Publications

2026

Control-Augmented Diffusion for Autoregressive Data Assimilation
P. Srivastava, F. Sofian, F. Immorlano, S. Mandt
Advances in Neural Information Processing Systems (NeurIPS), 2026 [BibTeX]
Hierarchical Variational Policies for Reward-Guided Diffusion
K. Pandey, F. Sofian, J. N. Groeneveld, F. Draxler, S. Mandt
Advances in Neural Information Processing Systems (NeurIPS), 2026 [BibTeX]
A Tale of Two Temperatures: Simple, Efficient, and Diverse Sampling from Diffusion Language Models
T. X. Olausson, M. Jazbec, X. Wang, A. Solar-Lezama, C. A. Naesseth, S. Mandt, E. Nalisnick
Conference on Language Modeling (COLM), 2026 [BibTeX]
Calibrated Test-Time Guidance for Bayesian Inference
D. Geyfman, F. Draxler, J. N. Groeneveld, H. Lee, T. Karaletsos, S. Mandt
Proceedings of the International Conference on Machine Learning (ICML), 2026 [BibTeX]
Heavy-tailed Physics-Informed Neural Networks
J. Abijuru, M. Nagda, P. Ostheimer, J. Tauberschmidt, S. J. Vollmer, S. Mandt, M. Kloft, S. Fellenz
Proceedings of the International Conference on Machine Learning (ICML), 2026 [BibTeX]
Skipping the Zeros in Diffusion Models for Sparse Data Generation
P. Ostheimer, M. Nagda, A. Balinskyy, G. V. Rodrigues, J. Radig, C. Herrmann, S. Mandt, M. Kloft, S. Fellenz
Proceedings of the International Conference on Machine Learning (ICML), 2026 [BibTeX]
SetPINNs: Set-based Physics-informed Neural Networks
M. Nagda, P. Ostheimer, T. Specht, F. Rhein, F. Jirasek, S. Mandt, M. Kloft, S. Fellenz
International Conference on Artificial Intelligence and Statistics (AISTATS), 2026 [BibTeX]
Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
D. Wagner, A. Nair, B. J. Franks, J. Arweiler, A. Muraleedharan, I. Jungjohann, F. Hartung, A. Balinskyy, S. Varshneya, M. C. Ahuja, N. H. Syed, M. Nagda, P. Liznerski, S. Reithermann, M. Rudolph, S. J. Vollmer, R. Schulz, T. Katz, S. Mandt, M. Bortz, H. Leitte, D. Neider, J. Burger, F. Jirasek, H. Hasse, S. Fellenz, M. Kloft
International Conference on Artificial Intelligence and Statistics (AISTATS), 2026 [BibTeX]
Parallel Token Prediction for Language Models
F. Draxler, J. Will, F. Sofian, T. Karaletsos, S. Singh, S. Mandt
International Conference on Learning Representations (ICLR), 2026 [BibTeX]
DiffStyleTS: Diffusion Model for Style Transfer in Time Series
M. Nagda, P. Ostheimer, J. Arweiler, I. Jungjohann, J. Werner, D. Wagner, A. Muraleedharan, P. Jafari, J. Schmid, F. Jirasek, J. Burger, M. Bortz, H. Hasse, S. Mandt, M. Kloft, S. Fellenz
Transactions on Machine Learning Research, 2026 [BibTeX]
Advances in Diffusion-Based Generative Compression
Y. Yang, S. Mandt
IEEE BITS the Information Theory Magazine, 2026 [BibTeX]
Thermodynamically consistent machine learning model for excess Gibbs energy
M. Hoffmann, T. Specht, Q. Goettl, J. Burger, S. Mandt, H. Hasse, F. Jirasek
Nature Communications, 2026 [BibTeX]
Structure is Supervision: Multiview Masked Autoencoders for Radiology
S. Laguna, A. Agostini, A. Ryser, S. Ruiperez-Campillo, I. Cannistraci, M. Vandenhirtz, S. Mandt, N. Deperrois, F. Nooralahzadeh, M. Krauthammer, T. M. Sutter, J. E. Vogt
Transactions on Machine Learning Research, 2026 [BibTeX]

2025

Style Transfer for High-Fidelity Time Series Augmentation
M. Nagda, P. Ostheimer, J. Arweiler, I. Jungjohann, J. Werner, D. Wagner, A. Muraleedharan, P. Jafari, J. Schmid, F. Jirasek, J. Burger, M. Bortz, H. Hasse, S. Mandt, M. Kloft, S. Fellenz
Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2025 [BibTeX]
Transformers for Mixed-type Event Sequences
F. Draxler, Y. Meng, K. Nelson, L. Laskowski, Y. Yang, T. Karaletsos, S. Mandt
Advances in Neural Information Processing Systems (NeurIPS), 2025 [BibTeX]
UMAMI: Unifying Masked Autoregressive Models and Deterministic Rendering for View Synthesis
T. Le, T. Pham, T. Nguyen, D. Kong, X. Xie, S. Mandt
Advances in Neural Information Processing Systems (NeurIPS), 2025 [BibTeX]
NoBOOM: Chemical Process Datasets for Industrial Anomaly Detection
D. Wagner, F. Hartung, J. Arweiler, A. Muraleedharan, I. Jungjohann, A. Nair, S. Reithermann, R. Schulz, M. Bortz, D. Neider, H. Leitte, J. Pfeffinger, S. Mandt, S. Fellenz, T. Katz, F. Jirasek, J. Burger, H. Hasse, M. Kloft
Advances in Neural Information Processing Systems (NeurIPS), 2025 [BibTeX]
Variational Control for Guidance in Diffusion Models
K. Pandey, F. Sofian, F. Draxler, T. Karaletsos, S. Mandt
Proceedings of the International Conference on Machine Learning (ICML), 2025 [BibTeX]
Generative Uncertainty in Diffusion Models
M. Jazbec, E. Wong-Toi, G. Xia, D. Zhang, E. Nalisnick, S. Mandt
Proceedings of the International Conference on Uncertainty in Artificial Intelligence (UAI), 2025 [BibTeX]
One Diffusion to Generate Them All
D. Le, T. Pham, S. Lee, C. Clark, A. Kembhavi, S. Mandt, R. Krishna, J. Lu
Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2025 [BibTeX]
Progressive Compression with Universally Quantized Diffusion Models
Y. Yang, J. Will, S. Mandt
International Conference on Learning Representations (ICLR), 2025 [BibTeX]
Heavy-Tailed Diffusion Models
K. Pandey, J. Pathak, Y. Xu, S. Mandt, M. Pritchard, A. Vahdat, M. Mardani
International Conference on Learning Representations (ICLR), 2025 [BibTeX]
AstroCompress: A benchmark dataset for multi-purpose compression of astronomical data
T. Truong, R. Sudharsan, Y. Yang, P. Ma, R. Yang, S. Mandt, J. Bloom
International Conference on Learning Representations (ICLR), 2025 [BibTeX]
ClimSim-Online: A Large Multi-Scale Dataset and Framework for Hybrid Physics-ML Climate Emulation
S. Yu, Z. Hu, A. Subramaniam, W. Hannah, L. Peng, J. Lin, M. A. Bhouri, R. Gupta, B. Lütjens, J. C. Will, G. Behrens, J. J. M. Busecke, N. Loose, C. I. Stern, T. Beucler, B. Harrop, H. Heuer, B. R. Hillman, A. Jenney, N. Liu, A. White, T. Zheng, Z. Kuang, F. Ahmed, E. Barnes, N. D. Brenowitz, C. Bretherton, V. Eyring, S. Ferretti, N. Lutsko, P. Gentine, S. Mandt, J. D. Neelin, R. Yu, L. Zanna, N. M. Urban, J. Yuval, R. Abernathey, P. Baldi, W. Chuang, Y. Huang, F. Iglesias-Suarez, S. Jantre, P.-L. Ma, S. Shamekh, G. Zhang, M. Pritchard
Journal of Machine Learning Research, 2025 [BibTeX]
On the Challenges and Opportunities in Generative AI
L. Manduchi, K. Pandey, C. Meister, R. Bamler, R. Cotterell, S. Däubener, S. Fellenz, A. Fischer, T. Gärtner, M. Kirchler, M. Kloft, Y. Li, C. Lippert, G. de Melo, E. Nalisnick, B. Ommer, R. Ranganath, M. Rudolph, K. Ullrich, G. Van den Broeck, J. E. Vogt, Y. Wang, F. Wenzel, F. Wood, S. Mandt^*, V. Fortuin^* (^*shared last authorship)
Transactions on Machine Learning Research, 2025 [BibTeX]
JANET: Joint Adaptive predictioN-region Estimation for Time-series
E. English, E. Wong-Toi, M. Fontana, S. Mandt, P. Smyth, C. Lippert
Machine Learning, 2025 [BibTeX]
Transferring Climate Change Physical Knowledge
F. Immorlano, V. Eyring, T. de Gouville, D. Elia, S. Mandt, G. Aloisio, P. Gentine
Proceedings of the National Academy of Sciences, 2025 [BibTeX]
Advancing thermodynamic group-contribution methods by machine learning: UNIFAC 2.0
N. Hayer, T. Wendel, S. Mandt, H. Hasse, F. Jirasek
Chemical Engineering Journal, 2025 [BibTeX]
Lossless Compression with Probabilistic Circuits
S. Mandt, A. Liu, G.v.d. Broeck
US Patent 12,456,231, 2025 [BibTeX]
Generative AI and Foundation Models: A Gentle Technical Introduction
G. de Melo, S. Koyejo, S. Mandt
The Oxford Handbook of the Foundations and Regulation of Generative AI, 2025 [BibTeX]

2024

Fast samplers for Inverse Problems in Iterative Refinement models
K. Pandey, R. Yang, S. Mandt
Advances in Neural Information Processing Systems (NeurIPS), 2024 [BibTeX]
Precipitation Downscaling with Spatiotemporal Video Diffusion
P. Srivastava, R. Yang, G. Kerrigan, G. Dresdner, J. McGibbon, C. Bretherton, S. Mandt
Advances in Neural Information Processing Systems (NeurIPS), 2024 [BibTeX]
Unity by Diversity: Improved Representation Learning for Multimodal VAEs
T. Sutter, Y. Meng, A. Agostini, D. Chopard, N. Fortin, J. Vogt, B. Shahbaba, S. Mandt
Advances in Neural Information Processing Systems (NeurIPS), 2024 [BibTeX]
Understanding Pathologies of Deep Heteroskedastic Regression
E. Wong-Toi, A. Boyd, V. Fortuin, S. Mandt
Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2024 [BibTeX]
Early-Exit Neural Networks with Nested Prediction Sets
M. Jazbec, P. Forre, S. Mandt, D. Zhang, E. Nalisnick
Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2024 [BibTeX]
Bayesian Deep Learning is Needed in the Age of Large-Scale AI
T. Papamarkou, M. Skoularidou, K. Palla, L. Aitchison, J. Arbel, D. Dunson, M. Filippone, V. Fortuin, P. Hennig, J. M. Hernández-Lobato, A. Hubin, A. Immer, T. Karaletsos, M. E. Khan, A. Kristiadi, Y. Li, S. Mandt, C. Nemeth, M. A. Osborne, T. G. J. Rudner, D. Rügamer, Y. W. Teh, M. Welling, A. G. Wilson, R. Zhang
Proceedings of the International Conference on Machine Learning (ICML), 2024 [BibTeX]
Neural Nerf Compression
T. Pham, S. Mandt
Proceedings of the International Conference on Machine Learning (ICML), 2024 [BibTeX]
Efficient Integrators for Diffusion Generative Models
K. Pandey, M. Rudolph, S. Mandt
Proceedings of the International Conference on Learning Representations (ICLR), 2024 [BibTeX]
Self-Supervised Anomaly Detection with Neural Transformations
C. Qiu, M. Kloft, S. Mandt, M. Rudolph
Transactions on Pattern Analysis and Machine Intelligence, 2024 [BibTeX]
HANNA: hard-constraint neural network for consistent activity coefficient prediction
T. Specht, M. Nagda, S. Fellenz, S. Mandt, H. Hasse, F. Jirasek
Chemical Science, 2024 [BibTeX]
Understanding Precipitation Changes through Unsupervised Machine Learning
G. Mooers, T. Beucler, M. Pritchard, S. Mandt
Environmental Data Science, 2024 [BibTeX]
Asymmetrically-powered Neural Image Compression with Shallow Decoders
Y. Yang, S. Mandt
US Patent Application, 2024 [BibTeX]

2023

Estimating the Rate-Distortion Function by Wasserstein Gradient Descent
Y. Yang, S. Eckstein, M. Nutz, S. Mandt
Advances in Neural Information Processing Systems (NeurIPS), 2023 [BibTeX]
Lossy Image Compression with Conditional Diffusion Models
R. Yang, S. Mandt
Advances in Neural Information Processing Systems (NeurIPS), 2023 [BibTeX]
Zero-Shot Batch-Level Anomaly Detection
A. Li, C. Qiu, M. Kloft, P. Smyth, M. Rudolph, S. Mandt
Advances in Neural Information Processing Systems (NeurIPS), 2023 [BibTeX]
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators
S. Yu, W. Hannah, L. Peng, J. Lin, M. A. Bhouri, R. Gupta, B. Lütjens, J. C. Will, G. Behrens, J. Busecke, N. Loose, C. Stern, T. Beucler, B. Harrop, B. Hillman, A. Jenney, S. L. Ferretti, N. Liu, A. Anandkumar, N. Brenowitz, V. Eyring, N. Geneva, P. Gentine, S. Mandt, J. Pathak, A. Subramaniam, C. Vondrick, R. Yu, L. Zanna, T. Zheng, R. Abernathey, F. Ahmed, D. Bader, P. Baldi, E. Barnes, C. Bretherton, P. Caldwell, W. Chuang, Y. Han, Y. Huang, F. Iglesias-Suarez, S. Jantre, K. Kashinath, M. Khairoutdinov, T. Kurth, N. Lutsko, P.-L. Ma, G. Mooers, J. D. Neelin, D. Randall, S. Shamekh, M. Taylor, N. Urban, J. Yuval, G. Zhang, M. Pritchard
Advances in Neural Information Processing Systems (NeurIPS), 2023 [BibTeX]
Computationally-Efficient Neural Image Compression with Shallow Decoders
Y. Yang, S. Mandt
Proceedings of the International Conference on Computer Vision (ICCV), 2023 [BibTeX]
A Complete Recipe for Diffusion Generative Models
K. Pandey, S. Mandt
Proceedings of the International Conference on Computer Vision (ICCV), 2023 [BibTeX]
Inference for Mark-Censored Temporal Point Processes
A. Boyd, Y. Chang, S. Mandt, P. Smyth
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI), 2023 [BibTeX]
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
B. Tran, B. Shahbaba, S. Mandt, M. Filippone
Proceedings of the 40th International Conference on Machine Learning (ICML), 2023 [BibTeX]
Deep Anomaly Detection under Labeling Budget Constraints
A. Li, C. Qiu, P. Smyth, M. Kloft, S. Mandt^*, M. Rudolph^* (^*shared last authorship)
Proceedings of the 40th International Conference on Machine Learning (ICML), 2023 [BibTeX]
Probabilistic Querying of Continuous-Time Event Sequences
A. Boyd, Y. Chang, S. Mandt, P. Smyth
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS), 2023 [BibTeX]
Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning
G. Mooers, M. Pritchard, T. Beucler, P. Srivastava, H. Mangipudi, L. Peng, P. Gentine, S. Mandt
Scientific Reports, 2023 [BibTeX]
Diffusion Probabilistic Modeling for Video Generation
R. Yang, P. Srivastava, S. Mandt
Entropy, 2023 [BibTeX]
SC2 Benchmark: Supervised Compression for Split Computing
Y. Matsubara, R. Yang, M. Levorato, S. Mandt
Transactions on Machine Learning Research, 2023 [BibTeX]
Insights from Generative Modeling for Neural Video Compression
R. Yang, Y. Yang, J. Marino, S. Mandt
Transactions on Pattern Analysis and Machine Intelligence, 2023 [BibTeX]
Machine Learning Based Video Compression
S. Mandt, C. Schroers, J. Han, S. Lombardo
US Patent 11,544,606, 2023 [BibTeX]
An Introduction to Neural Data Compression
Y. Yang, S. Mandt, L. Theis
Foundations and Trends in Computer Graphics and Vision, 2023 [BibTeX]

2022

Predictive Querying for Autoregressive Neural Sequence Models
A. Boyd, S. Showalter, S. Mandt, P. Smyth
Advances in Neural Information Processing Systems (NeurIPS) 35, 2022 [BibTeX]
Structured Stochastic Gradient MCMC
A. Alexos, A. Boyd, S. Mandt
Proceedings of the 39th International Conference on Machine Learning (ICML), 2022 [BibTeX]
Latent Outlier Exposure for Anomaly Detection with Contaminated Data
C. Qiu, A. Li, M. Kloft, M. Rudolph, S. Mandt
Proceedings of the 39th International Conference on Machine Learning (ICML), 2022 [BibTeX]
Raising the Bar in Graph-level Anomaly Detection
C. Qiu, M. Kloft, S. Mandt, M. Rudolph
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI), 2022 [BibTeX]
Towards Empirical Sandwich Bounds on the Rate-Distortion Function
Y. Yang, S. Mandt
International Conference on Learning Representations (ICLR), 2022 [BibTeX]
Lossless Compression with Probabilistic Circuits
A. Liu, S. Mandt, G. v.d.Broeck
International Conference on Learning Representations (ICLR), 2022 [BibTeX]
Supervised Compression for Resource-Constrained Edge Computing Systems
Y. Matsubara, R. Yang, M. Levorato, S. Mandt
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022 [BibTeX]
Learning to Simulate High Energy Particle Collisions from Unlabeled Data
J. Howard, S. Mandt, D. Whiteson, Y. Yang
Scientific Reports, 2022 [BibTeX]
Making Thermodynamic Models of Mixtures Predictive by Machine Learning: Matrix Completion of Pair Interactions
F. Jirasek, R. Bamler, S. Fellenz, M. Bortz, M. Kloft, S. Mandt, H. Hasse
Chemical Science, 2022 [BibTeX]
Systems and Methods for Image Compression at Multiple, Different Bitrates
C. Schroers, E. Doggett, S. Mandt, J. Mcphillen, S. Labrozzi, R. Weber, M. Bamert
US Patent 11,335,034, 2022 [BibTeX]
Factorized Variational Autoencoders
P. Carr, Z. Deng, R. Navarathna, Y. Yue, S. Mandt
US Patent 11,403,531, 2022 [BibTeX]

2021

Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning
A. Li, A. Boyd, P. Smyth, S. Mandt
Advances in Neural Information Processing Systems 34 (NeurIPS), 2021 [BibTeX]
Neural Transformation Learning for Deep Anomaly Detection Beyond Images
C. Qiu, T. Pfrommer, M. Kloft, S. Mandt, M. Rudolph
Proceedings of the 38th International Conference on Machine Learning, 2021 [BibTeX]
Hierarchical Autoregressive Modeling for Neural Video Compression
R. Yang, Y. Yang, J. Marino, S. Mandt
International Conference on Learning Representations (ICLR), 2021 [BibTeX]
Scalable Gaussian Process Variational Autoencoders
M. Jazbec, M. Ashman, V. Fortuin, M. Pearce, S. Mandt, G. Rätsch
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, 2021 [BibTeX]
History Marginalization Improves Forecasting in Variational Recurrent Neural Networks
C. Qiu, S. Mandt, M. Rudolph
Entropy, 2021 [BibTeX]
Improving Sequential Latent Variable Models with Autoregressive Flows
J. Marino, J. He, L. Chen, S. Mandt
Machine Learning, 2021 [BibTeX]
Pixel Error Detection System
E. Doggett, A. Wolak, P.D. Tsatsoulis, N. McCarthy, S. Mandt
US Patent 11,080,835, 2021, 2021 [BibTeX]
Automated Content Evaluation Using a Predictive Model
S. Lombardo, C. Segalin, L. Chen, R. Navarathna, S. Mandt
US Patent 10,997,476, 2021 [BibTeX]
Efficient Encoding and Decoding Sequences using Variational Autoencoders
S. Mandt, Y. Li
US Patents 11,238,341 and 11,205,121, 2021, 2021 [BibTeX]
Dynamic Word Embeddings
R. Bamler, S. Mandt
US Patent 11,068,658, 2021 [BibTeX]

2020

Improving Inference for Neural Image Compression
Y. Yang, R. Bamler, S. Mandt
Advances in Neural Information Processing Systems 33, 2020 [BibTeX]
User-Dependent Neural Sequence Models for Continuous-Time Event Data
A. Boyd, R. Bamler, S. Mandt, P. Smyth
Advances in Neural Information Processing Systems 33, 2020 [BibTeX]
Variational Bayesian Quantization
Y. Yang, R. Bamler, S. Mandt
Proceedings of the 37th International Conference on Machine Learning, 2020 [BibTeX]
How Good is the Bayes Posterior in Deep Neural Networks Really?
F. Wenzel, K. Roth, B. Veeling, J. Swiatkowski, L. Tran, S. Mandt, J. Snoek, T. Salimans, R. Jenatton, S. Nowozin
Proceedings of the 37th International Conference on Machine Learning, 2020 [BibTeX]
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
J. Swiatkowski, K. Roth, B. Veeling, L. Tran, J. Dillon, S. Mandt, J. Snoek, T. Salimans, R. Jenatton, S. Nowozin
Proceedings of the 37th International Conference on Machine Learning, 2020 [BibTeX]
GP-VAE: Deep Probabilistic Time Series Imputation
V. Fortuin, D. Baranchuk, G. Rätsch, S. Mandt
Proceedings of the Twenty-Third International Conference on Artificial Intelligence and Statistics, 2020 [BibTeX]
Extreme Classification via Adversarial Softmax Approximation
R. Bamler, S. Mandt
International Conference on Learning Representations (ICLR), 2020 [BibTeX]
Augmenting and Tuning Knowledge Graph Embeddings
R. Bamler, F. Salehi, S. Mandt
Proceedings of the 35th International Conference on Uncertainty in Artificial Intelligence, 2020 [BibTeX]
Hybridizing Physical and Data-Driven Prediction Methods for Physicochemical Properties
F. Jirasek, R. Bamler, S. Mandt
Chemical Communications, 2020 [BibTeX]
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
F. Jirasek, R. Alves, J. Damay, R. Vandermeulen, R. Bamler, M. Bortz, S. Mandt, M. Kloft, H. Hasse
The Journal of Physical Chemistry Letters, 2020 [BibTeX]

2019

Deep Generative Video Compression
J. Han, S. Lombardo, C. Schroers, S. Mandt
Advances in Neural Information Processing Systems 32, 2019 [BibTeX]
Autoregressive Text Generation Beyond Feedback Loops
F. Schmidt, S. Mandt, T. Hofmann
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019 [BibTeX]
Mobile Robotic Painting of Texture
M. Helou, S. Mandt, A. Krause, P. Beardsley
Proceedings of the IEEE International Conference on Robotics and Automation, 2019 [BibTeX]
Active Mini-Batch Sampling using Repulsive Point Processes
C. Zhang, C. Öztireli, S. Mandt, G. Salvi
Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 2019 [BibTeX]
Tightening Bounds for Variational Inference by Revisiting Perturbation Theory
R. Bamler, C. Zhang, M. Opper, S. Mandt
Journal of Statistical Mechanics: Theory and Experiment, 2019 [BibTeX]

2018

Quasi Monte Carlo Variational Inference
A. Buchholz, F. Wenzel, S. Mandt
Proceedings of the 35th International Conference on Machine Learning, 2018 [BibTeX]
Iterative Amortized Inference
J. Marino, Y. Yue, S. Mandt
Proceedings of the 35th International Conference on Machine Learning, 2018 [BibTeX]
Improving Optimization for Models with Continuous Symmetry Breaking
R. Bamler, S. Mandt
Proceedings of the 35th International Conference on Machine Learning, 2018 [BibTeX]
Disentangled Sequential Autoencoder
Y. Li, S. Mandt
Proceedings of the 35th International Conference on Machine Learning, 2018 [BibTeX]
Continuous Word Embedding Fusion via Spectral Decomposition
T. Fu, C. Zhang, S. Mandt
Proceedings of the 22nd Conference on Computational Natural Language Learning, 2018 [BibTeX]
Image Anomaly Detection with Generative Adversarial Networks
L. Deecke, R. Vandermeulen, L. Ruff, S. Mandt, M. Kloft
Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2018 [BibTeX]
Scalable Generalized Dynamic Topic Models
P. Jähnichen, F. Wenzel, M. Kloft, S. Mandt
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, 2018 [BibTeX]
Advances in Variational Inference
C. Zhang, J. Bütepage, H. Kjellström, S. Mandt
Transactions on Pattern Analysis and Machine Intelligence, 2018 [BibTeX]

2017

Perturbative Black Box Variational Inference
R. Bamler, C. Zhang, M. Opper, S. Mandt
Advances in Neural Information Processing Systems 30, 2017 [BibTeX]
Dynamic Word Embeddings
R. Bamler, S. Mandt
Proceedings of the 34th International Conference on Machine Learning, 2017 [BibTeX]
Determinantal Point Processes for Mini-Batch Diversification
C. Zhang, H. Kjellström, S. Mandt
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), 2017 [BibTeX]
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 Recognition, 2017 [BibTeX]
Stochastic Gradient Descent as Approximate Bayesian Inference
S. Mandt, H. Hoffman, D. Blei
Journal of Machine Learning Research, 2017 [BibTeX]
Sparse Probit Linear Mixed Model
S. Mandt, F. Wenzel, S. Nakajima, J. Cunningham, C. Lippert, M. Kloft
Machine Learning, 2017 [BibTeX]

2016

Exponential Family Embeddings
M. Rudolph, F. Ruiz, S. Mandt, D. Blei
Advances in Neural Information Processing Systems 29, 2016 [BibTeX]
A Variational Analysis of Stochastic Gradient Algorithms
S. Mandt, M. Hoffman, D. Blei
Proceedings of The 33rd International Conference on Machine Learning, 2016 [BibTeX]
Variational Tempering
S. Mandt, J. McInerney, F. Abrol, R. Ranganath, D. Blei
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016 [BibTeX]
Huber-Norm Regularization for Linear Prediction Models
O. Zadorozhnyi, G. Benecke, S. Mandt, T. Scheffer, M. Kloft
Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2016 [BibTeX]

2015

Stochastic Differential Equations for Quantum Dynamics of Spin-Boson Networks
S. Mandt, D. Sadri, A. Houck, H. Tureci
New Journal of Physics, 2015 [BibTeX]

2014

Smoothed Gradients for Stochastic Variational Inference
S. Mandt, D. Blei
Advances in Neural Information Processing Systems 27, 2014 [BibTeX]
Damping of Bloch Oscillations: Variational Solutions of the Boltzmann Equation Beyond Linear Response
S. Mandt
Physical Review A, 2014 [BibTeX]

2013

Relaxation Towards Negative Temperatures in Bosonic Systems: Generalized Gibbs Ensembles and Beyond Integrability
S. Mandt, A. Feiguin, S. Manmana
Physical Review A, 2013 [BibTeX]

2012

Fermionic Transport in a Homogeneous Hubbard Model: Out-of-Equilibrium Dynamics With Ultracold Atoms
U. Schneider, L. Hackermüller, J. P. Ronzheimer, S. Will, S. Braun, T. Best, I. Bloch, E. Demler, S. Mandt, D. Rasch, A. Rosch
Nature Physics, 2012 [BibTeX]

2011

Interacting Fermionic Atoms in Optical Lattices Diffuse Symmetrically Upwards and Downwards in a Gravitational Potential
S. Mandt, A. Rapp, A. Rosch
Physical Review Letters, 2011 [BibTeX]

2010

Equilibration Rates and Negative Absolute Temperatures for Ultracold Atoms in Optical Lattices
A. Rapp, S. Mandt, A. Rosch
Physical Review Letters, 2010 [BibTeX]
Zooming in on Local Level Statistics by Supersymmetric Extension of Free Probability
S. Mandt, M. R. Zirnbauer
Journal of Physics A: Mathematical and Theoretical, 2010 [BibTeX]