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Author Information
Tom Mitchell (Carnegie Mellon University)
Jennifer Wortman Vaughan (Microsoft Research)

Jenn Wortman Vaughan is a Senior Principal Researcher at Microsoft Research, New York City. Her research background is in machine learning and algorithmic economics. She is especially interested in the interaction between people and AI, and has often studied this interaction in the context of prediction markets and other crowdsourcing systems. In recent years, she has turned her attention to human-centered approaches to transparency, interpretability, and fairness in machine learning as part of MSR's FATE group and co-chair of Microsoft’s Aether Working Group on Transparency. Jenn came to MSR in 2012 from UCLA, where she was an assistant professor in the computer science department. She completed her Ph.D. at the University of Pennsylvania in 2009, and subsequently spent a year as a Computing Innovation Fellow at Harvard. She is the recipient of Penn's 2009 Rubinoff dissertation award for innovative applications of computer technology, a National Science Foundation CAREER award, a Presidential Early Career Award for Scientists and Engineers (PECASE), and a handful of best paper awards. In her "spare" time, Jenn is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which has been held each year since 2006.
Sanjoy Dasgupta (UC San Diego)
Finale Doshi-Velez (Harvard)
Zachary Lipton (Carnegie Mellon University)
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2021 : Fairness:: Assessing Fairness in Practice: AI Teams’ Processes, Challenges, and Needs for Support »
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2020 : Batch RL Models Built for Validation »
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2020 : Panel »
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2020 Spotlight: Incorporating Interpretable Output Constraints in Bayesian Neural Networks »
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2020 Poster: A Unified View of Label Shift Estimation »
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2020 : Discussion Panel: Hugo Larochelle, Finale Doshi-Velez, Devi Parikh, Marc Deisenroth, Julien Mairal, Katja Hofmann, Phillip Isola, and Michael Bowling »
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2019 : Panel - The Role of Communication at Large: Aparna Lakshmiratan, Jason Yosinski, Been Kim, Surya Ganguli, Finale Doshi-Velez »
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2019 : Tom Mitchell - Understanding Neural Processes: Getting Beyond Where and When, to How »
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2019 : Invited talk #4 »
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2019 : Tom Mitchell »
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2019 : Finale Doshi-Velez: Combining Statistical methods with Human Input for Evaluation and Optimization in Batch Settings »
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2019 Poster: Learning Data Manipulation for Augmentation and Weighting »
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2019 Poster: An adaptive nearest neighbor rule for classification »
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2019 Spotlight: An adaptive nearest neighbor rule for classification »
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2019 Poster: Game Design for Eliciting Distinguishable Behavior »
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2018 : Invited Talk 1 »
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2018 : Finale Doshi-Velez »
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2018 Workshop: Learning by Instruction »
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2018 : Panel on research process »
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2018 : Finale Doshi-Velez »
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2018 : Zachary Lipton »
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2018 Poster: Human-in-the-Loop Interpretability Prior »
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2018 Spotlight: Human-in-the-Loop Interpretability Prior »
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2018 Poster: Representation Balancing MDPs for Off-policy Policy Evaluation »
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2018 Poster: Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems »
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2018 Poster: Learning from discriminative feature feedback »
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2018 Poster: Does mitigating ML's impact disparity require treatment disparity? »
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2017 : Panel Session »
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2017 : The Unfair Externalities of Exploration »
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2017 : Finale Doshi-Velez »
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2017 : Invited Talk: Learning from Limited Labeled Data (But a Lot of Unlabeled Data) »
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2017 : Automatic Model Selection in BNNs with Horseshoe Priors »
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2017 : NELL: Lessons and Future Directions »
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2017 : Poster spotlights »
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2017 : Coffee break and Poster Session I »
Nishith Khandwala · Steve Gallant · Gregory Way · Aniruddh Raghu · Li Shen · Aydan Gasimova · Alican Bozkurt · William Boag · Daniel Lopez-Martinez · Ulrich Bodenhofer · Samaneh Nasiri GhoshehBolagh · Michelle Guo · Christoph Kurz · Kirubin Pillay · Kimis Perros · George H Chen · Alexandre Yahi · Madhumita Sushil · Sanjay Purushotham · Elena Tutubalina · Tejpal Virdi · Marc-Andre Schulz · Samuel Weisenthal · Bharat Srikishan · Petar Veličković · Kartik Ahuja · Andrew Miller · Erin Craig · Disi Ji · Filip Dabek · Chloé Pou-Prom · Hejia Zhang · Janani Kalyanam · Wei-Hung Weng · Harish Bhat · Hugh Chen · Simon Kohl · Mingwu Gao · Tingting Zhu · Ming-Zher Poh · Iñigo Urteaga · Antoine Honoré · Alessandro De Palma · Maruan Al-Shedivat · Pranav Rajpurkar · Matthew McDermott · Vincent Chen · Yanan Sui · Yun-Geun Lee · Li-Fang Cheng · Chen Fang · Sibt ul Hussain · Cesare Furlanello · Zeev Waks · Hiba Chougrad · Hedvig Kjellstrom · Finale Doshi-Velez · Wolfgang Fruehwirt · Yanqing Zhang · Lily Hu · Junfang Chen · Sunho Park · Gatis Mikelsons · Jumana Dakka · Stephanie Hyland · yann chevaleyre · Hyunwoo Lee · Xavier Giro-i-Nieto · David Kale · Michael Hughes · Gabriel Erion · Rishab Mehra · William Zame · Stojan Trajanovski · Prithwish Chakraborty · Kelly Peterson · Muktabh Mayank Srivastava · Amy Jin · Heliodoro Tejeda Lemus · Priyadip Ray · Tamas Madl · Joseph Futoma · Enhao Gong · Syed Rameel Ahmad · Eric Lei · Ferdinand Legros -
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2017 : Invited talk: The Role of Explanation in Holding AIs Accountable »
Finale Doshi-Velez -
2017 Workshop: Learning in the Presence of Strategic Behavior »
Nika Haghtalab · Yishay Mansour · Tim Roughgarden · Vasilis Syrgkanis · Jennifer Wortman Vaughan -
2017 Poster: A Decomposition of Forecast Error in Prediction Markets »
Miro Dudik · Sebastien Lahaie · Ryan Rogers · Jennifer Wortman Vaughan -
2017 Poster: Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes »
Taylor Killian · Samuel Daulton · Finale Doshi-Velez · George Konidaris -
2017 Oral: Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes »
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2017 Poster: Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach »
Emmanouil Platanios · Hoifung Poon · Tom M Mitchell · Eric Horvitz -
2016 : BNNs for RL: A Success Story and Open Questions »
Finale Doshi-Velez -
2016 : Jennifer Wortman Vaughan: "The Communication Network Within the Crowd" »
Jennifer Wortman Vaughan -
2016 Tutorial: Crowdsourcing: Beyond Label Generation »
Jennifer Wortman Vaughan -
2015 Workshop: Machine Learning From and For Adaptive User Technologies: From Active Learning & Experimentation to Optimization & Personalization »
Joseph Jay Williams · Yasin Abbasi Yadkori · Finale Doshi-Velez -
2015 : Data Driven Phenotyping for Diseases »
Finale Doshi-Velez -
2015 Poster: Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction »
Been Kim · Julie A Shah · Finale Doshi-Velez -
2014 Workshop: NIPS’14 Workshop on Crowdsourcing and Machine Learning »
David Parkes · Denny Zhou · Chien-Ju Ho · Nihar Bhadresh Shah · Adish Singla · Jared Heyman · Edwin Simpson · Andreas Krause · Rafael Frongillo · Jennifer Wortman Vaughan · Panagiotis Papadimitriou · Damien Peters -
2014 Workshop: 4th Workshop on Automated Knowledge Base Construction (AKBC) »
Sameer Singh · Fabian M Suchanek · Sebastian Riedel · Partha Pratim Talukdar · Kevin Murphy · Christopher Ré · William Cohen · Tom Mitchell · Andrew McCallum · Jason E Weston · Ramanathan Guha · Boyan Onyshkevych · Hoifung Poon · Oren Etzioni · Ari Kobren · Arvind Neelakantan · Peter Clark -
2014 Workshop: NIPS Workshop on Transactional Machine Learning and E-Commerce »
David Parkes · David H Wolpert · Jennifer Wortman Vaughan · Jacob D Abernethy · Amos Storkey · Mark Reid · Ping Jin · Nihar Bhadresh Shah · Mehryar Mohri · Luis E Ortiz · Robin Hanson · Aaron Roth · Satyen Kale · Sebastien Lahaie -
2014 Poster: Incremental Clustering: The Case for Extra Clusters »
Margareta Ackerman · Sanjoy Dasgupta -
2014 Session: Oral Session 9 »
Jennifer Wortman Vaughan -
2014 Poster: Optimal rates for k-NN density and mode estimation »
Sanjoy Dasgupta · Samory Kpotufe -
2013 Workshop: Crowdsourcing: Theory, Algorithms and Applications »
Jennifer Wortman Vaughan · Greg Stoddard · Chien-Ju Ho · Adish Singla · Michael Bernstein · Devavrat Shah · Arpita Ghosh · Evgeniy Gabrilovich · Denny Zhou · Nikhil Devanur · Xi Chen · Alexander Ihler · Qiang Liu · Genevieve Patterson · Ashwinkumar Badanidiyuru Varadaraja · Hossein Azari Soufiani · Jacob Whitehill -
2013 Poster: Moment-based Uniform Deviation Bounds for $k$-means and Friends »
Matus J Telgarsky · Sanjoy Dasgupta -
2011 Workshop: 2nd Workshop on Computational Social Science and the Wisdom of Crowds »
Winter Mason · Jennifer Wortman Vaughan · Hanna Wallach -
2011 Workshop: Relations between machine learning problems - an approach to unify the field »
Robert Williamson · John Langford · Ulrike von Luxburg · Mark Reid · Jennifer Wortman Vaughan -
2010 Workshop: Computational Social Science and the Wisdom of Crowds »
Jennifer Wortman Vaughan · Hanna Wallach -
2009 Poster: Zero-shot Learning with Semantic Output Codes »
Mark M Palatucci · Dean Pomerleau · Geoffrey E Hinton · Tom Mitchell -
2008 Workshop: Parallel Implementations of Learning Algorithms: What have you done for me lately? »
Robert Thibadeau · Dan Hammerstrom · David S Touretzky · Tom Mitchell -
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Michael Kearns · Jinsong Tan · Jennifer Wortman Vaughan -
2007 Poster: Privacy-Preserving Belief Propagation and Sampling »
Michael Kearns · Jinsong Tan · Jennifer Wortman Vaughan -
2007 Poster: Learning Bounds for Domain Adaptation »
John Blitzer · Yacov Crammer · Alex Kulesza · Fernando Pereira · Jennifer Wortman Vaughan -
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John-Dylan Haynes · Tom Mitchell · Francisco Pereira -
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