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ORCIDTheofanis Karaletsos
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- affiliation: Insitro, USA
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2020 – today
- 2024
[j5]Benson Chen
, Mohammad M. Sultan, Theofanis Karaletsos:
Compositional Deep Probabilistic Models of DNA-Encoded Libraries. J. Chem. Inf. Model. 64(4): 1123-1133 (2024)- [c14]Yujia Bao, Srinivasan Sivanandan, Theofanis Karaletsos:
Channel Vision Transformers: An Image Is Worth 1 x 16 x 16 Words. ICLR 2024 - [c13]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 - [i24]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) - [i23]Charlotte Bunne, Yusuf Roohani, Yanay Rosen, Ankit Gupta, Xikun Zhang, Marcel Roed, Theo Alexandrov, Mohammed AlQuraishi, Patricia Brennan, Daniel B. Burkhardt, Andrea Califano, Jonah Cool, Abby F. Dernburg, Kirsty Ewing, Emily B. Fox, Matthias Haury, Amy E. Herr, Eric Horvitz, Patrick D. Hsu, Viren Jain, Gregory R. Johnson, Thomas Kalil, David R. Kelley, Shana O. Kelley, Anna Kreshuk, Tim Mitchison, Stephani Otte, Jay Shendure, Nicolas J. Sofroniew, Fabian J. Theis, Christina V. Theodoris, Srigokul Upadhyayula, Marc Valer, Bo Wang, Eric Xing, Serena Yeung-Levy, Marinka Zitnik, Theofanis Karaletsos, Aviv Regev, Emma Lundberg, Jure Leskovec, Stephen R. Quake:
How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities. CoRR abs/2409.11654 (2024) - [i22]Berker Demirel, Lingjing Kong, Kun Zhang, Theofanis Karaletsos, Celestine Mendler-Dünner, Francesco Locatello:
Adjusting Pretrained Backbones for Performativity. CoRR abs/2410.04499 (2024) - 2023
- [j4]Kirill Shmilovich, Benson Chen, Theofanis Karaletsos, Mohammad M. Sultan:
DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded Libraries. J. Chem. Inf. Model. 63(9): 2719-2727 (2023) - [c12]Michael Bereket, Theofanis Karaletsos:
Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder. NeurIPS 2023 - [i21]Yujia Bao, Theofanis Karaletsos:
Contextual Vision Transformers for Robust Representation Learning. CoRR abs/2305.19402 (2023) - [i20]Yujia Bao, Srinivasan Sivanandan, Theofanis Karaletsos:
Channel Vision Transformers: An Image Is Worth C x 16 x 16 Words. CoRR abs/2309.16108 (2023) - [i19]Michael Bereket, Theofanis Karaletsos:
Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder. CoRR abs/2311.02794 (2023) - 2022
- [c11]Hippolyt Ritter, Theofanis Karaletsos:
TyXe: Pyro-based Bayesian neural nets for Pytorch. MLSys 2022 - [c10]Dionysis Manousakas, Hippolyt Ritter, Theofanis Karaletsos:
Black-box coreset variational inference. NeurIPS 2022 - [i18]Dionysis Manousakas, Hippolyt Ritter, Theofanis Karaletsos:
Black-box Coreset Variational Inference. CoRR abs/2211.02377 (2022) - [i17]Kirill Shmilovich, Benson Chen, Theofanis Karaletsos, Mohammad M. Sultan:
DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded Libraries. CoRR abs/2212.00136 (2022) - 2021
- [c9]Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui:
Variational Auto-Regressive Gaussian Processes for Continual Learning. ICML 2021: 5290-5300 - [i16]Yuanqing Wang, Theofanis Karaletsos:
Stochastic Aggregation in Graph Neural Networks. CoRR abs/2102.12648 (2021) - [i15]Ousmane Amadou Dia, Theofanis Karaletsos, Caner Hazirbas, Cristian Canton-Ferrer, Ilknur Kaynar Kabul, Erik Meijer:
Localized Uncertainty Attacks. CoRR abs/2106.09222 (2021) - [i14]Hippolyt Ritter, Theofanis Karaletsos:
TyXe: Pyro-based Bayesian neural nets for Pytorch. CoRR abs/2110.00276 (2021) - 2020
- [c8]Christian F. Perez, Felipe Petroski Such, Theofanis Karaletsos:
Generalized Hidden Parameter MDPs: Transferable Model-Based RL in a Handful of Trials. AAAI 2020: 5403-5411 - [c7]Theofanis Karaletsos, Thang D. Bui:
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights. NeurIPS 2020 - [i13]Christian F. Perez, Felipe Petroski Such, Theofanis Karaletsos:
Generalized Hidden Parameter MDPs Transferable Model-based RL in a Handful of Trials. CoRR abs/2002.03072 (2020) - [i12]Theofanis Karaletsos, Thang D. Bui:
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights. CoRR abs/2002.04033 (2020) - [i11]Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui:
Variational Auto-Regressive Gaussian Processes for Continual Learning. CoRR abs/2006.05468 (2020)
2010 – 2019
- 2019
- [j3]Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul A. Szerlip, Paul Horsfall, Noah D. Goodman:
Pyro: Deep Universal Probabilistic Programming. J. Mach. Learn. Res. 20: 28:1-28:6 (2019) - [c6]Martin Jankowiak, Theofanis Karaletsos:
Pathwise Derivatives for Multivariate Distributions. AISTATS 2019: 333-342 - [i10]Xinyu Hu, Paul A. Szerlip, Theofanis Karaletsos, Rohit Singh:
Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference. CoRR abs/1901.05906 (2019) - 2018
- [c5]Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos, Jakob H. Macke:
Likelihood-free inference with emulator networks. AABI 2018: 32-53 - [i9]Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos, Jakob H. Macke:
Likelihood-free inference with emulator networks. CoRR abs/1805.09294 (2018) - [i8]Martin Jankowiak, Theofanis Karaletsos:
Pathwise Derivatives for Multivariate Distributions. CoRR abs/1806.01856 (2018) - [i7]Theofanis Karaletsos, Peter Dayan, Zoubin Ghahramani:
Probabilistic Meta-Representations Of Neural Networks. CoRR abs/1810.00555 (2018) - [i6]Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul A. Szerlip, Paul Horsfall, Noah D. Goodman:
Pyro: Deep Universal Probabilistic Programming. CoRR abs/1810.09538 (2018) - [i5]Christian F. Perez, Felipe Petroski Such, Theofanis Karaletsos:
Efficient transfer learning and online adaptation with latent variable models for continuous control. CoRR abs/1812.03399 (2018) - 2017
- [j2]Yi Zhong, Theofanis Karaletsos, Philipp Drewe, Vipin T. Sreedharan, David Kuo, Kamini Singh, Hans-Guido Wendel, Gunnar Rätsch:
RiboDiff: detecting changes of mRNA translation efficiency from ribosome footprints. Bioinform. 33(1): 139-141 (2017) - [c4]Andreas Veit, Serge J. Belongie, Theofanis Karaletsos:
Conditional Similarity Networks. CVPR 2017: 1781-1789 - 2016
- [c3]Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch:
A Generative Model of Words and Relationships from Multiple Sources. AAAI 2016: 2622-2629 - [c2]Theofanis Karaletsos, Serge J. Belongie, Gunnar Rätsch:
When crowds hold privileges: Bayesian unsupervised representation learning with oracle constraints. ICLR (Poster) 2016 - [i4]Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch:
Knowledge Transfer with Medical Language Embeddings. CoRR abs/1602.03551 (2016) - [i3]Andreas Veit, Serge J. Belongie, Theofanis Karaletsos:
Disentangling Nonlinear Perceptual Embeddings With Multi-Query Triplet Networks. CoRR abs/1603.07810 (2016) - [i2]Theofanis Karaletsos:
Adversarial Message Passing For Graphical Models. CoRR abs/1612.05048 (2016) - 2015
- [i1]Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch:
A Generative Model of Words and Relationships from Multiple Sources. CoRR abs/1510.00259 (2015) - 2013
- [c1]Katherine Redfield Chan, Xinghua Lou, Theofanis Karaletsos, Christopher Crosbie, Stuart M. Gardos, David Artz, Gunnar Rätsch:
An Empirical Analysis of Topic Modeling for Mining Cancer Clinical Notes. ICDM Workshops 2013: 56-63 - 2012
- [j1]Theofanis Karaletsos, Oliver Stegle, Christine Dreyer, John M. Winn, Karsten M. Borgwardt:
ShapePheno: unsupervised extraction of shape phenotypes from biological image collections. Bioinform. 28(7): 1001-1008 (2012)
Coauthor Index
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