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Author Information
Jordan Boyd-Graber (University of Maryland)
Hal Daumé III (Microsoft Research & University of Maryland)
Hal Daumé III wields a professor appointment in Computer Science and Language Science at the University of Maryland, and spends time as a principal researcher in the machine learning group and fairness group at Microsoft Research in New York City. He and his wonderful advisees study questions related to how to get machines to become more adept at human language, by developing models and algorithms that allow them to learn from data. The two major questions that really drive their research these days are: (1) how can we get computers to learn language through natural interaction with people/users? and (2) how can we do this in a way that promotes fairness, transparency and explainability in the learned models?
He He (Stanford)
Mohit Iyyer (Allen Institute for Artificial Intelligence)
Pedro Rodriguez (University of Maryland at College Park)
I am a 3rd year PhD student in machine learning and natural language processing. I am a member of the CLIP lab at UMD, and advised by Jordan Boyd-Graber. My research interests include question answering, deep learning, and interpretable machine learning. I am looking for research internship opportunities for summer 2018.
More from the Same Authors
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2021 Spotlight: Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence »
Alexander Hoyle · Pranav Goel · Andrew Hian-Cheong · Denis Peskov · Jordan Boyd-Graber · Philip Resnik -
2021 : Poster: The Many Roles that Causal Reasoning Plays in Reasoning about Fairness in Machine Learning »
Irene Y Chen · Hal Daumé III · Solon Barocas -
2023 Poster: $\texttt{TACO}$: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning »
Ruijie Zheng · Xiyao Wang · Yanchao Sun · Shuang Ma · Jieyu Zhao · Huazhe Xu · Hal Daumé III · Furong Huang -
2023 Poster: ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition »
Aashaka Desai · Lauren Berger · Fyodor Minakov · Nessa Milano · Chinmay Singh · Kriston Pumphrey · Richard Ladner · Hal Daumé III · Alex X Lu · Naomi Caselli · Danielle Bragg -
2022 Workshop: HCAI@NeurIPS 2022, Human Centered AI »
Michael Muller · Plamen P Angelov · Hal Daumé III · Shion Guha · Q.Vera Liao · Nuria Oliver · David Piorkowski -
2021 : The Many Roles that Causal Reasoning Plays in Reasoning about Fairness in Machine Learning »
Irene Y Chen · Hal Daumé III · Solon Barocas -
2021 Poster: Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence »
Alexander Hoyle · Pranav Goel · Andrew Hian-Cheong · Denis Peskov · Jordan Boyd-Graber · Philip Resnik -
2020 : Showdown against trivia experts »
Jordan Boyd-Graber -
2019 Poster: Reinforcement Learning with Convex Constraints »
Sobhan Miryoosefi · Kianté Brantley · Hal Daumé III · Miro Dudik · Robert Schapire -
2019 Tutorial: Imitation Learning and its Application to Natural Language Generation »
Kyunghyun Cho · Hal Daumé III -
2018 Workshop: Wordplay: Reinforcement and Language Learning in Text-based Games »
Adam Trischler · Angeliki Lazaridou · Yonatan Bisk · Wendy Tay · Nate Kushman · Marc-Alexandre Côté · Alessandro Sordoni · Daniel Ricks · Tom Zahavy · Hal Daumé III -
2018 Poster: Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages »
Michelle Yuan · Benjamin Van Durme · Jordan Boyd-Graber -
2016 Workshop: Let's Discuss: Learning Methods for Dialogue »
Hal Daumé III · Paul Mineiro · Amanda Stent · Jason E Weston -
2016 Poster: A Credit Assignment Compiler for Joint Prediction »
Kai-Wei Chang · He He · Stephane Ross · Hal Daumé III · John Langford -
2015 Demonstration: Interactive Incremental Question Answering »
Jordan Boyd-Graber · Mohit Iyyer -
2014 Workshop: Second Workshop on Transfer and Multi-Task Learning: Theory meets Practice »
Urun Dogan · Tatiana Tommasi · Yoshua Bengio · Francesco Orabona · Marius Kloft · Andres Munoz · Gunnar Rätsch · Hal Daumé III · Mehryar Mohri · Xuezhi Wang · Daniel Hernández-lobato · Song Liu · Thomas Unterthiner · Pascal Germain · Vinay P Namboodiri · Michael Goetz · Christopher Berlind · Sigurd Spieckermann · Marta Soare · Yujia Li · Vitaly Kuznetsov · Wenzhao Lian · Daniele Calandriello · Emilie Morvant -
2014 Workshop: Representation and Learning Methods for Complex Outputs »
Richard Zemel · Dale Schuurmans · Kilian Q Weinberger · Yuhong Guo · Jia Deng · Francesco Dinuzzo · Hal Daumé III · Honglak Lee · Noah A Smith · Richard Sutton · Jiaqian YU · Vitaly Kuznetsov · Luke Vilnis · Hanchen Xiong · Calvin Murdock · Thomas Unterthiner · Jean-Francis Roy · Martin Renqiang Min · Hichem SAHBI · Fabio Massimo Zanzotto -
2014 Poster: Learning to Search in Branch and Bound Algorithms »
He He · Hal Daumé III · Jason Eisner -
2014 Poster: Learning a Concept Hierarchy from Multi-labeled Documents »
Viet-An Nguyen · Jordan Boyd-Graber · Philip Resnik · Jonathan Chang -
2013 Workshop: Topic Models: Computation, Application, and Evaluation »
David Mimno · Amr Ahmed · Jordan Boyd-Graber · Ankur Moitra · Hanna Wallach · Alexander Smola · David Blei · Anima Anandkumar -
2013 Poster: Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent »
Yuening Hu · Jordan Boyd-Graber · Hal Daumé III · Z. Irene Ying -
2013 Poster: Lexical and Hierarchical Topic Regression »
Viet-An Nguyen · Jordan Boyd-Graber · Philip Resnik -
2012 Poster: Imitation Learning by Coaching »
He He · Hal Daumé III · Jason Eisner -
2012 Poster: Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression »
Piyush Rai · Abhishek Kumar · Hal Daumé III -
2012 Poster: Learned Prioritization for Trading Off Accuracy and Speed »
Jiarong Jiang · Adam Teichert · Hal Daumé III · Jason Eisner -
2011 Poster: Message-Passing for Approximate MAP Inference with Latent Variables »
Jiarong Jiang · Piyush Rai · Hal Daumé III -
2011 Poster: Co-regularized Multi-view Spectral Clustering »
Abhishek Kumar · Piyush Rai · Hal Daumé III -
2010 Poster: Learning Multiple Tasks using Manifold Regularization »
Arvind Agarwal · Hal Daumé III · Samuel Gerber -
2010 Poster: Co-regularization Based Semi-supervised Domain Adaptation »
Hal Daumé III · Abhishek Kumar · Avishek Saha -
2009 Workshop: Applications for Topic Models: Text and Beyond »
David Blei · Jordan Boyd-Graber · Jonathan Chang · Katherine Heller · Hanna Wallach -
2009 Poster: Reading Tea Leaves: How Humans Interpret Topic Models »
Jonathan Chang · Jordan Boyd-Graber · Sean Gerrish · Chong Wang · David Blei -
2009 Poster: Multi-Label Prediction via Sparse Infinite CCA »
Piyush Rai · Hal Daumé III -
2009 Oral: Reading Tea Leaves: How Humans Interpret Topic Models »
Jonathan Chang · Jordan Boyd-Graber · Sean Gerrish · Chong Wang · David Blei -
2008 Poster: Syntactic Topic Models »
Jordan Boyd-Graber · David Blei -
2008 Poster: Nonparametric Bayesian Sparse Hierarchical Factor Modeling and Regression »
Piyush Rai · Hal Daumé III -
2008 Spotlight: Syntactic Topic Models »
Jordan Boyd-Graber · David Blei -
2007 Poster: Bayesian Agglomerative Clustering with Coalescents »
Yee Whye Teh · Hal Daumé III · Daniel Roy -
2007 Oral: Bayesian Agglomerative Clustering with Coalescents »
Yee Whye Teh · Hal Daumé III · Daniel Roy