ShapePheno: unsupervised extraction of shape phenotypes from biological image collections. - Theofanis Karaletsos; Oliver Stegle; Christine Dreyer; John M. Winn; Karsten M. Borgwardt (Bioinform., 2012)

An Empirical Analysis of Topic Modeling for Mining Cancer Clinical Notes. - Katherine Redfield Chan; Xinghua Lou; Theofanis Karaletsos; Christopher Crosbie; Stuart M. Gardos; David Artz; Gunnar Rätsch (ICDM Workshops, 2013)

A Generative Model of Words and Relationships from Multiple Sources. - Stephanie L. Hyland; Theofanis Karaletsos; Gunnar Rätsch (AAAI, 2016)

Knowledge Transfer with Medical Language Embeddings. - Stephanie L. Hyland; Theofanis Karaletsos; Gunnar Rätsch (CoRR, 2016)

Adversarial Message Passing For Graphical Models. - Theofanis Karaletsos (CoRR, 2016)

When crowds hold privileges: Bayesian unsupervised representation learning with oracle constraints. - Theofanis Karaletsos; Serge J. Belongie; Gunnar Rätsch (ICLR (Poster), 2016)

Disentangling Nonlinear Perceptual Embeddings With Multi-Query Triplet Networks. - Andreas Veit; Serge J. Belongie; Theofanis Karaletsos (CoRR, 2016)

Conditional Similarity Networks. - Andreas Veit; Serge J. Belongie; Theofanis Karaletsos (CVPR, 2017)

RiboDiff: detecting changes of mRNA translation efficiency from ribosome footprints. - Yi Zhong; Theofanis Karaletsos; Philipp Drewe; Vipin T. Sreedharan; David Kuo; Kamini Singh; Hans-Guido Wendel; Gunnar Rätsch (Bioinform., 2017)

Likelihood-free inference with emulator networks. - Jan-Matthis Lueckmann; Giacomo Bassetto; Theofanis Karaletsos; Jakob H. Macke (AABI, 2018)

Probabilistic Meta-Representations Of Neural Networks. - Theofanis Karaletsos; Peter Dayan; Zoubin Ghahramani (CoRR, 2018)

Efficient transfer learning and online adaptation with latent variable models for continuous control. - Christian F. Perez; Felipe Petroski Such; Theofanis Karaletsos (CoRR, 2018)

Pathwise Derivatives for Multivariate Distributions. - Martin Jankowiak; Theofanis Karaletsos (AISTATS, 2019)

Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference. - Xinyu Hu; Paul A. Szerlip; Theofanis Karaletsos; Rohit Singh (CoRR, 2019)

Pyro: Deep Universal Probabilistic Programming. - Eli Bingham; Jonathan P. Chen; Martin Jankowiak; Fritz Obermeyer; Neeraj Pradhan; Theofanis Karaletsos; Rohit Singh; Paul A. Szerlip; Paul Horsfall; Noah D. Goodman (J. Mach. Learn. Res., 2019)

Generalized Hidden Parameter MDPs: Transferable Model-Based RL in a Handful of Trials. - Christian F. Perez; Felipe Petroski Such; Theofanis Karaletsos (AAAI, 2020)

Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights. - Theofanis Karaletsos; Thang D. Bui (NeurIPS, 2020)

Variational Auto-Regressive Gaussian Processes for Continual Learning. - Sanyam Kapoor; Theofanis Karaletsos; Thang D. Bui (ICML, 2021)

Stochastic Aggregation in Graph Neural Networks. - Yuanqing Wang; Theofanis Karaletsos (CoRR, 2021)

Localized Uncertainty Attacks. - Ousmane Amadou Dia; Theofanis Karaletsos; Caner Hazirbas; Cristian Canton-Ferrer; Ilknur Kaynar Kabul; Erik Meijer 001 (CoRR, 2021)

TyXe: Pyro-based Bayesian neural nets for Pytorch. - Hippolyt Ritter; Theofanis Karaletsos (MLSys, 2022)

Black-box coreset variational inference. - Dionysis Manousakas; Hippolyt Ritter; Theofanis Karaletsos (NeurIPS, 2022)

Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder. - Michael Bereket; Theofanis Karaletsos (NeurIPS, 2023)

Contextual Vision Transformers for Robust Representation Learning. - Yujia Bao; Theofanis Karaletsos (CoRR, 2023)

Channel Vision Transformers: An Image Is Worth C x 16 x 16 Words. - Yujia Bao; Srinivasan Sivanandan; Theofanis Karaletsos (CoRR, 2023)

DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded Libraries. - Kirill Shmilovich; Benson Chen; Theofanis Karaletsos; Mohammad M. Sultan (J. Chem. Inf. Model., 2023)

Channel Vision Transformers: An Image Is Worth 1 x 16 x 16 Words. - Yujia Bao; Srinivasan Sivanandan; Theofanis Karaletsos (ICLR, 2024)

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. - 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; et al. (ICML, 2024)

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. - 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; et al. (CoRR, 2024)

How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities. - Charlotte Bunne; Yusuf H. Roohani; Yanay Rosen; Ankit Gupta; Xikun Zhang 001; Marcel Roed; Theo Alexandrov; Mohammed AlQuraishi; Patricia Brennan; Daniel B. Burkhardt; Andrea Califano; Jonah Cool; et al. (CoRR, 2024)

Adjusting Pretrained Backbones for Performativity. - Berker Demirel; Lingjing Kong; Kun Zhang 001; Theofanis Karaletsos; Celestine Mendler-Dünner; Francesco Locatello (CoRR, 2024)

Compositional Deep Probabilistic Models of DNA-Encoded Libraries. - Benson Chen; Mohammad M. Sultan; Theofanis Karaletsos (J. Chem. Inf. Model., 2024)

Variational Control for Guidance in Diffusion Models. - Kushagra Pandey; Farrin Marouf Sofian; Felix Draxler; Theofanis Karaletsos; Stephan Mandt (CoRR, 2025)