Hal Daumé III - cv + bio

CV		Bio
		Hal Daumé is a Volpi-Cupal endowed Professor of Computer Science and Language Science at the University of Maryland, where he leads TRAILS , an NSF & NIST-funded institute on Trustworthy AI; he is also a Senior Principal Researcher at Microsoft Research NYC. His research focus is on developing natural language processing systems that interact naturally with people, promote their self-efficacy, while mitigating societal harms. Together with his students and colleagues, he has received several awards, including best paper at AACL 2022, ACL 2018, NAACL 2016, CEAS 2011 and ECML 2009, test of time award at ACL 2022 (and nomination at ACL 2017), and best demo at NeurIPS 2015. He has been program chair for ICML 2020 (together with Aarti Singh) and for NAACL 2013 (together with Katrin Kirchhoff), and he was an inaugural diversity and inclusion co-chair at NeurIPS 2018 (with Katherine Heller). When not sciencing and teaching, he spends most of his time climbing, yogaing, cooking, backpacking, skiing, and biking.

Contact Information:

Hal Daumé III
Computer Science
University of Maryland
Iribe Center #4134
College Park, MD 20742

(o) 301-405-1073
(email) me _AT_ hal3 _DOT_ name
(skype) haldaume3
(twitter) @haldaume3

Representative Publications:

Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning
Khanh Nguyen and Hal Daumé III
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019
[Abstract] [BibTeX] [Code/Data]

Mobile agents that can leverage help from humans can potentially accomplish more complex tasks than they could entirely on their own. We develop "Help, Anna!" (HANNA), an interactive photo-realistic simulator in which an agent fulfills object-finding tasks by requesting and interpreting natural languageand-vision assistance. An agent solving tasks in a HANNA environment can leverage simulated human assistants, called ANNA (Automatic Natural Navigation Assistants), which, upon request, provide natural language and visual instructions to direct the agent towards the goals. To address the HANNA problem, we develop a memory-augmented neural agent that hierarchically models multiple levels of decision-making, and an imitation learning algorithm that teaches the agent to avoid repeating past mistakes while simultaneously predicting its own chances of making future progress. Empirically, our approach is able to ask for help more effectively than competitive baselines and, thus, attains higher task success rate on both previously seen and previously unseen environments. We publicly release code and data at this https URL .

@inproceedings{daume19hanna, title = {Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning}, author = {Khanh Nguyen and me}, booktitle = {Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2019}, url = {http://hal3.name/docs/#daume19hanna}, link = {https://github.com/khanhptnk/hanna}, video = {}, }

Reinforcement Learning with Convex Constraints
Sobhan Miryoosefi, Kianté Brantley, Hal Daumé III, Miroslav Dudík and Robert Schapire
NeurIPS, 2019
[Abstract] [BibTeX]

In standard reinforcement learning (RL), a learning agent seeks to optimize the overall reward. However, many key aspects of a desired behavior are more naturally expressed as constraints. For instance, the designer may want to limit the use of unsafe actions, increase the diversity of trajectories to enable exploration, or approximate expert trajectories when rewards are sparse. In this paper, we propose an algorithmic scheme that can handle a wide class of constraints in RL tasks, specifically, any constraints that require expected values of some vector measurements (such as the use of an action) to lie in a convex set. This captures previously studied constraints (such as safety and proximity to an expert), but also enables new classes of constraints (such as diversity). Our approach comes with rigorous theoretical guarantees and only relies on the ability to approximately solve standard RL tasks. As a result, it can be easily adapted to work with any model-free or model-based RL algorithm. In our experiments, we show that it matches previous algorithms that enforce safety via constraints, but can also enforce new properties that these algorithms cannot incorporate, such as diversity.

@inproceedings{daume19convexrl, title = {Reinforcement Learning with Convex Constraints}, author = {Sobhan Miryoosefi and Kiant\'e Brantley and Hal {Daum\'e III} and Miroslav Dud\'ik and Robert Schapire}, booktitle = {NeurIPS}, year = {2019}, url = {http://hal3.name/docs/#daume19convexrl}, }

Improving fairness in machine learning systems: What do industry practitioners need?
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miroslav Dudík and Hanna Wallach
CHI, 2019
[Abstract] [BibTeX]

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.

@inproceedings{daume19fairness, title = {Improving fairness in machine learning systems: What do industry practitioners need?}, author = {Kenneth Holstein and Jennifer Wortman Vaughan and Hal {Daum\'e III} and Miroslav Dud\'ik and Hanna Wallach}, booktitle = {CHI}, year = {2019}, url = {http://hal3.name/docs/#daume19fairness}, }

Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information
Sudha Rao and Hal Daumé III
Conference of the Association for Computational Linguistics (ACL), 2018
🏆 Best Paper Award
[Abstract] [BibTeX]

Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this work, we build a neural network model for the task of ranking clariﬁcation questions. Our model is inspired by the idea of expected value of perfect information: a good question is one whose expected answer will be useful. We study this problem using data from StackExchange, a plentiful online resource in which people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We create a dataset of clariﬁcation questions consisting of ∼77K posts paired with a clariﬁcation question (and answer) from three domains of StackExchange: askubuntu, unix and superuser. We evaluate our model on 500 samples of this dataset against expert human judgments and demonstrate signiﬁcant improvements over controlled baselines.

@inproceedings{daume18clarification, title = {Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information}, author = {Sudha Rao and me}, booktitle = {Proceedings of the Conference of the Association for Computational Linguistics (ACL)}, year = {2018}, url = {http://hal3.name/docs/#daume18clarification}, }

Datasheets for Datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III and Kate Crawford
arxiv, 2018
[Abstract] [BibTeX]

Currently there is no standard way to identify how a dataset was created, and what characteristics, motivations, and potential skews it represents. To begin to address this issue, we propose the concept of a datasheet for datasets, a short document to accompany public datasets, commercial APIs, and pretrained models. The goal of this proposal is to enable better communication between dataset creators and users, and help the AI community move toward greater transparency and accountability. By analogy, in computer hardware, it has become industry standard to accompany everything from the simplest components (e.g., resistors), to the most complex microprocessor chips, with datasheets detailing standard operating characteristics, test results, recommended usage, and other information. We outline some of the questions a datasheet for datasets should answer. These questions focus on when, where, and how the training data was gathered, its recommended use cases, and, in the case of human-centric datasets, information regarding the subjects’ demographics and consent as applicable. We develop prototypes of datasheets for two well-known datasets: Labeled Faces in The Wild [33] and the Pang & Lee Polarity Dataset [45].

@inproceedings{daume18datasheets, title = {Datasheets for Datasets}, author = {Timnit Gebru and Jamie Morgenstern and Briana Vecchione and Jennifer Wortman Vaughan and Hanna Wallach and Hal {Daum\'e III} and Kate Crawford}, booktitle = {arxiv}, year = {2018}, url = {http://hal3.name/docs/#daume18datasheets}, }

Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation
He He, Jordan Boyd-Graber and Hal Daumé III
Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2016
[Abstract] [BibTeX]

Computational approaches to simultaneous in- terpretation are stymied by how little we know about the tactics human interpreters use. We produce a parallel corpus of translated and si- multaneously interpreted text and study differ- ences between them through a computational approach. Our analysis reveals that human in- terpreters regularly apply several effective tac- tics to reduce translation latency, including sen- tence segmentation and passivization. In addi- tion to these unique, clever strategies, we show that limited human memory also causes other idiosyncratic properties of human interpreta- tion such as generalization and omission of source content.

@inproceedings{daume16interpretese, title = {Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation}, author = {He He and Jordan Boyd-Graber and Hal {Daum\'e III}}, booktitle = {Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)}, year = {2016}, url = {http://hal3.name/docs/#daume16interpretese}, }

Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
Khanh Nguyen, Hal Daumé III and Jordan Boyd-Graber
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017
[Abstract] [BibTeX]

Machine translation is a natural candidate problem for reinforcement learning from human feedback: users provide quick, dirty ratings on candidate translations to guide a system to improve. Yet, current neural machine translation training focuses on expensive human-generated reference translations. We describe a reinforcement learning algorithm that improves neural machi\ ne translation systems from simulated human feedback. Our algorithm combines the advantage actor-critic algorithm (Mnih et al., 2016) with the attention-based neural encoderdecoder architecture (Luong et al., 2015). This algorithm (a) is well-designed for problems with a large action space and delayed rewards, (b) effectively optimizes traditional corpus-level machine translation metrics, and (c) is robust to skewed, high-variance, granular feedback modeled after actual human behaviors.

@inproceedings{daume17simhuman, title = {Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback}, author = {Khanh Nguyen and Hal {Daum\'e III} and Jordan Boyd-Graber}, booktitle = {Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2017}, url = {http://hal3.name/docs/#daume17simhuman}, }

Learning to search better than your teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III and John Langford
International Conference on Machine Learning (ICML), 2015
[Abstract] [BibTeX]

Methods for learning to search for structured prediction typically imitate a reference policy, with existing theoretical guarantees demonstrating low regret compared to that reference. This is unsatisfactory in many applications where the reference policy is suboptimal and the goal of learning is to improve upon it. Can learning to search work even when the reference is poor? We provide a new learning to search algorithm, LOLS, which does well relative to the reference policy, but additionally guarantees low regret compared to deviations from the learned policy: a local-optimality guarantee. Consequently, LOLS can improve upon the reference policy, unlike previous algorithms. This enables us to develop structured contextual bandits, a partial information structured prediction setting with many potential applications.

@inproceedings{daume15lols, title = {Learning to search better than your teacher}, author = {Kai-Wei Chang and Akshay Krishnamurthy and Alekh Agarwal and Hal {Daum\'e III} and John Langford}, booktitle = {Proceedings of the International Conference on Machine Learning (ICML)}, year = {2015}, url = {http://hal3.name/docs/#daume15lols}, }

credits: design and font inspired by Seth Able's LoRD, some images converted to ANSI using ManyTools, original drawing of me by anonymous.
last updated on thirty september, two thousand twenty four.

CV

Bio

Contact Information:

Representative Publications: