Schrouff, Jessica

10 publications

TMLR 2025 An Evolutionary Algorithm for Black-Box Adversarial Attack Against Explainable Methods Phoenix Neale Williams, Jessica Schrouff, Lea Goetz

ICML 2025 FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch Virginia Aglietti, Ira Ktena, Jessica Schrouff, Eleni Sgouritsa, Francisco Ruiz, Alan Malek, Alexis Bellot, Silvia Chiappa

ICML 2024 Evaluating Model Bias Requires Characterizing Its Mistakes Isabela Albuquerque, Jessica Schrouff, David Warde-Farley, Ali Taylan Cemgil, Sven Gowal, Olivia Wiles

ICLRW 2024 Evaluating Model Bias Requires Characterizing Its Mistakes Isabela Albuquerque, Jessica Schrouff, David Warde-Farley, Ali Taylan Cemgil, Sven Gowal, Olivia Wiles

NeurIPS 2024 Mind the Graph When Balancing Data for Fairness or Robustness Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki, Alan Malek, Isabela Albuquerque, Arthur Gretton, Alexander D'Amour, Silvia Chiappa

AISTATS 2023 Adapting to Latent Subgroup Shifts via Concepts and Proxies Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D’Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai

NeurIPS 2022 A Reduction to Binary Approach for Debiasing Multiclass Datasets Ibrahim M Alabdulmohsin, Jessica Schrouff, Sanmi Koyejo

NeurIPS 2022 Diagnosing Failures of Fairness Transfer Across Distribution Shift in Real-World Medical Settings Jessica Schrouff, Natalie Harris, Sanmi Koyejo, Ibrahim M Alabdulmohsin, Eva Schnider, Krista Opsahl-Ong, Alexander Brown, Subhrajit Roy, Diana Mincu, Christina Chen, Awa Dieng, Yuan Liu, Vivek Natarajan, Alan Karthikesalingam, Katherine A. Heller, Silvia Chiappa, Alexander D'Amour

CHIL 2022 Disability Prediction in Multiple Sclerosis Using Performance Outcome Measures and Demographic Data Subhrajit Roy, Diana Mincu, Lev Proleev, Negar Rostamzadeh, Chintan Ghate, Natalie Harris, Christina Chen, Jessica Schrouff, Nenad Tomašev, Fletcher Lee Hartsell, Katherine Heller

JMLR 2022 Underspecification Presents Challenges for Credibility in Modern Machine Learning Alexander D'Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley