Patrick Schwab

According to our database, Patrick Schwab authored at least 30 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Knowledge abstraction and filtering based federated learning over heterogeneous data views in healthcare.

Anshul Thakur, Soheila Molaei, Pafue Christy Nganjimi, Andrew A. S. Soltan, Patrick Schwab, Kim Branson, David A. Clifton

npj Digit. Medicine, 2024

Efficient Differentiable Discovery of Causal Order.

Mathieu Chevalley, Arash Mehrjou, Patrick Schwab

CoRR, 2024

Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm.

Mathieu Chevalley, Patrick Schwab, Arash Mehrjou

CoRR, 2024

Sample Selection Bias in Machine Learning for Healthcare.

Vinod Kumar Chauhan, Lei A. Clifton, Achille Salaün, Huiqi Yvonne Lu, Kim Branson, Patrick Schwab, Gaurav Nigam, David A. Clifton

CoRR, 2024

A collection of the accepted papers for the Human-Centric Representation Learning workshop at AAAI 2024.

Dimitris Spathis, Aaqib Saeed, Ali Etemad, Sana Tonekaboni, Stefanos Laskaridis, Shohreh Deldari, Chi Ian Tang, Patrick Schwab, Shyam Tailor

CoRR, 2024

2023

The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data.

Mathieu Chevalley, Jacob Sackett-Sanders, Yusuf Roohani, Pascal Notin, Artemy Bakulin, Dariusz Brzezinski, Kaiwen Deng, Yuanfang Guan, Justin Hong, Michael Ibrahim, Wojciech Kotlowski, Marcin Kowiel, Panagiotis Misiakos, Achille Nazaret, Markus Püschel, Chris Wendler, Arash Mehrjou, Patrick Schwab

CoRR, 2023

Multi-omics Prediction from High-content Cellular Imaging with Deep Learning.

Rahil Mehrizi, Arash Mehrjou, Maryana Alegro, Yi Zhao, Benedetta Carbone, Carl Fishwick, Johanna Vappiani, Jing Bi, Siobhan Sanford, Hakan Keles, Marcus Bantscheff, Cuong Nguyen, Patrick Schwab

CoRR, 2023

DiscoBAX: Discovery of optimal intervention sets in genomic experiment design.

Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab

Proceedings of the International Conference on Machine Learning, 2023

2022

FED-CD: Federated Causal Discovery from Interventional and Observational Data.

Amin Abyaneh, Nino Scherrer, Patrick Schwab, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou

CoRR, 2022

CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data.

Mathieu Chevalley, Yusuf Roohani, Arash Mehrjou, Jure Leskovec, Patrick Schwab

CoRR, 2022

Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database.

Arash Mehrjou, Ashkan Soleymani, Annika Buchholz, Jürgen Hetzel, Patrick Schwab, Stefan Bauer

CoRR, 2022

Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations.

Simon Bing, Andrea Dittadi, Stefan Bauer, Patrick Schwab

CoRR, 2022

GeneDisco: A Benchmark for Experimental Design in Drug Discovery.

Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data.

Patrick Schwab, Walter Karlen

IEEE J. Biomed. Health Informatics, 2021

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge.

Solveig K. Sieberts, Jennifer Schaff, Marlena Duda, Bálint Á Pataki, Ming Sun, Phil Snyder, Jean-Francois Daneault, Federico Parisi, Gianluca Costante, Udi Rubin, Peter Banda, Yooree Chae, Elias Chaibub Neto, Earl Ray Dorsey, Zafer Aydin, Aipeng Chen, Laura L. Elo, Carlos Espino, Enrico Glaab, Ethan Goan, Fatemeh Noushin Golabchi, Yasin Görmez, Maria K. Jaakkola, Jitendra Jonnagaddala, Riku Klén, Dongmei Li, Christian McDaniel, Dimitri Perrin, Thanneer M. Perumal, Nastaran Mohammadian Rad, Erin Rainaldi, Stefano Sapienza, Patrick Schwab, Nikolai Shokhirev, Mikko S. Venäläinen, Gloria Vergara-Diaz, Yuqian Zhang, Avner G. S. Abrami, Aditya Adhikary, Carla Agurto, Sherry Bhalla, Halil Ibrahim Bilgin, Vittorio Caggiano, Jun Cheng, Eden Deng, Qiwei Gan, Rajan Girsa, Zhi Han, Stephen Heisig, Kun Huang, Samad Jahandideh, Wolfgang Kopp, Christoph F. Kurz, Gregor Lichtner, Raquel Norel, G. P. S. Raghava, Tavpritesh Sethi, Nicholas Shawen, Vaibhav Tripathi, Matthew Tsai, Tongxin Wang, Yi Wu, Jie Zhang, Xinyu Zhang, Yuanjia Wang, Yuanfang Guan, Daniela Brunner, Paolo Bonato, Lara M. Mangravite, Larsson Omberg

npj Digit. Medicine, 2021

Overcoming barriers to data sharing with medical image generation: a comprehensive evaluation.

August DuMont Schütte, Jürgen Hetzel, Sergios Gatidis, Tobias Hepp, Benedikt Dietz, Stefan Bauer, Patrick Schwab

npj Digit. Medicine, 2021

Learning Neural Causal Models with Active Interventions.

Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael C. Mozer, Yoshua Bengio, Stefan Bauer, Nan Rosemary Ke

CoRR, 2021

NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments.

Sonali Parbhoo, Stefan Bauer, Patrick Schwab

CoRR, 2021

2020

Real-time Prediction of COVID-19 related Mortality using Electronic Health Records.

Patrick Schwab, Arash Mehrjou, Sonali Parbhoo, Leo Anthony Celi, Jürgen Hetzel, Markus Hofer, Bernhard Schölkopf, Stefan Bauer

CoRR, 2020

predCOVID-19: A Systematic Study of Clinical Predictive Models for Coronavirus Disease 2019.

Patrick Schwab, August DuMont Schütte, Benedikt Dietz, Stefan Bauer

CoRR, 2020

Learning Counterfactual Representations for Estimating Individual Dose-Response Curves.

Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhmann, Walter Karlen

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

CXPlain: Causal Explanations for Model Interpretation under Uncertainty.

Patrick Schwab, Walter Karlen

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks.

Patrick Schwab, Djordje Miladinovic, Walter Karlen

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data.

Patrick Schwab, Walter Karlen

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks.

Patrick Schwab, Lorenz Linhardt, Walter Karlen

CoRR, 2018

Granger-causal Attentive Mixtures of Experts.

Patrick Schwab, Walter Karlen

CoRR, 2018

Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care.

Patrick Schwab, Emanuela Keller, Carl Muroi, David J. Mack, Christian Strässle, Walter Karlen

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks.

Patrick Schwab, Gaetano Claudio Scebba, Jia Zhang, Marco Delai, Walter Karlen

Proceedings of the Computing in Cardiology, 2017

2015

Capturing the Essence: Towards the Automated Generation of Transparent Behavior Models.

Patrick Schwab, Helmut Hlavacs

Proceedings of the Eleventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2015

2014

A Unified Framework for Retrieving Diverse Social Images.

Maia Zaharieva, Patrick Schwab

Proceedings of the Working Notes Proceedings of the MediaEval 2014 Workshop, 2014