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ORCIDStephan Mandt
Person information
- affiliation: University of California, Irvine, Department of Computer Science, CA, USA
- affiliation (former): Columbia University, New York, NY, USA
- affiliation (former, PhD 2012): University of Cologne, Germany
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2020 – today
- 2024
[c56]Kushagra Pandey, Maja Rudolph, Stephan Mandt:
Efficient Integrators for Diffusion Generative Models. ICLR 2024- [c55]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: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 2024 - [c54]Tuan Pham, Stephan Mandt:
Neural NeRF Compression. ICML 2024 - [e1]Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li:
International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain. Proceedings of Machine Learning Research 238, PMLR 2024 [contents] - [i79]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 abs/2402.00809 (2024) - [i78]Kushagra Pandey, Maja Rudolph, Stephan Mandt:
Towards Fast Stochastic Sampling in Diffusion Generative Models. CoRR abs/2402.07211 (2024) - [i77]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:
On the Challenges and Opportunities in Generative AI. CoRR abs/2403.00025 (2024) - [i76]Thomas M. Sutter, Yang Meng, Norbert Fortin, Julia E. Vogt, Stephan Mandt:
Unity by Diversity: Improved Representation Learning in Multimodal VAEs. CoRR abs/2403.05300 (2024) - [i75]Kushagra Pandey, Ruihan Yang, Stephan Mandt:
Fast Samplers for Inverse Problems in Iterative Refinement Models. CoRR abs/2405.17673 (2024) - [i74]Tuan Pham, Stephan Mandt:
Neural NeRF Compression. CoRR abs/2406.08943 (2024) - [i73]Duong H. Le, Tuan Pham, Aniruddha Kembhavi, Stephan Mandt, Wei-Chiu Ma, Jiasen Lu:
Preserving Identity with Variational Score for General-purpose 3D Editing. CoRR abs/2406.08953 (2024) - [i72]Aodong Li, Yunhan Zhao, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt:
Anomaly Detection of Tabular Data Using LLMs. CoRR abs/2406.16308 (2024) - [i71]Eshant English, Eliot Wong-Toi, Matteo Fontana, Stephan Mandt, Padhraic Smyth, Christoph Lippert:
JANET: Joint Adaptive predictioN-region Estimation for Time-series. CoRR abs/2407.06390 (2024) - [i70]Thomas Specht, Mayank Nagda, Sophie Fellenz, Stephan Mandt, Hans Hasse, Fabian Jirasek:
HANNA: Hard-constraint Neural Network for Consistent Activity Coefficient Prediction. CoRR abs/2407.18011 (2024) - [i69]Nicolas Hayer, Thorsten Wendel, Stephan Mandt, Hans Hasse, Fabian Jirasek:
Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0. CoRR abs/2408.05220 (2024) - [i68]Kushagra Pandey, Jaideep Pathak, Yilun Xu, Stephan Mandt, Michael S. Pritchard, Arash Vahdat, Morteza Mardani:
Heavy-Tailed Diffusion Models. CoRR abs/2410.14171 (2024) - 2023
- [j9]Ruihan Yang
, Prakhar Srivastava, Stephan Mandt:
Diffusion Probabilistic Modeling for Video Generation. Entropy 25(10): 1469 (2023) - [j8]Yibo Yang, Stephan Mandt, Lucas Theis:
An Introduction to Neural Data Compression. Found. Trends Comput. Graph. Vis. 15(2): 113-200 (2023) - [j7]Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt:
Insights From Generative Modeling for Neural Video Compression. IEEE Trans. Pattern Anal. Mach. Intell. 45(8): 9908-9921 (2023) - [j6]Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt:
SC2 Benchmark: Supervised Compression for Split Computing. Trans. Mach. Learn. Res. 2023 (2023) - [c53]Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth:
Probabilistic Querying of Continuous-Time Event Sequences. AISTATS 2023: 10235-10251 - [c52]Yibo Yang, Stephan Mandt:
Computationally-Efficient Neural Image Compression with Shallow Decoders. ICCV 2023: 530-540 - [c51]Kushagra Pandey, Stephan Mandt:
A Complete Recipe for Diffusion Generative Models. ICCV 2023: 4238-4249 - [c50]Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja Rudolph:
Deep Anomaly Detection under Labeling Budget Constraints. ICML 2023: 19882-19910 - [c49]Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone:
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes. ICML 2023: 34409-34430 - [c48]Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt:
Zero-Shot Anomaly Detection via Batch Normalization. NeurIPS 2023 - [c47]Yibo Yang, Stephan Eckstein, Marcel Nutz, Stephan Mandt:
Estimating the Rate-Distortion Function by Wasserstein Gradient Descent. NeurIPS 2023 - [c46]Ruihan Yang, Stephan Mandt:
Lossy Image Compression with Conditional Diffusion Models. NeurIPS 2023 - [c45]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 - [c44]Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth:
Inference for mark-censored temporal point processes. UAI 2023: 226-236 - [i67]Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone:
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes. CoRR abs/2302.04534 (2023) - [i66]Aodong Li, Chen Qiu, Padhraic Smyth, Marius Kloft, Stephan Mandt, Maja Rudolph:
Deep Anomaly Detection under Labeling Budget Constraints. CoRR abs/2302.07832 (2023) - [i65]Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt:
Zero-Shot Anomaly Detection without Foundation Models. CoRR abs/2302.07849 (2023) - [i64]Kushagra Pandey, Stephan Mandt:
Generative Diffusions in Augmented Spaces: A Complete Recipe. CoRR abs/2303.01748 (2023) - [i63]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 abs/2303.05904 (2023) - [i62]Yibo Yang, Stephan Mandt:
Asymmetrically-powered Neural Image Compression with Shallow Decoders. CoRR abs/2304.06244 (2023) - [i61]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.:
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. CoRR abs/2306.08754 (2023) - [i60]Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt:
Understanding Pathologies of Deep Heteroskedastic Regression. CoRR abs/2306.16717 (2023) - [i59]Kushagra Pandey, Maja Rudolph, Stephan Mandt:
Efficient Integrators for Diffusion Generative Models. CoRR abs/2310.07894 (2023) - [i58]Yibo Yang, Stephan Eckstein, Marcel Nutz, Stephan Mandt:
Estimating the Rate-Distortion Function by Wasserstein Gradient Descent. CoRR abs/2310.18908 (2023) - [i57]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 abs/2310.20168 (2023) - [i56]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 abs/2311.05931 (2023) - [i55]Prakhar Srivastava, Ruihan Yang, Gavin Kerrigan, Gideon Dresdner, Jeremy McGibbon, Christopher S. Bretherton, Stephan Mandt:
Probabilistic Precipitation Downscaling with Optical Flow-Guided Diffusion. CoRR abs/2312.06071 (2023) - [i54]Vincent Fortuin, Yingzhen Li, Kevin Murphy, Stephan Mandt, Laura Manduchi:
Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072). Dagstuhl Reports 13(2): 47-70 (2023) - 2022
- [j5]Joseph Marino, Lei Chen, Jiawei He, Stephan Mandt:
Improving sequential latent variable models with autoregressive flows. Mach. Learn. 111(4): 1597-1620 (2022) - [c43]Anji Liu, Stephan Mandt, Guy Van den Broeck:
Lossless Compression with Probabilistic Circuits. ICLR 2022 - [c42]Yibo Yang, Stephan Mandt:
Towards Empirical Sandwich Bounds on the Rate-Distortion Function. ICLR 2022 - [c41]Antonios Alexos, Alex J. Boyd, Stephan Mandt:
Structured Stochastic Gradient MCMC. ICML 2022: 414-434 - [c40]Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt:
Latent Outlier Exposure for Anomaly Detection with Contaminated Data. ICML 2022: 18153-18167 - [c39]Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph:
Raising the Bar in Graph-level Anomaly Detection. IJCAI 2022: 2196-2203 - [c38]Alex Boyd, Samuel Showalter, Stephan Mandt, Padhraic Smyth:
Predictive Querying for Autoregressive Neural Sequence Models. NeurIPS 2022 - [c37]Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt:
Supervised Compression for Resource-Constrained Edge Computing Systems. WACV 2022: 923-933 - [i53]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 abs/2202.03944 (2022) - [i52]Yibo Yang, Stephan Mandt, Lucas Theis:
An Introduction to Neural Data Compression. CoRR abs/2202.06533 (2022) - [i51]Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt:
Latent Outlier Exposure for Anomaly Detection with Contaminated Data. CoRR abs/2202.08088 (2022) - [i50]Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt:
SC2: Supervised Compression for Split Computing. CoRR abs/2203.08875 (2022) - [i49]Ruihan Yang, Prakhar Srivastava, Stephan Mandt:
Diffusion Probabilistic Modeling for Video Generation. CoRR abs/2203.09481 (2022) - [i48]Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph:
Raising the Bar in Graph-level Anomaly Detection. CoRR abs/2205.13845 (2022) - [i47]Ruihan Yang, Stephan Mandt:
Lossy Image Compression with Conditional Diffusion Models. CoRR abs/2209.06950 (2022) - [i46]Alex Boyd, Samuel Showalter, Stephan Mandt, Padhraic Smyth:
Predictive Querying for Autoregressive Neural Sequence Models. CoRR abs/2210.06464 (2022) - [i45]Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth:
Probabilistic Querying of Continuous-Time Event Sequences. CoRR abs/2211.08499 (2022) - 2021
- [j4]Chen Qiu, Stephan Mandt, Maja Rudolph:
History Marginalization Improves Forecasting in Variational Recurrent Neural Networks. Entropy 23(12): 1563 (2021) - [c36]Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch:
Scalable Gaussian Process Variational Autoencoders. AISTATS 2021: 3511-3519 - [c35]Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt:
Hierarchical Autoregressive Modeling for Neural Video Compression. ICLR 2021 - [c34]Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph:
Neural Transformation Learning for Deep Anomaly Detection Beyond Images. ICML 2021: 8703-8714 - [c33]Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt:
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning. NeurIPS 2021: 6816-6828 - [i44]Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph:
Neural Transformation Learning for Deep Anomaly Detection Beyond Images. CoRR abs/2103.16440 (2021) - [i43]Antonios Alexos, Alex Boyd, Stephan Mandt:
Structured Stochastic Gradient MCMC. CoRR abs/2107.09028 (2021) - [i42]Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt:
Insights from Generative Modeling for Neural Video Compression. CoRR abs/2107.13136 (2021) - [i41]Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt:
Supervised Compression for Resource-constrained Edge Computing Systems. CoRR abs/2108.11898 (2021) - [i40]Anji Liu, Stephan Mandt, Guy Van den Broeck:
Lossless Compression with Probabilistic Circuits. CoRR abs/2111.11632 (2021) - [i39]Yibo Yang, Stephan Mandt:
Towards Empirical Sandwich Bounds on the Rate-Distortion Function. CoRR abs/2111.12166 (2021) - [i38]Harshini Mangipudi, Griffin Mooers, Mike Pritchard, Tom Beucler, Stephan Mandt:
Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs. CoRR abs/2112.01221 (2021) - 2020
- [c32]Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt:
GP-VAE: Deep Probabilistic Time Series Imputation. AISTATS 2020: 1651-1661 - [c31]Griffin Mooers, Jens Tuyls, Stephan Mandt, Mike Pritchard, Tom G. Beucler:
Generative Modeling of Atmospheric Convection. CI 2020: 98-105 - [c30]Robert Bamler, Stephan Mandt:
Extreme Classification via Adversarial Softmax Approximation. ICLR 2020 - [c29]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: 9289-9299 - [c28]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: 10248-10259 - [c27]Yibo Yang, Robert Bamler, Stephan Mandt:
Variational Bayesian Quantization. ICML 2020: 10670-10680 - [c26]Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth:
User-Dependent Neural Sequence Models for Continuous-Time Event Data. NeurIPS 2020 - [c25]Yibo Yang, Robert Bamler, Stephan Mandt:
Improving Inference for Neural Image Compression. NeurIPS 2020 - [i37]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 abs/2001.04694 (2020) - [i36]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 abs/2002.02405 (2020) - [i35]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 abs/2002.02655 (2020) - [i34]Robert Bamler, Stephan Mandt:
Extreme Classification via Adversarial Softmax Approximation. CoRR abs/2002.06298 (2020) - [i33]Yibo Yang, Robert Bamler, Stephan Mandt:
Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding. CoRR abs/2002.08158 (2020) - [i32]Yibo Yang, Robert Bamler, Stephan Mandt:
Improving Inference for Neural Image Compression. CoRR abs/2006.04240 (2020) - [i31]Griffin Mooers, Jens Tuyls, Stephan Mandt, Michael S. Pritchard, Tom Beucler:
Generative Modeling for Atmospheric Convection. CoRR abs/2007.01444 (2020) - [i30]Joseph Marino, Lei Chen, Jiawei He, Stephan Mandt:
Improving Sequential Latent Variable Models with Autoregressive Flows. CoRR abs/2010.03172 (2020) - [i29]Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt:
Hierarchical Autoregressive Modeling for Neural Video Compression. CoRR abs/2010.10258 (2020) - [i28]Chen Qiu, Stephan Mandt, Maja Rudolph:
Variational Dynamic Mixtures. CoRR abs/2010.10403 (2020) - [i27]Metod Jazbec, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch:
Scalable Gaussian Process Variational Autoencoders. CoRR abs/2010.13472 (2020) - [i26]Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth:
User-Dependent Neural Sequence Models for Continuous-Time Event Data. CoRR abs/2011.03231 (2020) - [i25]Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt:
Variational Beam Search for Online Learning with Distribution Shifts. CoRR abs/2012.08101 (2020)
2010 – 2019
- 2019
- [j3]Cheng Zhang, Judith Bütepage, Hedvig Kjellström, Stephan Mandt:
Advances in Variational Inference. IEEE Trans. Pattern Anal. Mach. Intell. 41(8): 2008-2026 (2019) - [c24]Cheng Zhang, Cengiz Öztireli, Stephan Mandt, Giampiero Salvi:
Active Mini-Batch Sampling Using Repulsive Point Processes. AAAI 2019: 5741-5748 - [c23]Joseph Marino, Lei Chen, Jiawei He, Stephan Mandt:
Improving Sequential Latent Variable Models with Autoregressive Flows. AABI 2019: 1-16 - [c22]Florian Schmidt, Stephan Mandt, Thomas Hofmann:
Autoregressive Text Generation Beyond Feedback Loops. EMNLP/IJCNLP (1) 2019: 3398-3404 - [c21]Majed El Helou, Stephan Mandt, Andreas Krause, Paul A. Beardsley:
Mobile Robotic Painting of Texture. ICRA 2019: 640-647 - [c20]Salvator Lombardo, Jun Han, Christopher Schroers, Stephan Mandt:
Deep Generative Video Compression. NeurIPS 2019: 9283-9294 - [c19]Robert Bamler, Farnood Salehi, Stephan Mandt:
Augmenting and Tuning Knowledge Graph Embeddings. UAI 2019: 508-518 - [i24]Robert Bamler, Farnood Salehi, Stephan Mandt:
Augmenting and Tuning Knowledge Graph Embeddings. CoRR abs/1907.01068 (2019) - [i23]Robert Bamler, Stephan Mandt:
A Quantum Field Theory of Representation Learning. CoRR abs/1907.02163 (2019) - [i22]Vincent Fortuin, Gunnar Rätsch, Stephan Mandt:
Multivariate Time Series Imputation with Variational Autoencoders. CoRR abs/1907.04155 (2019) - [i21]Florian Schmidt, Stephan Mandt, Thomas Hofmann:
Autoregressive Text Generation Beyond Feedback Loops. CoRR abs/1908.11658 (2019) - [i20]Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt:
Tightening Bounds for Variational Inference by Revisiting Perturbation Theory. CoRR abs/1910.00069 (2019) - 2018
- [c18]Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt:
Scalable Generalized Dynamic Topic Models. AISTATS 2018: 1427-1435 - [c17]Tianfan Fu, Cheng Zhang, Stephan Mandt:
Continuous Word Embedding Fusion via Spectral Decomposition. CoNLL 2018: 11-20 - [c16]Joseph Marino, Yisong Yue, Stephan Mandt:
Learning to Infer. ICLR (Workshop) 2018 - [c15]Robert Bamler, Stephan Mandt:
Improving Optimization in Models With Continuous Symmetry Breaking. ICML 2018: 432-441 - [c14]Alexander Buchholz, Florian Wenzel, Stephan Mandt:
Quasi-Monte Carlo Variational Inference. ICML 2018: 667-676 - [c13]Joseph Marino, Yisong Yue, Stephan Mandt:
Iterative Amortized Inference. ICML 2018: 3400-3409 - [c12]Yingzhen Li, Stephan Mandt:
Disentangled Sequential Autoencoder. ICML 2018: 5656-5665 - [c11]Lucas Deecke, Robert A. Vandermeulen, Lukas Ruff, Stephan Mandt, Marius Kloft:
Image Anomaly Detection with Generative Adversarial Networks. ECML/PKDD (1) 2018: 3-17 - [i19]Yingzhen Li, Stephan Mandt:
A Deep Generative Model for Disentangled Representations of Sequential Data. CoRR abs/1803.02991 (2018) - [i18]Robert Bamler, Stephan Mandt:
Improving Optimization in Models With Continuous Symmetry Breaking. CoRR abs/1803.03234 (2018) - [i17]Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt:
Scalable Generalized Dynamic Topic Models. CoRR abs/1803.07868 (2018) - [i16]Cheng Zhang, Cengiz Öztireli, Stephan Mandt, Giampiero Salvi:
Active Mini-Batch Sampling using Repulsive Point Processes. CoRR abs/1804.02772 (2018) - [i15]Alexander Buchholz, Florian Wenzel, Stephan Mandt:
Quasi-Monte Carlo Variational Inference. CoRR abs/1807.01604 (2018) - [i14]Joseph Marino, Yisong Yue, Stephan Mandt:
Iterative Amortized Inference. CoRR abs/1807.09356 (2018) - [i13]Jun Han, Salvator Lombardo, Christopher Schroers, Stephan Mandt:
Deep Probabilistic Video Compression. CoRR abs/1810.02845 (2018) - 2017
- [j2]Stephan Mandt, Matthew D. Hoffman, David M. Blei:
Stochastic Gradient Descent as Approximate Bayesian Inference. J. Mach. Learn. Res. 18: 134:1-134:35 (2017) - [j1]Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft:
Sparse probit linear mixed model. Mach. Learn. 106(9-10): 1621-1642 (2017) - [c10]Zhiwei Deng, Rajitha Navarathna, Peter Carr, Stephan Mandt, Yisong Yue, Iain A. Matthews, Greg Mori:
Factorized Variational Autoencoders for Modeling Audience Reactions to Movies. CVPR 2017: 6014-6023 - [c9]Robert Bamler, Stephan Mandt:
Dynamic Word Embeddings. ICML 2017: 380-389 - [c8]Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt:
Perturbative Black Box Variational Inference. NIPS 2017: 5079-5088 - [c7]Cheng Zhang, Hedvig Kjellström, Stephan Mandt:
Balanced Mini-batch Sampling for SGD Using Determinantal Point Processes. UAI 2017 - [i12]Robert Bamler, Stephan Mandt:
Dynamic Word Embeddings via Skip-Gram Filtering. CoRR abs/1702.08359 (2017) - [i11]Stephan Mandt, Matthew D. Hoffman, David M. Blei:
Stochastic Gradient Descent as Approximate Bayesian Inference. CoRR abs/1704.04289 (2017) - [i10]Cheng Zhang, Hedvig Kjellström, Stephan Mandt:
Stochastic Learning on Imbalanced Data: Determinantal Point Processes for Mini-batch Diversification. CoRR abs/1705.00607 (2017) - [i9]Robert Bamler, Stephan Mandt:
Structured Black Box Variational Inference for Latent Time Series Models. CoRR abs/1707.01069 (2017) - [i8]Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt:
Perturbative Black Box Variational Inference. CoRR abs/1709.07433 (2017) - [i7]Geng Ji, Robert Bamler, Erik B. Sudderth, Stephan Mandt:
Bayesian Paragraph Vectors. CoRR abs/1711.03946 (2017) - [i6]Cheng Zhang, Judith Bütepage, Hedvig Kjellström, Stephan Mandt:
Advances in Variational Inference. CoRR abs/1711.05597 (2017) - 2016
- [c6]Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David M. Blei:
Variational Tempering. AISTATS 2016: 704-712 - [c5]Stephan Mandt, Matthew D. Hoffman, David M. Blei:
A Variational Analysis of Stochastic Gradient Algorithms. ICML 2016: 354-363 - [c4]Maja Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei:
Exponential Family Embeddings. NIPS 2016: 478-486 - [c3]Oleksandr Zadorozhnyi, Gunthard Benecke, Stephan Mandt, Tobias Scheffer, Marius Kloft:
Huber-Norm Regularization for Linear Prediction Models. ECML/PKDD (1) 2016: 714-730 - [c2]Stephan Mandt, Florian Wenzel, Shinichi Nakajima, Christoph Lippert, Marius Kloft:
Separating Sparse Signals from Correlated Noise in Binary Classification. CFA@UAI 2016: 48-58 - [i5]Stephan Mandt, Matthew D. Hoffman, David M. Blei:
A Variational Analysis of Stochastic Gradient Algorithms. CoRR abs/1602.02666 (2016) - [i4]Maja Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei:
Exponential Family Embeddings. CoRR abs/1608.00778 (2016) - 2015
- [i3]Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft:
Sparse Estimation in a Correlated Probit Model. CoRR abs/1507.04777 (2015) - 2014
- [c1]Stephan Mandt, David M. Blei:
Smoothed Gradients for Stochastic Variational Inference. NIPS 2014: 2438-2446 - [i2]Stephan Mandt, David M. Blei:
Smoothed Gradients for Stochastic Variational Inference. CoRR abs/1406.3650 (2014) - [i1]Farhan Abrol, Stephan Mandt, Rajesh Ranganath, David M. Blei:
Deterministic Annealing for Stochastic Variational Inference. CoRR abs/1411.1810 (2014)
Coauthor Index
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