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Machine learning systems based on minimizing average error have been shown to perform inconsistently across notable subsets of the data, which is not exposed by a low average error for the entire dataset. In consequential social and economic applications, where data represent people, this can lead to discrimination of underrepresented gender and ethnic groups. Distributionally Robust Optimization (DRO) seemingly addresses this problem by minimizing the worst expected risk across subpopulations. We establish theoretical results that clarify the relation between DRO and the optimization of the same loss averaged on an adequately weighted training dataset. A practical implication of our results is that neither DRO nor curating the training set should be construed as a complete solution for bias mitigation.
Author Information
Agnieszka Słowik (Department of Computer Science and Technology University of Cambridge)
Leon Bottou (Facebook AI Research)
Léon Bottou received a Diplôme from l'Ecole Polytechnique, Paris in 1987, a Magistère en Mathématiques Fondamentales et Appliquées et Informatiques from Ecole Normale Supérieure, Paris in 1988, and a PhD in Computer Science from Université de Paris-Sud in 1991. He joined AT&T Bell Labs from 1991 to 1992 and AT&T Labs from 1995 to 2002. Between 1992 and 1995 he was chairman of Neuristique in Paris, a small company pioneering machine learning for data mining applications. He has been with NEC Labs America in Princeton since 2002. Léon's primary research interest is machine learning. His contributions to this field address theory, algorithms and large scale applications. Léon's secondary research interest is data compression and coding. His best known contribution in this field is the DjVu document compression technology (http://www.djvu.org.) Léon published over 70 papers and is serving on the boards of JMLR and IEEE TPAMI. He also serves on the scientific advisory board of Kxen Inc .
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2021 : On the Relation between Distributionally Robust Optimization and Data Curation »
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2023 Poster: Birth of a Transformer: A Memory Viewpoint »
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2022 Poster: The Effects of Regularization and Data Augmentation are Class Dependent »
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2021 : Contributed talks in Session 4 (Zoom) »
Quanquan Gu · Agnieszka Słowik · Jacques Chen · Neha Wadia · Difan Zou -
2021 : Poster Session 1 (gather.town) »
Hamed Jalali · Robert Hönig · Maximus Mutschler · Manuel Madeira · Abdurakhmon Sadiev · Egor Shulgin · Alasdair Paren · Pascal Esser · Simon Roburin · Julius Kunze · Agnieszka Słowik · Frederik Benzing · Futong Liu · Hongyi Li · Ryotaro Mitsuboshi · Grigory Malinovsky · Jayadev Naram · Zhize Li · Igor Sokolov · Sharan Vaswani -
2019 Poster: Cold Case: The Lost MNIST Digits »
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2019 Spotlight: Cold Case: The Lost MNIST Digits »
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2018 Workshop: Causal Learning »
Martin Arjovsky · Christina Heinze-Deml · Anna Klimovskaia · Maxime Oquab · Leon Bottou · David Lopez-Paz -
2018 Workshop: Smooth Games Optimization and Machine Learning »
Simon Lacoste-Julien · Ioannis Mitliagkas · Gauthier Gidel · Vasilis Syrgkanis · Eva Tardos · Leon Bottou · Sebastian Nowozin -
2018 Poster: SING: Symbol-to-Instrument Neural Generator »
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2017 : Geometrical Insights for Unsupervised Learning »
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2017 : Looking for a Missing Signal »
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2016 : Welcome »
David Lopez-Paz · Alec Radford · Leon Bottou -
2016 Workshop: Adversarial Training »
David Lopez-Paz · Leon Bottou · Alec Radford -
2015 Workshop: Optimization for Machine Learning (OPT2015) »
Suvrit Sra · Alekh Agarwal · Leon Bottou · Sashank J. Reddi -
2014 Workshop: Learning Semantics »
Cedric Archambeau · Antoine Bordes · Leon Bottou · Chris J Burges · David Grangier -
2014 Workshop: Deep Learning and Representation Learning »
Andrew Y Ng · Yoshua Bengio · Adam Coates · Roland Memisevic · Sharanyan Chetlur · Geoffrey E Hinton · Shamim Nemati · Bryan Catanzaro · Surya Ganguli · Herbert Jaeger · Phil Blunsom · Leon Bottou · Volodymyr Mnih · Chen-Yu Lee · Rich M Schwartz -
2013 Workshop: NIPS 2013 Workshop on Causality: Large-scale Experiment Design and Inference of Causal Mechanisms »
Isabelle Guyon · Leon Bottou · Bernhard Schölkopf · Alexander Statnikov · Evelyne Viegas · james m robins -
2011 Workshop: Learning Semantics »
Antoine Bordes · Jason Weston · Ronan Collobert · Leon Bottou -
2007 Tutorial: Learning Using Many Examples »
Leon Bottou · Andrew W Moore -
2007 Poster: The Tradeoffs of Large Scale Learning »
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