Papers
Preprints
Stochastic Aggregation in Graph Neural Networks
Yuanqing Wang, Theofanis Karaletsos.
under review, 2021
Bayesian Active Drug Discovery
Yuanqing Wang, Manh Nguyen, Michael Retchin, John D. Chodera, Theofanis Karaletsos.
under review, 2020
Probabilistic Meta-Representations For Neural Networks
Theofanis Karaletsos, Peter Dayan, Zoubin Ghahramani.
Uncertainty in Artificial Intelligence: Uncertainty In Deep Learning Workshop 2018.
Automatic Relevance Determination For Deep Generative Models
Theofanis Karaletsos, Gunnar Rätsch.
Pre-print 2015
Conference Publications
Variational Auto-Regressive Gaussian Processes for Continual Learning
Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui.
International Conference on Machine Learning (ICML) 2021
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights
Theofanis Karaletsos, Thang D. Bui.
Neural Information Processing Systems (NeurIPS) 2020, Advances in Approximate Bayesian Inference (AABI) 2019
TyXe: Pyro-based Bayesian neural nets for Pytorch
Hippolyt Ritter, Theofanis Karaletsos.
International Conference on Probabilistic Programming (PROBPROG) 2020.
Generalized Hidden Parameter MDPs: Transferable Model-Based RL in a handful of trials
Christian Perez, Felipe Such, Theofanis Karaletsos.
Proceedings Of the AAAI Conference On Artificial Intelligence (AAAI) 2020 (full oral).
Likelihood-Free Inference with emulator networks
Jan-Matthis Lückmann, Giacomo Bassetto,
Theofanis Karaletsos, Jakob H. Macke.
Proceedings of The 1st Symposium on Advances in Approximate Bayesian Inference (AABI), PMLR 96:32-53,2019, 2019.
Pathwise Derivatives For Multivariate Distributions
Martin Jankowiak,
Theofanis Karaletsos.
Proceedings Of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
Conditional Similarity Networks
Andreas Veit, Serge Belongie,
Theofanis Karaletsos.
Computer Vision and Pattern Recognition (CVPR), 2017.
Adversarial Message Passing For Graphical Models
Theofanis Karaletsos.
NIPS Advances In Approximate Bayesian Inference (AABI), 2016.
Bayesian unsupervised representation learning with oracle constraints
Theofanis Karaletsos, Serge Belongie, Gunnar Rätsch.
International Conference on Learning Representations (ICLR), 2016.
A Generative Model Of Words and Relationships from Multiple Sources
Stephanie Hyland,
Theofanis Karaletsos, Gunnar Rätsch.
AAAI Conference On Artificial Intelligence (AAAI) (spotlight talk) , 2016.
Knowledge Transfer with Medical Language Embeddings
Stephanie Hyland,
Theofanis Karaletsos, Gunnar Rätsch.
SDM-DMMH, also shot version in NIPS Workshop Machine Learning in Healthcare 2015 , 2016.
An Empirical Analysis of Topic Modeling for Mining Cancer Clinical Notes
Katherine Redfield Chan, Xinghua Lou,
Theofanis Karaletsos, Christopher Crosbie,Stuart Gardos, David Artz, Gunnar Rätsch.
ICDM-BioDM, 2013.
Journal Publications
Pyro: Deep Universal Probabilistic Programming
Eli Bingham, Jonathan P. Chen. Martin Jankowiak, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, Noah D. Goodman.
Journal of Machine Learning Research, 2018.
RiboDiff: Detecting Changes of Translation Efficiency from Ribosome Foot-prints
Yi Zhong,
Theofanis Karaletsos, Philipp Drewe, Vipin Thankam T Sreedharan, Kamini Singh, Hans-Guido Wendel, Gunnar Rätsch.
Oxford Press Bioinformatics, 2016.
ShapePheno: Unsupervised extraction of shape phenotypes from biological image collections
Theofanis Karaletsos, Oliver Stegle, Christine Dreyer, John Winn, Karsten Borgwardt.
Oxford Press Bioinformatics, 2012.
Workshop Papers
Gaussian Process Meta-Representations For Neural Networks
Theofanis Karaletsos, Thang Bui.
2nd Symposium on Advances in Approximate Bayesian Inference, 2019.
Generalized Hidden Parameter MDPs,
Christian Perez, Felipe Such,
Theofanis Karaletsos.
International Conference For Machine Learning: Generative Modeling and Model-Based Reasoning For Robotics and AI workshop, 2019.
Applying SVGD to bayesian neural networks for cyclical time-series prediction and inference
Xinyu Hu, Paul Szerlip,
Theofanis Karaletsos, Rohit Singh.
Neural Information Processing Systems: Bayesian Deep Learning Workshop, 2018.
Efficient transfer learning and online adaptation with latent variable models for continuous control
Christian Perez, Felipe Such,
Theofanis Karaletsos.
Neural Information Processing Systems: Continual Learning Workshop 2018, 2018.
A Generative Model Of Words and Relationships from Multiple Sources
Stephanie Hyland,
Theofanis Karaletsos, Gunnar Rätsch.
IWES Workshop for Learning Semantics, 2015.
Probabilistic Disease Progression Models For Retrospective Analysis Of Cancer Health Records
Theofanis Karaletsos, Stefan Stark, Gunnar Rätsch.
NIPS Workshop Machine Learning in Healthcare, 2015.
Poisson Matrix Factorization For Joint Modeling Of Genetics and Medical Text
Melanie Fernandez,
Theofanis Karaletsos, Julia Vogt, Stephanie Hyland, Gunnar Rätsch, Fernando Perez-Cruz.
NIPS Workshop Machine Learning in Healthcare, 2015.
Towards an integrated dynamic model of temporal structure of clinical textnotes and interactions with genetic profiles
Theofanis Karaletsos, X. Lou, K.R.Chan, C. Crosbie, G. Rätsch.
NIPS Machine Learning for Clinical Data Analysis in Healthcare, 2013.
JigPheno: Semantic Feature Extraction From Biological Images
Theofanis Karaletsos, Oliver Stegle, John Winn, Karsten Borgwardt.
NIPS Machine Learning in Computational Biology (oral), 2010.
Open Source Software
Pyro
Pyro is a deep, universal probabilistic programming language written in Python on top of PyTorch. For more information, check out the release blog.
TyXe
Bayesian Neural Network Toolbox for Pytorch
Patents
Representations Of Units in Neural Networks
Theofanis Karaletsos, Peter Dayan, Zoubin Ghahramani.
US Patent App., 16/356,991 2019.
Model Based Reinforcement Learning Based On Generalized Hidden Parameter Markov Decision Processes
Christian Perez, Felipe Such, Theofanis Karaletsos.
US Patent App., 62/851,858 2019.
Event detection using sensor data
NP Volk, Theofanis Karaletsos, U Madhow, JB Yosinski,TR Sumers.
US Patent App., 16/233,779 2019.
Systems and Methods For Intelligent Regularization of Neural Network Archi-tectures
Zoubin Ghahramani, Douglas Bemis, Theofanis Karaletsos.
US Patent App., 15/789,898 2018.