Serena Wang

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All published works

Action	Title	Year	Authors
+ PDF Chat	Score Design for Multi-Criteria Incentivization	2024	Anmol Kabra Mina Karzand Tosca Lechner Nathan Srebro Serena Wang
+	Regularization Strategies for Quantile Regression	2021	Taman Narayan Serena Wang Kevin Robert Canini Maya R. Gupta
+	Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence	2021	Ghassen Jerfel Serena Wang Clara Fannjiang Katherine Heller Yi-An Ma Michael I. Jordan
+ PDF Chat	Finding Equilibrium in Multi-Agent Games with Payoff Uncertainty	2020	Wenshuo Guo Mihaela Curmei Serena Wang Benjamin Recht Michael I. Jordan
+ PDF Chat	Pairwise Fairness for Ranking and Regression	2020	Harikrishna Narasimhan Andrew Cotter Maya R. Gupta Serena Wang
+	Finding Equilibrium in Multi-Agent Games with Payoff Uncertainty	2020	Wenshuo Guo Mihaela Curmei Serena Wang Benjamin Recht Michael I. Jordan
+	Pairwise Fairness for Ranking and Regression	2019	Harikrishna Narasimhan Andrew Cotter Maya R. Gupta Serena Wang
+	Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals	2019	Andrew Cotter Heinrich Jiang Maya R. Gupta Serena Wang Taman Narayan Seungil You Karthik Sridharan
+	Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals	2018	Andrew Cotter Heinrich Jiang Serena Wang Taman Narayan Maya R. Gupta Seungil You Karthik Sridharan
+	Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints	2018	Andrew Cotter Maya R. Gupta Heinrich Jiang Nathan Srebro Karthik Sridharan Serena Wang Blake Woodworth Seungil You
+	Proxy Fairness	2018	Maya R. Gupta Andrew Cotter Mahdi Milani Fard Serena Wang
+	Interpretable Set Functions	2018	Andrew Cotter Maya R. Gupta Heinrich Jiang James Muller Taman Narayan Serena Wang Tao Zhu
+	Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization	2018	Serena Wang Maya R. Gupta Seungil You
+	Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints	2018	Andrew Cotter Maya R. Gupta Heinrich Jiang Nathan Srebro Karthik Sridharan Serena Wang Blake Woodworth Seungil You
+	Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals	2018	Andrew Cotter Heinrich Jiang Serena Wang Taman Narayan Maya R. Gupta Seungil You Karthik Sridharan
+	Proxy Fairness	2018	Maya R. Gupta Andrew Cotter Mahdi Milani Fard Serena Wang

Common Coauthors

Coauthor	Papers Together
Maya R. Gupta	12
Andrew Cotter	10
Seungil You	6
Heinrich Jiang	5
Karthik Sridharan	5
Taman Narayan	5
Nathan Srebro	3
Michael I. Jordan	3
Mahdi Milani Fard	2
Blake Woodworth	2
Harikrishna Narasimhan	2
Benjamin Recht	2
Mihaela Curmei	2
Heinrich Jiang	1
Yi-An Ma	1
Wenshuo Guo	1
Wenshuo Guo	1
Tao Zhu	1
Anmol Kabra	1
Tosca Lechner	1
Katherine Heller	1
Kevin Robert Canini	1
Ghassen Jerfel	1
Mina Karzand	1
Clara Fannjiang	1
James Muller	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Two-Player Games for Efficient Non-Convex Constrained Optimization	2018	Andrew Cotter Heinrich Jiang Karthik Sridharan	4
+	Equality of Opportunity in Supervised Learning	2016	Moritz Hardt Eric Price Nathan Srebro	2
+	Adam: A Method for Stochastic Optimization	2014	Diederik P. Kingma Jimmy Ba	2
+	Robust Optimization for Non-Convex Objectives	2017	Robert F. Chen Brendan Lucier Yaron Singer Vasilis Syrgkanis	2
+	Counterfactual Fairness	2017	Matt J. Kusner Joshua R. Loftus Chris Russell Ricardo Silva	2
+	Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness	2017	Michael Kearns Seth Neel Aaron Roth Zhiwei Steven Wu	2
+	Monotonic calibrated interpolated look-up tables	2016	Maya R. Gupta Andrew Cotter Jan Pfeifer Konstantin Voevodski Kevin Robert Canini Alexander Mangylov Wojciech Moczydłowski Alexander Van Esbroeck	2
+	Fairness of Exposure in Rankings	2018	Ashudeep Singh Thorsten Joachims	2
+	Satisfying Real-world Goals with Dataset Constraints	2016	Gabriel Goh Andrew Cotter Maya R. Gupta Michael P. Friedlander	2
+ PDF Chat	Reducing Disparate Exposure in Ranking: A Learning To Rank Approach	2018	Meike Zehlike Carlos Castillo	2
+ PDF Chat	Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification	2019	Daniel Borkan Lucas Dixon Jeffrey Sorensen Nithum Thain Lucy Vasserman	2
+ PDF Chat	Fairness through awareness	2012	Cynthia Dwork Moritz Hardt Toniann Pitassi Omer Reingold Richard S. Zemel	2
+	Bayesian Inference in Econometric Models Using Monte Carlo Integration	1989	John Geweke	1
+	Methods of Reducing Sample Size in Monte Carlo Computations	1953	Herman Kahn Andrew W. Marshall	1
+ PDF Chat	Simultaneous multiple non-crossing quantile regression estimation using kernel constraints	2011	Yanping Liu Yichao Wu	1
+	Quantile Curves without Crossing	1997	Xuming He	1
+	Classifying with confidence from incomplete information	2013	Nathan Parrish Hyrum S. Anderson Maya R. Gupta Dun Yu Hsiao	1
+ PDF Chat	A Neyman–Pearson Approach to Statistical Learning	2005	Clayton Scott Robert D. Nowak	1
+ PDF Chat	Minimum variance importance sampling<i>via</i>Population Monte Carlo	2007	Randal Douc Arnaud Guillin Jean‐Michel Marin Christian P. Robert	1
+ PDF Chat	Noncrossing quantile regression curve estimation	2010	Howard D. Bondell Brian J. Reich Huixia Wang	1
+	Matching pursuits with time-frequency dictionaries	1993	Stéphane Mallat Zhifeng Zhang	1
+	Sequential greedy approximation for certain convex optimization problems	2003	Tong Zhang	1
+	Stochastic variational inference	2013	Matthew D. Hoffman David M. Blei Chong Wang John Paisley	1
+	Nonparametric Quantile Estimation	2006	Ichiro Takeuchi Quoc V. Le Timothy D. Sears Alexander J. Smola	1
+	Pareto Smoothed Importance Sampling	2015	Aki Vehtari Andrew Gelman Jonah Gabry	1
+	Nonparametric Estimation under Shape Constraints	2014	Piet Groeneboom Geurt Jongbloed	1
+ PDF Chat	Joint quantile regression in vector-valued RKHSs	2016	Maxime Sangnier Olivier Fercoq Florence d’Alché–Buc	1
+	Lattice Regression	2009	Eric Garcia Maya R. Gupta	1
+	Spatial Point Processes	2011	Mark Huber	1
+	Boosting Variational Inference	2016	Fangjian Guo Xiangyu Wang Kai Fan Tamara Broderick David B. Dunson	1
+	Variational Boosting: Iteratively Refining Posterior Approximations	2016	Andrew C. Miller Nicholas J. Foti Ryan P. Adams	1
+	A Convex Framework for Fair Regression	2017	Richard A. Berk Hoda Heidari Shahin Jabbari Matthew Joseph Michael Kearns Jamie Morgenstern Seth Neel Aaron Roth	1
+ PDF Chat	FA*IR	2017	Meike Zehlike Francesco Bonchi Carlos Castillo Sara Hajian M. Megahed Ricardo Baeza‐Yates	1
+	Yes, but Did It Work?: Evaluating Variational Inference	2018	Yuling Yao Aki Vehtari Daniel Simpson Andrew Gelman	1
+	Empirical Risk Minimization under Fairness Constraints	2018	Michele Donini Luca Oneto Shai Ben-David John Shawe‐Taylor Massimiliano Pontil	1
+	The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric	2019	Nathan Kallus Angela Zhou	1
+	Policy Learning for Fairness in Ranking	2019	Ashudeep Singh Thorsten Joachims	1
+	Fair Regression: Quantitative Definitions and Reduction-based Algorithms	2019	Alekh Agarwal Miroslav Dudı́k Zhiwei Steven Wu	1
+	Fairness Constraints: Mechanisms for Fair Classification	2015	Muhammad Bilal Zafar Isabel Valera Manuel Gomez-Rodriguez Krishna P. Gummadi	1
+	A Contrastive Divergence for Combining Variational Inference and MCMC	2019	Francisco J. R. Ruiz Michalis K. Titsias	1
+	Fair Regression: Quantitative Definitions and Reduction-based Algorithms	2019	Alekh Agarwal Miroslav Dudík Zhiwei Steven Wu	1
+	Deep Sets	2017	Manzil Zaheer Satwik Kottur Siamak Ravanbakhsh Barnabás Póczos Ruslan Salakhutdinov Alexander J. Smola	1
+	Fair quantile regression	2019	Dana Yang John Lafferty David Pollard	1
+	Variational Inference via $\chi$ Upper Bound Minimization	2017	Adji B. Dieng Dustin Tran Rajesh Ranganath John Paisley David M. Blei	1
+ PDF Chat	The sample size required in importance sampling	2018	Sourav Chatterjee Persi Diaconis	1
+	Empirical Risk Minimization Under Fairness Constraints	2018	Michele Donini Luca Oneto Shai Ben-David John Shawe‐Taylor Massimiliano Pontil	1
+	Ranking with Fairness Constraints	2018	L. Elisa Celis Damian Straszak Nisheeth K. Vishnoi	1
+	Pruning Random Forests for Prediction on a Budget	2016	Nan Feng Joseph Wang Venkatesh Saligrama	1
+	Multiagent Evaluation under Incomplete Information	2019	Mark Rowland Shayegan Omidshafiei Karl Tuyls Julien Pérolat Michal Vaľko Georgios Piliouras Rémi Munos	1
+	Policy Learning for Fairness in Ranking	2019	Ashudeep Singh Thorsten Joachims	1