Hai Qian

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All published works

Action	Title	Year	Authors
+	Emergent invariance and scaling properties in the collective return dynamics of a stock market	2022	Hideyuki Miyahara Hai Qian Pavan Holur Vwani Roychowdhury
+	Interpretable Ranking with Generalized Additive Models	2021	Honglei Zhuang Xuanhui Wang Michael Bendersky Alexander Grushetsky Yonghui Wu Petr Mitrichev Ethan Sterling Nathan Bell Walker Ravina Hai Qian
+	Interpretable Learning-to-Rank with Generalized Additive Models	2020	Honglei Zhuang Xuanhui Wang Michael Bendersky Alexander Grushetsky Yonghui Wu Petr Mitrichev Ethan Sterling Nathan Bell Walker Ravina Hai Qian
+	Toward a better trade-off between performance and fairness with kernel-based distribution matching	2019	Flavien Prost Hai Qian Qiuwen Chen Ed H. Jilin Chen Alex Beutel
+	Fairness in Recommendation Ranking through Pairwise Comparisons	2019	Alex Beutel Jilin Chen Tulsee Doshi Hai Qian Li Wei Yi Wu Lukasz Heldt Zhe Zhao Lichan Hong Ed H.
+	Fairness in Recommendation Ranking through Pairwise Comparisons	2019	Alex Beutel Jilin Chen Tulsee Doshi Hai Qian Li Wei Yi Wu Lukasz Heldt Zhe Zhao Lichan Hong Ed H.
+	Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements	2019	Alex Beutel Jilin Chen Tulsee Doshi Hai Qian Allison Woodruff Christine Luu Pierre Kreitmann Jonathan Bischof Ed H.
+	Transfer of Machine Learning Fairness across Domains	2019	Candice Schumann Xuezhi Wang Alex Beutel Jilin Chen Hai Qian Ed H.
+	Toward a better trade-off between performance and fairness with kernel-based distribution matching	2019	Flavien Prost Hai Qian Qiuwen Chen Ed H. Jilin Chen Alex Beutel
+	Fairness in Recommendation Ranking through Pairwise Comparisons	2019	Alex Beutel Jilin Chen Tulsee Doshi Hai Qian Li Wei Yi Wu Lukasz Heldt Zhe Zhao Lichan Hong Ed H.

Common Coauthors

Coauthor	Papers Together
Alex Beutel	7
Ed H.	7
Jilin Chen	7
Tulsee Doshi	4
Lukasz Heldt	3
Cristos Goodrow	3
Lichan Hong	3
Michael Bendersky	2
Yonghui Wu	2
Honglei Zhuang	2
Alexander Grushetsky	2
Flavien Prost	2
Xuanhui Wang	2
Petr Mitrichev	2
Walker Ravina	2
Zhe Zhao	2
Ethan Sterling	2
Li Wei	2
Qiuwen Chen	2
Yi Wu	2
Nathan Bell	2
Pavan Holur	1
Christine Luu	1
Pierre Kreitmann	1
Xuezhi Wang	1
Candice Schumann	1
Vwani Roychowdhury	1
Allison Woodruff	1
Jonathan Bischof	1
Hideyuki Miyahara	1
Zhe Zhao	1
Yi Wu	1
Li Wei	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Mitigating Unwanted Biases with Adversarial Learning	2018	Brian Hu Zhang Blake Lemoine Margaret Mitchell	5
+	Learning Adversarially Fair and Transferable Representations	2018	David Madras Elliot Creager Toniann Pitassi Richard S. Zemel	5
+	Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations	2017	Alex Beutel Ed H. Jilin Chen Zhe Zhao	4
+	Equality of Opportunity in Supervised Learning	2016	Moritz Hardt Eric Price Nathan Srebro	4
+ PDF Chat	Fairness through awareness	2012	Cynthia Dwork Moritz Hardt Toniann Pitassi Omer Reingold Richard S. Zemel	4
+	On Fairness and Calibration	2017	Geoff Pleiss Manish Raghavan Felix Wu Jon Kleinberg Kilian Q. Weinberger	3
+	On conditional parity as a notion of non-discrimination in machine learning	2017	Ya’acov Ritov Yuekai Sun Ruofei Zhao	3
+	Fairness Constraints: Mechanisms for Fair Classification	2015	Muhammad Bilal Zafar Isabel Valera Manuel Gomez-Rodriguez Krishna P. Gummadi	3
+ 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	Assessing calibration of prognostic risk scores	2013	Cynthia S. Crowson Elizabeth J. Atkinson Terry M. Therneau	2
+	Generalized Additive Models	1986	Trevor Hastie Robert Tibshirani	2
+ PDF Chat	FA*IR	2017	Meike Zehlike Francesco Bonchi Carlos Castillo Sara Hajian M. Megahed Ricardo Baeza‐Yates	2
+ PDF Chat	Component selection and smoothing in multivariate nonparametric regression	2006	Yi Lin Hao Helen Zhang	2
+	Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness	2017	Michael Kearns Seth Neel Aaron Roth Zhiwei Steven Wu	2
+ PDF Chat	Axiomatic Interpretability for Multiclass Additive Models	2019	Xuezhou Zhang Sarah Tan Paul Koch Yin Lou Urszula Chajewska Rich Caruana	2
+	Censoring Representations with an Adversary	2015	Harrison Edwards Amos Storkey	2
+	A comparison of methods for the fitting of generalized additive models	2007	Harald Binder Gerhard Tutz	2
+	Fairness of Exposure in Rankings	2018	Ashudeep Singh Thorsten Joachims	2
+	Unbiased Comparative Evaluation of Ranking Functions	2016	Tobias Schnabel Adith Swaminathan Peter I. Frazier Thorsten Joachims	2
+ PDF Chat	High-dimensional additive modeling	2009	Lukas Meier Sara van de Geer Peter Bühlmann	2
+	Unbiased Learning-to-Rank with Biased Feedback	2017	Thorsten Joachims Adith Swaminathan Tobias Schnabel	2
+	Penalizing Unfairness in Binary Classification	2017	Yahav Bechavod Katrina Ligett	2
+	Domain Separation Networks	2016	Konstantinos Bousmalis George Trigeorgis Nathan Silberman Dilip Krishnan Dumitru Erhan	2
+	Beyond Sparsity: Tree Regularization of Deep Models for Interpretability	2017	Mike Wu Michael C. Hughes Sonali Parbhoo Maurizio Zazzi Volker Röth Finale Doshi‐Velez	2
+ PDF Chat	Inherent Tradeoffs in the Fair Determination of Risk Scores	2023	Manish Raghavan	2
+ PDF Chat	Techniques for interpretable machine learning	2019	Mengnan Du Ninghao Liu Xia Hu	2
+ 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	2
+ PDF Chat	Exploring author gender in book rating and recommendation	2018	Michael D. Ekstrand Mucun Tian Mohammed R. Imran Kazi Hoda Mehrpouyan Daniel Kluver	2
+	Estimating Position Bias without Intrusive Interventions	2019	Aman Agarwal Ivan Zaitsev Xuanhui Wang Cheng Li Marc Najork Thorsten Joachims	2
+ PDF Chat	Degenerate Feedback Loops in Recommender Systems	2019	Ray Jiang Silvia Chiappa Tor Lattimore András György Pushmeet Kohli	2
+ PDF Chat	Sparse Additive Models	2009	Pradeep Ravikumar John Lafferty Han Liu Larry Wasserman	2
+	Learning Fair Classifiers	2015	Muhammad Bilal Zafar Isabel Valera Manuel Gomez-Rodriguez Krishna P. Gummadi	2
+ PDF Chat	Sparse Partially Linear Additive Models	2015	Yin Lou Jacob Bien Rich Caruana Johannes Gehrke	2
+	Fairness-Aware Tensor-Based Recommendation	2018	Ziwei Zhu Xia Hu James Caverlee	2
+	The Frontiers of Fairness in Machine Learning	2018	Alexandra Chouldechova Aaron Roth	2
+	TF-Ranking	2019	Rama Kumar Pasumarthi Sebastian Bruch Xuanhui Wang Cheng Li Michael Bendersky Marc Najork Jan Pfeifer Nadav Golbandi Rohan Anil S. Wolf	2
+	Learning Transferable Features with Deep Adaptation Networks	2015	Mingsheng Long Yue Cao Jianmin Wang Michael I. Jordan	2
+	Neural Collaborative Filtering	2017	Xiangnan He Lizi Liao Hanwang Zhang Liqiang Nie Xia Hu Tat‐Seng Chua	2
+	Learning Important Features Through Propagating Activation Differences	2017	Avanti Shrikumar Peyton Greenside Anshul Kundaje	2
+	Equality of Opportunity in Supervised Learning	2016	Moritz Hardt Eric Price Nathan Srebro	1
+	Is Attention Interpretable?	2019	Sofia Serrano Noah A. Smith	1
+	"Why Should I Trust You?": Explaining the Predictions of Any Classifier	2016	Marco Túlio Ribeiro Sameer Singh Carlos Guestrin	1
+	Top-K Off-Policy Correction for a REINFORCE Recommender System	2018	Minmin Chen Alex Beutel Paul Covington Sagar Jain Francois Belletti Ed H.	1
+	Recommendations as Treatments: Debiasing Learning and Evaluation	2016	Tobias Schnabel Adith Swaminathan Ashudeep Singh Navin Chandak Thorsten Joachims	1
+	Domain Separation Networks	2016	Konstantinos Bousmalis George Trigeorgis Nathan Silberman Dilip Krishnan Dumitru Erhan	1
+	A Unified Approach to Interpreting Model Predictions	2017	Scott Lundberg Su‐In Lee	1
+	Censoring Representations with an Adversary	2015	Harrison Edwards Amos Storkey	1
+	Avoiding Discrimination through Causal Reasoning	2017	Niki Kilbertus Mateo Rojas-Carulla Giambattista Parascandolo Moritz Hardt Dominik Janzing Bernhard Schölkopf	1
+ PDF Chat	Explaining Explanations: An Overview of Interpretability of Machine Learning	2018	Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal	1
+	Towards Robust and Privacy-preserving Text Representations	2018	Yitong Li Timothy Baldwin Trevor Cohn	1