David Madras

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All published works

Action	Title	Year	Authors
+ PDF Chat	Auto-Evaluation with Few Labels through Post-hoc Regression	2024	Benjamin Eyre David Madras
+ PDF Chat	Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images	2024	Hansa Srinivasan Candice Schumann Aradhana Sinha David Madras Gbolahan O. Olanubi Alex Beutel Susanna Ricco Jilin Chen
+ PDF Chat	Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context	2024	Gemini Team Machel Reid Nikolay Savinov Denis Teplyashin Dmitry Lepikhin Timothy Lillicrap Jean-baptiste Alayrac Radu Soricut Angeliki Lazaridou
+	Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images	2024	Hansa Srinivasan Candice Schumann Aradhana Sinha David Madras Gbolahan O. Olanubi Alex Beutel Susanna Ricco Jilin Chen
+	Gemini: A Family of Highly Capable Multimodal Models	2023	Gemini Team Rohan Anil Sebastian Borgeaud Jean-Baptiste Alayrac Jiahui Yu Radu Soricut Johan Schalkwyk Andrew M. Dai Anja Hauth Katie Millican
+	Learning and Forgetting Unsafe Examples in Large Language Models	2023	Jiachen Zhao Zhun Deng David Madras James Zou Mengye Ren
+	Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift	2023	Benjamin Eyre Elliot Creager David Madras Vardan Papyan Richard S. Zemel
+	Identifying and Benchmarking Natural Out-of-Context Prediction Problems	2021	David Madras Richard S. Zemel
+	Identifying and Benchmarking Natural Out-of-Context Prediction Problems	2021	David Madras Richard S. Zemel
+	Identifying and Benchmarking Natural Out-of-Context Prediction Problems	2021	David Madras Richard S. Zemel
+	Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification	2020	Robert Adragna Elliot Creager David Madras Richard S. Zemel
+	Causal Modeling for Fairness In Dynamical Systems	2020	Elliot Creager David Madras Toniann Pitassi Richard S. Zemel
+	Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification	2020	Robert Adragna Elliot Creager David Madras Richard S. Zemel
+	Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data	2020	Sindy Löwe David Madras Richard S. Zemel Max Welling
+	Detecting Extrapolation with Local Ensembles	2019	David Madras James Atwood Alex D’Amour
+	Fairness through Causal Awareness	2019	David Madras Elliot Creager Toniann Pitassi Richard S. Zemel
+	Flexibly Fair Representation Learning by Disentanglement	2019	Elliot Creager David Madras Jörn-Henrik Jacobsen Marissa A. Weis Kevin Swersky Toniann Pitassi Richard S. Zemel
+	Causal Modeling for Fairness in Dynamical Systems	2019	Elliot Creager David Madras Toniann Pitassi Richard S. Zemel
+	Detecting Underspecification with Local Ensembles	2019	David Madras James Atwood Alex D’Amour
+	Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data.	2018	David Madras Elliot Creager Toniann Pitassi Richard S. Zemel
+	Learning Adversarially Fair and Transferable Representations	2018	David Madras Elliot Creager Toniann Pitassi Richard S. Zemel
+	Predict Responsibly: Increasing Fairness by Learning to Defer	2018	David Madras Toniann Pitassi Richard S. Zemel
+	Learning Adversarially Fair and Transferable Representations	2018	David Madras Elliot Creager Toniann Pitassi Richard S. Zemel
+	Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data	2018	David Madras Elliot Creager Toniann Pitassi Richard S. Zemel
+	Predict Responsibly: Increasing Fairness by Learning To Defer	2017	David Madras Toniann Pitassi Richard S. Zemel
+	Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer	2017	David Madras Toniann Pitassi Richard S. Zemel
+	Change-point Detection Methods for Body-Worn Video	2016	Stephanie Allen David Madras Ye Ye Greg Zanotti
+	Change-point Detection Methods for Body-Worn Video	2016	Stephanie Allen David Madras Ye Ye Greg Zanotti

Common Coauthors

Coauthor	Papers Together
Richard S. Zemel	18
Elliot Creager	11
Toniann Pitassi	11
Jilin Chen	4
Hansa Srinivasan	3
Jessica Landon	2
Anudhyan Boral	2
Nimesh Ghelani	2
Vincent J. Hellendoorn	2
Mukund Sundararajan	2
Laura Knight	2
Disha Shrivastava	2
Sanjay Ghemawat	2
Maxim Krikun	2
Alex Tomala	2
Paul Komarek	2
Parker Schuh	2
Andrew Brock	2
Michael Sharman	2
Jack W. Rae	2
Mantas Pajarskas	2
Arthur Guez	2
Josip Djolonga	2
Richard Tanburn	2
Komal Jalan	2
Vittorio Selo	2
Nina Martin	2
Balaji Lakshminarayanan	2
Shuo-Yiin Chang	2
Jeremy Wiesner	2
Sergey Brin	2
Abhanshu Sharma	2
Adam Paszke	2
Nithya Attaluri	2
Axel Stjerngren	2
Raphaël Lopez Kaufman	2
Demis Hassabis	2
Phil Crone	2
Ricardo Aguilar	2
Ken Franko	2
Gbolahan O. Olanubi	2
James Molloy	2
David F. Steiner	2
Oriol Vinyals	2
Remi Crocker	2
Olivier Dousse	2
Tianqi Liu	2
Yonghui Wu	2
Devendra Singh Sachan	2
Thibault Sottiaux	2

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Counterfactual Fairness	2017	Matt J. Kusner Joshua R. Loftus Chris Russell Ricardo Silva	4
+	Adam: A Method for Stochastic Optimization	2014	Diederik P. Kingma Jimmy Ba	4
+	Equality of Opportunity in Supervised Learning	2016	Moritz Hardt Eric Price Nathan Srebro	3
+	Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments	2017	Alexandra Chouldechova	3
+ PDF Chat	Fair Inference on Outcomes	2018	Razieh Nabi Ilya Shpitser	3
+ PDF Chat	Fairness through awareness	2012	Cynthia Dwork Moritz Hardt Toniann Pitassi Omer Reingold Richard S. Zemel	3
+	Causal Inference Using Potential Outcomes	2005	Donald B. Rubin	3
+	Avoiding Discrimination through Causal Reasoning	2017	Niki Kilbertus Mateo Rojas-Carulla Giambattista Parascandolo Moritz Hardt Dominik Janzing Bernhard Schölkopf	3
+	Equality of Opportunity in Classification: A Causal Approach	2018	Junzhe Zhang Elias Bareinboim	2
+	On the (im)possibility of fairness	2016	Sorelle A. Friedler Carlos Scheidegger Suresh Venkatasubramanian	2
+ PDF Chat	To Predict and Serve?	2016	Kristian Lum William Isaac	2
+	Stochastic Backpropagation and Approximate Inference in Deep Generative Models	2014	Danilo Jimenez Rezende Shakir Mohamed Daan Wierstra	2
+	Censoring Representations with an Adversary	2015	Harrison Edwards Amos Storkey	2
+ PDF Chat	Inherent Tradeoffs in the Fair Determination of Risk Scores	2023	Manish Raghavan	2
+	Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls	2009	Jonathan A C Sterne Ian R. White John B. Carlin Michael Spratt Patrick Royston Michael G. Kenward Angela Wood James R. Carpenter	2
+	Inference and missing data	1976	Donald B. Rubin	2
+	Bayesian Nonparametric Modeling for Causal Inference	2010	Jennifer Hill	2
+	Towards a Neural Statistician	2016	Harrison Edwards Amos Storkey	2
+	Semi-Amortized Variational Autoencoders	2018	Yoon Kim Sam Wiseman Andrew C. Miller David Sontag Alexander M. Rush	2
+	Flexible Imputation of Missing Data	2012	Stef van Buuren	2
+	On the Causal Interpretation of Race in Regressions Adjusting for Confounding and Mediating Variables	2014	Tyler J. VanderWeele Whitney R. Robinson	2
+	Learning Representations for Counterfactual Inference	2016	Fredrik Johansson Uri Shalit David Sontag	2
+	On Fairness and Calibration	2017	Geoff Pleiss Manish Raghavan Felix Wu Jon Kleinberg Kilian Q. Weinberger	2
+	Fairness Without Demographics in Repeated Loss Minimization	2018	Tatsunori Hashimoto Megha Srivastava Hongseok Namkoong Percy Liang	2
+ PDF Chat	The Selective Labels Problem	2017	Himabindu Lakkaraju Jon Kleinberg Jure Leskovec Jens Ludwig Sendhil Mullainathan	2
+	Scikit-learn: Machine Learning in Python	2012	Fabián Pedregosa Gaël Varoquaux Alexandre Gramfort Vincent Michel Bertrand Thirion Olivier Grisel Mathieu Blondel Peter Prettenhofer Ron J. Weiss Vincent Dubourg	2
+	Statistics and Causal Inference	1986	Paul W. Holland	2
+	Equality of Opportunity in Supervised Learning	2016	Moritz Hardt Eric Price Nathan Srebro	2
+	Doubly Robust Estimation in Missing Data and Causal Inference Models	2005	Heejung Bang James M. Robins	1
+ PDF Chat	Max-Sum diversification, monotone submodular functions and dynamic updates	2012	Allan Borodin Hyun Chul Lee Yuli Ye	1
+ PDF Chat	Learning Fine-Grained Image Similarity with Deep Ranking	2014	Jiang Wang Yang Song Thomas Leung Chuck Rosenberg Jingbin Wang James Philbin Bo Chen Ying Wu	1
+	Semi-supervised Learning with Deep Generative Models	2014	Diederik P. Kingma Shakir Mohamed Danilo Jimenez Rezende Max Welling	1
+	On the Consistency Rule in Causal Inference	2010	Judea Pearl	1
+	Identifiability and Exchangeability for Direct and Indirect Effects	1992	James M. Robins Sander Greenland	1
+	Domain-Adversarial Neural Networks	2014	Hana Ajakan Pascal Germain Hugo Larochelle François Laviolette Mario Marchand	1
+	Avoiding Discrimination through Causal Reasoning	2017	Niki Kilbertus Mateo Rojas-Carulla Giambattista Parascandolo Moritz Hardt Dominik Janzing Bernhard Schölkopf	1
+	Optimized Data Pre-Processing for Discrimination Prevention	2017	Flávio P. Calmon Dennis Wei Karthikeyan Natesan Ramamurthy Kush R. Varshney	1
+ PDF Chat	Deep learning in neural networks: An overview	2014	Jürgen Schmidhuber	1
+ PDF Chat	Deep Learning Face Attributes in the Wild	2015	Ziwei Liu Ping Luo Xiaogang Wang Xiaoou Tang	1
+	An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process	1972	Leonard E. Baum	1
+	Semantic Segmentation using Adversarial Networks	2016	Pauline Luc Camille Couprie Soumith Chintala Jakob Verbeek	1
+	Fair prediction with disparate impact: A study of bias in recidivism prediction instruments	2016	Alexandra Chouldechova	1
+	Model Criticism for Bayesian Causal Inference	2016	Dustin Tran Francisco J. R. Ruiz Susan Athey David M. Blei	1
+	Doubly Robust Policy Evaluation and Learning	2011	Miroslav Dudík John Langford Lihong Li	1
+ PDF Chat	Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment	2017	Muhammad Bilal Zafar Isabel Valera Manuel Gomez-Rodriguez Krishna P. Gummadi	1
+	Understanding Black-box Predictions via Influence Functions	2017	Pang Wei Koh Percy Liang	1
+ PDF Chat	Least angle regression	2004	Bradley Efron Trevor Hastie Iain M. Johnstone Robert Tibshirani	1
+	Causal Effect Inference with Deep Latent-Variable Models	2017	Christos Louizos Uri Shalit Joris M. Mooij David Sontag Richard S. Zemel Max Welling	1
+ PDF Chat	Domain-Adversarial Training of Neural Networks	2017	Yaroslav Ganin Evgeniya Ustinova Hana Ajakan Pascal Germain Hugo Larochelle François Laviolette Mario Marchand Victor Lempitsky	1
+	Deep Learning using Linear Support Vector Machines	2013	Yichuan Tang	1