Timezone: »
Bayesian coresets have become of increasing interest recently for providing a theoretically sound, scalable approach to Bayesian inference. In brief, a coreset is a (weighted) subsample sample of a dataset that approximates the original dataset under some metric. Bayesian coresets specifically focus on approximations that approximate the posterior distribution. Unfortunately, existing Bayesian coreset approaches can significantly undersample minority subpopulations, leading to a lack of distributional robustness. As a remedy, this work extends existing Bayesian coresets from enforcing sparsity constraints to group-wise sparsity constraints. We explore how this approach helps to mitigate distributional vulnerability. We further generalize the group constraints to Bayesian coresets with matroid constraints, which may be of independent interest. We present an optimization analysis of the proposed approach, along with an empirical evaluation on benchmark datasets that support our claims.
Author Information
Shovik Guha (University of Illinois, Urbana Champaign)
Rajiv Khanna (University of California, Berkeley)
Sanmi Koyejo (University of Illinois at Urbana-Champaign & Google Research)

Sanmi Koyejo is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign and a research scientist at Google AI in Accra. Koyejo's research interests are in developing the principles and practice of adaptive and robust machine learning. Additionally, Koyejo focuses on applications to biomedical imaging and neuroscience. Koyejo co-founded the Black in AI organization and currently serves on its board.
More from the Same Authors
-
2021 : Probabilistic Performance Metric Elicitation »
Zachary Robertson · Hantao Zhang · Sanmi Koyejo -
2021 : Robust and Personalized Federated Learning with Spurious Features: an Adversarial Approach »
Xiaoyang Wang · Han Zhao · Klara Nahrstedt · Sanmi Koyejo -
2021 : RVFR: Robust Vertical Federated Learning via Feature Subspace Recovery »
Jing Liu · Chulin Xie · Krishnaram Kenthapadi · Sanmi Koyejo · Bo Li -
2021 : Secure Byzantine-Robust Distributed Learning via Clustering »
Raj Kiriti Velicheti · Sanmi Koyejo -
2021 : Exploiting Causal Chains for Domain Generalization »
Olawale Salaudeen · Sanmi Koyejo -
2022 : Metric Elicitation; Moving from Theory to Practice »
Safinah Ali · Sohini Upadhyay · Gaurush Hiranandani · Elena Glassman · Sanmi Koyejo -
2022 : The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence »
Brando Miranda · Patrick Yu · Yu-Xiong Wang · Sanmi Koyejo -
2022 : Batch Active Learning from the Perspective of Sparse Approximation »
Maohao Shen · Yibo Jacky Zhang · Bowen Jiang · Sanmi Koyejo -
2022 Spotlight: Lightning Talks 1A-4 »
Siwei Wang · Jing Liu · Nianqiao Ju · Shiqian Li · Eloïse Berthier · Muhammad Faaiz Taufiq · Arsene Fansi Tchango · Chen Liang · Chulin Xie · Jordan Awan · Jean-Francois Ton · Ziad Kobeissi · Wenguan Wang · Xinwang Liu · Kewen Wu · Rishab Goel · Jiaxu Miao · Suyuan Liu · Julien Martel · Ruobin Gong · Francis Bach · Chi Zhang · Rob Cornish · Sanmi Koyejo · Zhi Wen · Yee Whye Teh · Yi Yang · Jiaqi Jin · Bo Li · Yixin Zhu · Vinayak Rao · Wenxuan Tu · Gaetan Marceau Caron · Arnaud Doucet · Xinzhong Zhu · Joumana Ghosn · En Zhu -
2022 Spotlight: CoPur: Certifiably Robust Collaborative Inference via Feature Purification »
Jing Liu · Chulin Xie · Sanmi Koyejo · Bo Li -
2022 Poster: Diagnosing failures of fairness transfer across distribution shift in real-world medical settings »
Jessica Schrouff · Natalie Harris · Sanmi Koyejo · Ibrahim Alabdulmohsin · Eva Schnider · Krista Opsahl-Ong · Alexander Brown · Subhrajit Roy · Diana Mincu · Christina Chen · Awa Dieng · Yuan Liu · Vivek Natarajan · Alan Karthikesalingam · Katherine Heller · Silvia Chiappa · Alexander D'Amour -
2022 Poster: A Reduction to Binary Approach for Debiasing Multiclass Datasets »
Ibrahim Alabdulmohsin · Jessica Schrouff · Sanmi Koyejo -
2022 Poster: CoPur: Certifiably Robust Collaborative Inference via Feature Purification »
Jing Liu · Chulin Xie · Sanmi Koyejo · Bo Li -
2022 Poster: Fair Wrapping for Black-box Predictions »
Alexander Soen · Ibrahim Alabdulmohsin · Sanmi Koyejo · Yishay Mansour · Nyalleng Moorosi · Richard Nock · Ke Sun · Lexing Xie -
2022 Poster: A Nonconvex Framework for Structured Dynamic Covariance Recovery »
Katherine Tsai · Mladen Kolar · Sanmi Koyejo -
2020 Poster: Boundary thickness and robustness in learning models »
Yaoqing Yang · Rajiv Khanna · Yaodong Yu · Amir Gholami · Kurt Keutzer · Joseph Gonzalez · Kannan Ramchandran · Michael Mahoney -
2020 Poster: Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method »
Michal Derezinski · Rajiv Khanna · Michael Mahoney -
2020 Oral: Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method »
Michal Derezinski · Rajiv Khanna · Michael Mahoney -
2019 Tutorial: Representation Learning and Fairness »
Moustapha Cisse · Sanmi Koyejo