descent as Approximate Bayesian Inference.

Stephan Mandt, Matthew D. Hoffman, David M. Blei

J. Mach. Learn. Res., 2017

Bayesian Paragraph Vectors.

Geng Ji, Robert Bamler, Erik B. Sudderth, Stephan Mandt

CoRR, 2017

Stochastic Learning on Imbalanced Data: Determinantal Point Processes for Mini-batch Diversification.

Cheng Zhang, Hedvig Kjellström, Stephan Mandt

CoRR, 2017

Structured Black Box Variational Inference for Latent Time Series Models.

Robert Bamler, Stephan Mandt

CoRR, 2017

Dynamic Word Embeddings via Skip-Gram Filtering.

Robert Bamler, Stephan Mandt

CoRR, 2017

Balanced Mini-batch Sampling for SGD Using Determinantal Point Processes.

Cheng Zhang, Hedvig Kjellström, Stephan Mandt

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Perturbative Black Box Variational Inference.

Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Dynamic Word Embeddings.

Robert Bamler, Stephan Mandt

Proceedings of the 34th International Conference on Machine Learning, 2017

Factorized Variational Autoencoders for Modeling Audience Reactions to Movies.

Zhiwei Deng, Rajitha Navarathna, Peter Carr, Stephan Mandt, Yisong Yue, Iain A. Matthews, Greg Mori

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016

Separating Sparse Signals from Correlated Noise in Binary Classification.

Stephan Mandt, Florian Wenzel, Shinichi Nakajima, Christoph Lippert, Marius Kloft

Proceedings of the UAI 2016 Workshop on Causation: Foundation to Application co-located with the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), 2016

Huber-Norm Regularization for Linear Prediction Models.

Oleksandr Zadorozhnyi, Gunthard Benecke, Stephan Mandt, Tobias Scheffer, Marius Kloft

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

Exponential Family Embeddings.

Maja Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

A Variational Analysis of Stochastic Gradient Algorithms.

Stephan Mandt, Matthew D. Hoffman, David M. Blei

Proceedings of the 33nd International Conference on Machine Learning, 2016

Variational Tempering.

Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David M. Blei

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Sparse Estimation in a Correlated Probit Model.

Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft

CoRR, 2015

2014

Deterministic Annealing for Stochastic Variational Inference.

Farhan Abrol, Stephan Mandt, Rajesh Ranganath, David M. Blei

CoRR, 2014

Smoothed Gradients for Stochastic Variational Inference.

Stephan Mandt, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014