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ORCIDMaja Rudolph
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- affiliation: Bosch Center for AI, Pittsburgh, PA, USA
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2020 – today
- 2024
[c13]Kushagra Pandey, Maja Rudolph, Stephan Mandt:
Efficient Integrators for Diffusion Generative Models. ICLR 2024- [i26]Maja Rudolph, Stefan Kurz, Barbara Rakitsch:
Hybrid Modeling Design Patterns. CoRR abs/2401.00033 (2024) - [i25]Kushagra Pandey, Maja Rudolph, Stephan Mandt:
Towards Fast Stochastic Sampling in Diffusion Generative Models. CoRR abs/2402.07211 (2024) - [i24]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) - [i23]Lorenzo Perini, Maja Rudolph, Sabrina Schmedding, Chen Qiu:
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior. CoRR abs/2405.13699 (2024) - [i22]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) - 2023
- [j3]Dennis Wagner, Tobias Michels, Florian C. F. Schulz, Arjun Nair, Maja Rudolph, Marius Kloft:
TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection. Trans. Mach. Learn. Res. 2023 (2023) - [c12]Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja Rudolph:
Deep Anomaly Detection under Labeling Budget Constraints. ICML 2023: 19882-19910 - [c11]Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt:
Zero-Shot Anomaly Detection via Batch Normalization. NeurIPS 2023 - [i21]Aodong Li, Chen Qiu, Padhraic Smyth, Marius Kloft, Stephan Mandt, Maja Rudolph:
Deep Anomaly Detection under Labeling Budget Constraints. CoRR abs/2302.07832 (2023) - [i20]Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt:
Zero-Shot Anomaly Detection without Foundation Models. CoRR abs/2302.07849 (2023) - [i19]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) - [i18]Xi Wang, Laurence Aitchison, Maja Rudolph:
LoRA ensembles for large language model fine-tuning. CoRR abs/2310.00035 (2023) - [i17]Kushagra Pandey, Maja Rudolph, Stephan Mandt:
Efficient Integrators for Diffusion Generative Models. CoRR abs/2310.07894 (2023) - [i16]Clement Fung, Chen Qiu, Aodong Li, Maja Rudolph:
Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data. CoRR abs/2310.10461 (2023) - 2022
- [j2]Sindy Löwe, Phillip Lippe, Maja Rudolph, Max Welling:
Complex-Valued Autoencoders for Object Discovery. Trans. Mach. Learn. Res. 2022 (2022) - [c10]Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt:
Latent Outlier Exposure for Anomaly Detection with Contaminated Data. ICML 2022: 18153-18167 - [c9]Mona Schirmer, Mazin Eltayeb, Stefan Lessmann, Maja Rudolph:
Modeling Irregular Time Series with Continuous Recurrent Units. ICML 2022: 19388-19405 - [c8]Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph:
Raising the Bar in Graph-level Anomaly Detection. IJCAI 2022: 2196-2203 - [i15]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) - [i14]Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt:
Latent Outlier Exposure for Anomaly Detection with Contaminated Data. CoRR abs/2202.08088 (2022) - [i13]Sindy Löwe, Phillip Lippe, Maja Rudolph, Max Welling:
Complex-Valued Autoencoders for Object Discovery. CoRR abs/2204.02075 (2022) - [i12]Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph:
Raising the Bar in Graph-level Anomaly Detection. CoRR abs/2205.13845 (2022) - 2021
- [j1]Chen Qiu, Stephan Mandt, Maja Rudolph:
History Marginalization Improves Forecasting in Variational Recurrent Neural Networks. Entropy 23(12): 1563 (2021) - [c7]Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph:
Neural Transformation Learning for Deep Anomaly Detection Beyond Images. ICML 2021: 8703-8714 - [c6]Mona Schirmer, Mazin Eltayeb, Maja Rudolph:
Continuous-Discrete Recurrent Kalman Networks for Irregular Time Series. PKDD/ECML Workshops (1) 2021: 300-305 - [i11]Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph:
Neural Transformation Learning for Deep Anomaly Detection Beyond Images. CoRR abs/2103.16440 (2021) - [i10]Giao Nguyen-Quynh, Philipp Becker, Chen Qiu, Maja Rudolph, Gerhard Neumann:
Switching Recurrent Kalman Networks. CoRR abs/2111.08291 (2021) - [i9]Mona Schirmer, Mazin Eltayeb, Stefan Lessmann, Maja Rudolph:
Modeling Irregular Time Series with Continuous Recurrent Units. CoRR abs/2111.11344 (2021) - 2020
- [i8]Andreas Look, Chen Qiu, Maja Rudolph, Jan Peters, Melih Kandemir:
Deterministic Inference of Neural Stochastic Differential Equations. CoRR abs/2006.08973 (2020) - [i7]Chen Qiu, Stephan Mandt, Maja Rudolph:
Variational Dynamic Mixtures. CoRR abs/2010.10403 (2020)
2010 – 2019
- 2019
- [i6]James L. McClelland, Felix Hill, Maja Rudolph, Jason Baldridge, Hinrich Schütze:
Extending Machine Language Models toward Human-Level Language Understanding. CoRR abs/1912.05877 (2019) - 2018
- [b1]Maja Rudolph:
Exponential Family Embeddings. Columbia University, USA, 2018 - [c5]Maja Rudolph, David M. Blei:
Dynamic Embeddings for Language Evolution. WWW 2018: 1003-1011 - 2017
- [c4]Maja Rudolph, Francisco J. R. Ruiz, Susan Athey, David M. Blei:
Structured Embedding Models for Grouped Data. NIPS 2017: 251-261 - [i5]Maja Rudolph, David M. Blei:
Dynamic Bernoulli Embeddings for Language Evolution. CoRR abs/1703.08052 (2017) - [i4]Maja Rudolph, Francisco J. R. Ruiz, Susan Athey, David M. Blei:
Structured Embedding Models for Grouped Data. CoRR abs/1709.10367 (2017) - 2016
- [c3]Maja Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei:
Exponential Family Embeddings. NIPS 2016: 478-486 - [c2]Maja R. Rudolph, Matthew D. Hoffman, Aaron Hertzmann:
A Joint Model for Who-to-Follow and What-to-View Recommendations on Behance. WWW (Companion Volume) 2016: 581-584 - [c1]Maja R. Rudolph, Joseph G. Ellis, David M. Blei:
Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve. WWW 2016: 1113-1122 - [i3]Maja Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei:
Exponential Family Embeddings. CoRR abs/1608.00778 (2016) - [i2]Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei:
Edward: A library for probabilistic modeling, inference, and criticism. CoRR abs/1610.09787 (2016) - 2015
- [i1]Maja R. Rudolph, Joseph G. Ellis, David M. Blei:
Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve. CoRR abs/1506.07504 (2015)
Coauthor Index
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last updated on 2024-08-08 19:15 CEST by the dblp team
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