Smoothed Gradients for Stochastic Variational Inference. - Stephan Mandt; David M. Blei (NIPS, 2014)

Deterministic Annealing for Stochastic Variational Inference. - Farhan Abrol; Stephan Mandt; Rajesh Ranganath; David M. Blei (CoRR, 2014)

Sparse Estimation in a Correlated Probit Model. - Stephan Mandt; Florian Wenzel; Shinichi Nakajima; John P. Cunningham; Christoph Lippert; Marius Kloft (CoRR, 2015)

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

A Variational Analysis of Stochastic Gradient Algorithms. - Stephan Mandt; Matthew D. Hoffman; David M. Blei (ICML, 2016)

Exponential Family Embeddings. - Maja Rudolph; Francisco J. R. Ruiz; Stephan Mandt; David M. Blei (NIPS, 2016)

Huber-Norm Regularization for Linear Prediction Models. - Oleksandr Zadorozhnyi; Gunthard Benecke; Stephan Mandt; Tobias Scheffer; Marius Kloft (ECML/PKDD (1), 2016)

Separating Sparse Signals from Correlated Noise in Binary Classification. - Stephan Mandt; Florian Wenzel; Shinichi Nakajima; Christoph Lippert; Marius Kloft (CFA@UAI, 2016)

Factorized Variational Autoencoders for Modeling Audience Reactions to Movies. - Zhiwei Deng; Rajitha Navarathna; Peter Carr 001; Stephan Mandt; Yisong Yue; Iain A. Matthews; Greg Mori (CVPR, 2017)

Dynamic Word Embeddings. - Robert Bamler; Stephan Mandt (ICML, 2017)

Perturbative Black Box Variational Inference. - Robert Bamler; Cheng Zhang 005; Manfred Opper; Stephan Mandt (NIPS, 2017)

Balanced Mini-batch Sampling for SGD Using Determinantal Point Processes. - Cheng Zhang 005; Hedvig Kjellström; Stephan Mandt (UAI, 2017)

Dynamic Word Embeddings via Skip-Gram Filtering. - Robert Bamler; Stephan Mandt (CoRR, 2017)

Structured Black Box Variational Inference for Latent Time Series Models. - Robert Bamler; Stephan Mandt (CoRR, 2017)

Stochastic Learning on Imbalanced Data: Determinantal Point Processes for Mini-batch Diversification. - Cheng Zhang 005; Hedvig Kjellström; Stephan Mandt (CoRR, 2017)

Bayesian Paragraph Vectors. - Geng Ji 001; Robert Bamler; Erik B. Sudderth; Stephan Mandt (CoRR, 2017)

Stochastic Gradient Descent as Approximate Bayesian Inference. - Stephan Mandt; Matthew D. Hoffman; David M. Blei (J. Mach. Learn. Res., 2017)

Sparse probit linear mixed model. - Stephan Mandt; Florian Wenzel; Shinichi Nakajima; John P. Cunningham; Christoph Lippert; Marius Kloft (Mach. Learn., 2017)

Scalable Generalized Dynamic Topic Models. - Patrick Jähnichen; Florian Wenzel; Marius Kloft; Stephan Mandt (AISTATS, 2018)

Continuous Word Embedding Fusion via Spectral Decomposition. - Tianfan Fu; Cheng Zhang 005; Stephan Mandt (CoNLL, 2018)

Learning to Infer. - Joseph Marino; Yisong Yue; Stephan Mandt (ICLR (Workshop), 2018)

Improving Optimization in Models With Continuous Symmetry Breaking. - Robert Bamler; Stephan Mandt (ICML, 2018)

Quasi-Monte Carlo Variational Inference. - Alexander Buchholz; Florian Wenzel; Stephan Mandt (ICML, 2018)

Disentangled Sequential Autoencoder. - Yingzhen Li; Stephan Mandt (ICML, 2018)

Iterative Amortized Inference. - Joseph Marino; Yisong Yue; Stephan Mandt (ICML, 2018)

Image Anomaly Detection with Generative Adversarial Networks. - Lucas Deecke; Robert A. Vandermeulen; Lukas Ruff; Stephan Mandt; Marius Kloft (ECML/PKDD (1), 2018)

A Deep Generative Model for Disentangled Representations of Sequential Data. - Yingzhen Li; Stephan Mandt (CoRR, 2018)

Deep Probabilistic Video Compression. - Jun Han; Salvator Lombardo; Christopher Schroers; Stephan Mandt (CoRR, 2018)

Active Mini-Batch Sampling Using Repulsive Point Processes. - Cheng Zhang 005; Cengiz Öztireli; Stephan Mandt; Giampiero Salvi (AAAI, 2019)

Autoregressive Text Generation Beyond Feedback Loops. - Florian Schmidt 005; Stephan Mandt; Thomas Hofmann 001 (EMNLP/IJCNLP (1), 2019)

Mobile Robotic Painting of Texture. - Majed El Helou; Stephan Mandt; Andreas Krause 001; Paul A. Beardsley (ICRA, 2019)

Deep Generative Video Compression. - Salvator Lombardo; Jun Han; Christopher Schroers; Stephan Mandt (NeurIPS, 2019)

Augmenting and Tuning Knowledge Graph Embeddings. - Robert Bamler; Farnood Salehi; Stephan Mandt (UAI, 2019)

A Quantum Field Theory of Representation Learning. - Robert Bamler; Stephan Mandt (CoRR, 2019)

Multivariate Time Series Imputation with Variational Autoencoders. - Vincent Fortuin; Gunnar Rätsch; Stephan Mandt (CoRR, 2019)

Tightening Bounds for Variational Inference by Revisiting Perturbation Theory. - Robert Bamler; Cheng Zhang 005; Manfred Opper; Stephan Mandt (CoRR, 2019)

Advances in Variational Inference. - Cheng Zhang 005; Judith Bütepage; Hedvig Kjellström; Stephan Mandt (IEEE Trans. Pattern Anal. Mach. Intell., 2019)

GP-VAE: Deep Probabilistic Time Series Imputation. - Vincent Fortuin; Dmitry Baranchuk; Gunnar Rätsch; Stephan Mandt (AISTATS, 2020)

Generative Modeling of Atmospheric Convection. - Griffin Mooers; Jens Tuyls; Stephan Mandt; Mike Pritchard; Tom G. Beucler (CI, 2020)

Extreme Classification via Adversarial Softmax Approximation. - Robert Bamler; Stephan Mandt (ICLR, 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 (ICML, 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 (ICML, 2020)

Variational Bayesian Quantization. - Yibo Yang; Robert Bamler; Stephan Mandt (ICML, 2020)

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

Improving Inference for Neural Image Compression. - Yibo Yang; Robert Bamler; Stephan Mandt (NeurIPS, 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)

Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding. - Yibo Yang; Robert Bamler; Stephan Mandt (CoRR, 2020)

Generative Modeling for Atmospheric Convection. - Griffin Mooers; Jens Tuyls; Stephan Mandt; Michael S. Pritchard; Tom Beucler (CoRR, 2020)

Variational Dynamic Mixtures. - Chen Qiu 001; Stephan Mandt; Maja Rudolph (CoRR, 2020)

Scalable Gaussian Process Variational Autoencoders. - Metod Jazbec; Vincent Fortuin; Michael Pearce; Stephan Mandt; Gunnar Rätsch (CoRR, 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; Matthew Ashman; Vincent Fortuin; Michael Pearce; Stephan Mandt; Gunnar Rätsch (AISTATS, 2021)

Hierarchical Autoregressive Modeling for Neural Video Compression. - Ruihan Yang; Yibo Yang; Joseph Marino; Stephan Mandt (ICLR, 2021)

Neural Transformation Learning for Deep Anomaly Detection Beyond Images. - Chen Qiu 001; Timo Pfrommer; Marius Kloft; Stephan Mandt; Maja Rudolph (ICML, 2021)

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

Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs. - Harshini Mangipudi; Griffin Mooers; Mike Pritchard; Tom Beucler; Stephan Mandt (CoRR, 2021)

History Marginalization Improves Forecasting in Variational Recurrent Neural Networks. - Chen Qiu 001; Stephan Mandt; Maja Rudolph (Entropy, 2021)

Lossless Compression with Probabilistic Circuits. - Anji Liu; Stephan Mandt; Guy Van den Broeck (ICLR, 2022)

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

Structured Stochastic Gradient MCMC. - Antonios Alexos; Alex J. Boyd; Stephan Mandt (ICML, 2022)

Latent Outlier Exposure for Anomaly Detection with Contaminated Data. - Chen Qiu 001; Aodong Li; Marius Kloft; Maja Rudolph; Stephan Mandt (ICML, 2022)

Raising the Bar in Graph-level Anomaly Detection. - Chen Qiu 001; Marius Kloft; Stephan Mandt; Maja Rudolph (IJCAI, 2022)

Predictive Querying for Autoregressive Neural Sequence Models. - Alex Boyd; Samuel Showalter; Stephan Mandt; Padhraic Smyth (NeurIPS, 2022)

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

Detecting Anomalies within Time Series using Local Neural Transformations. - Tim Schneider; Chen Qiu 001; Marius Kloft; Decky Aspandi-Latif; Steffen Staab; Stephan Mandt; Maja Rudolph (CoRR, 2022)

SC2: Supervised Compression for Split Computing. - Yoshitomo Matsubara; Ruihan Yang; Marco Levorato; Stephan Mandt (CoRR, 2022)

Improving sequential latent variable models with autoregressive flows. - Joseph Marino; Lei Chen 023; Jiawei He 001; Stephan Mandt (Mach. Learn., 2022)

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

A Complete Recipe for Diffusion Generative Models. - Kushagra Pandey; Stephan Mandt (ICCV, 2023)

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

Deep Anomaly Detection under Labeling Budget Constraints. - Aodong Li; Chen Qiu 001; Marius Kloft; Padhraic Smyth; Stephan Mandt; Maja Rudolph (ICML, 2023)

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

Zero-Shot Anomaly Detection via Batch Normalization. - Aodong Li; Chen Qiu 001; Marius Kloft; Padhraic Smyth; Maja Rudolph; Stephan Mandt (NeurIPS, 2023)

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

Lossy Image Compression with Conditional Diffusion Models. - Ruihan Yang; Stephan Mandt (NeurIPS, 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; et al. (NeurIPS, 2023)

Inference for mark-censored temporal point processes. - Alex Boyd; Yuxin Chang; Stephan Mandt; Padhraic Smyth (UAI, 2023)

Zero-Shot Anomaly Detection without Foundation Models. - Aodong Li; Chen Qiu 001; Marius Kloft; Padhraic Smyth; Maja Rudolph; Stephan Mandt (CoRR, 2023)

Generative Diffusions in Augmented Spaces: A Complete Recipe. - Kushagra Pandey; 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; et al. (CoRR, 2023)

Asymmetrically-powered Neural Image Compression with Shallow Decoders. - Yibo Yang; 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; et al. (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)

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)

Probabilistic Precipitation Downscaling with Optical Flow-Guided Diffusion. - Prakhar Srivastava 003; Ruihan Yang; Gavin Kerrigan; Gideon Dresdner; Jeremy McGibbon; Christopher S. Bretherton; Stephan Mandt (CoRR, 2023)

An Introduction to Neural Data Compression. - Yibo Yang; Stephan Mandt; Lucas Theis (Found. Trends Comput. Graph. Vis., 2023)

SC2 Benchmark: Supervised Compression for Split Computing. - Yoshitomo Matsubara; Ruihan Yang; Marco Levorato; Stephan Mandt (Trans. Mach. Learn. Res., 2023)

Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072). - Vincent Fortuin; Yingzhen Li; Kevin Murphy; Stephan Mandt; Laura Manduchi (Dagstuhl Reports, 2023)

Insights From Generative Modeling for Neural Video Compression. - Ruihan Yang; Yibo Yang; Joseph Marino; Stephan Mandt (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Diffusion Probabilistic Modeling for Video Generation. - Ruihan Yang; Prakhar Srivastava 003; Stephan Mandt (Entropy, 2023)

Efficient Integrators for Diffusion Generative Models. - Kushagra Pandey; Maja Rudolph; Stephan Mandt (ICLR, 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 B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (ICML, 2024)

Neural NeRF Compression. - Tuan Pham; Stephan Mandt (ICML, 2024)

Precipitation Downscaling with Spatiotemporal Video Diffusion. - Prakhar Srivastava 003; Ruihan Yang; Gavin Kerrigan; Gideon Dresdner; Jeremy McGibbon; Christopher S. Bretherton; Stephan Mandt (NeurIPS, 2024)

Fast samplers for Inverse Problems in Iterative Refinement models. - Kushagra Pandey; Ruihan Yang; 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 (NeurIPS, 2024)

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

Understanding Pathologies of Deep Heteroskedastic Regression. - Eliot Wong-Toi; Alex Boyd; Vincent Fortuin; Stephan Mandt (UAI, 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; et al. (CoRR, 2024)

Towards Fast Stochastic Sampling in Diffusion Generative Models. - Kushagra Pandey; Maja Rudolph; 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 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; Christoph Lippert; et al. (CoRR, 2024)

Unity by Diversity: Improved Representation Learning in Multimodal VAEs. - Thomas M. Sutter; Yang Meng; Norbert Fortin; Julia E. Vogt; Stephan Mandt (CoRR, 2024)

Preserving Identity with Variational Score for General-purpose 3D Editing. - Duong H. Le 001; Tuan Pham; Aniruddha Kembhavi; Stephan Mandt; Wei-Chiu Ma; Jiasen Lu (CoRR, 2024)

Anomaly Detection of Tabular Data Using LLMs. - Aodong Li; Yunhan Zhao; Chen Qiu 001; Marius Kloft; Padhraic Smyth; Maja Rudolph; Stephan Mandt (CoRR, 2024)

HANNA: Hard-constraint Neural Network for Consistent Activity Coefficient Prediction. - Thomas Specht; Mayank Nagda; Sophie Fellenz; Stephan Mandt; Hans Hasse; Fabian Jirasek (CoRR, 2024)

Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0. - Nicolas Hayer; Thorsten Wendel; Stephan Mandt; Hans Hasse; Fabian Jirasek (CoRR, 2024)

Weakly-Supervised Multimodal Learning on MIMIC-CXR. - Andrea Agostini; Daphné Chopard; Yang Meng; Norbert Fortin; Babak Shahbaba; Stephan Mandt; Thomas M. Sutter; Julia E. Vogt (CoRR, 2024)

One Diffusion to Generate Them All. - Duong H. Le 001; Tuan Pham; Sangho Lee 008; Christopher Clark; Aniruddha Kembhavi; Stephan Mandt; Ranjay Krishna; Jiasen Lu (CoRR, 2024)

One Diffusion to Generate Them All. - Duong H. Le 001; Tuan Pham; Sangho Lee 001; Christopher Clark; Aniruddha Kembhavi; Stephan Mandt; Ranjay Krishna; Jiasen Lu (CVPR, 2025)

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

Progressive Compression with Universally Quantized Diffusion Models. - Yibo Yang; Justus C. Will; Stephan Mandt (ICLR, 2025)

Generative Uncertainty in Diffusion Models. - Metod Jazbec; Eliot Wong-Toi; Guoxuan Xia; Dan Zhang; Eric T. Nalisnick; Stephan Mandt (UAI, 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 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; et al. (Trans. Mach. Learn. Res., 2025)

Variational Control for Guidance in Diffusion Models. - Kushagra Pandey; Farrin Marouf Sofian; Felix Draxler; Theofanis Karaletsos; Stephan Mandt (CoRR, 2025)

Self-Supervised Anomaly Detection With Neural Transformations. - Chen Qiu 001; Marius Kloft; Stephan Mandt; Maja Rudolph (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

Diffusion-Guided Gaussian Splatting for Large-Scale Unconstrained 3D Reconstruction and Novel View Synthesis. - Niluthpol Chowdhury Mithun; Tuan Pham; Qiao Wang; Ben Southall; Kshitij Minhas; Bogdan Matei; Stephan Mandt; Supun Samarasekera; Rakesh Kumar 001 (CoRR, 2025)

JANET: Joint Adaptive predictioN-region Estimation for Time-series. - Eshant English; Eliot Wong-Toi; Matteo Fontana; Stephan Mandt; Padhraic Smyth; Christoph Lippert (Mach. Learn., 2025)