You enter a dark forest. Standing in front of you is:

A professor named Hal Daumé III (he/him). He wields appointments in Computer Science where he is a Perotto Professor, as well as Language Science at UMD (in Fall 2019 he is teaching Computational Linguistics I); he also spends time in the machine learning and fairness groups at Microsoft Research NYC. He and his wonderful advisees like to study questions related to how to get machines to becomes more adept at human language (and artificial intelligence tasks more broadly), by developing models and algorithms that allow them to learn from data. (Keywords: natural language processing and machine learning.) The two major questions that really drive their research these days are:

    (1) how can we get computers to learn
        through natural interaction with people/users?

and (2) how can we do this in a way that minimize harms
        in the learned models?

He's discussed interactive learning informally in a Talking Machines Podcast and more technically in recent talks; and has discussed fairness/bias in broad terms in a (now somewhat outdated) blog post. He is the author of the online textbook A Course in Machine Learning, which is fully open source.

Hal is super fortunate to have awesome colleagues in the Computional Linguistics and Information Processing Lab (which he formerly directed) and Center for Machine Learning. If you want to contact him, email is your best bet; you can also find him on @haldaume3 on Twitter. Or, in person, in his office (IRB 4150).

If you're a prospective grad student or grad applicant, please read his FAQ to answer some common questions. If you're thinking of inviting him for a talk or event, please ensure that the event is organized in an inclusive manner (inclusion rider). More generally, if you are organizing a conference, workshop or other event, you may wish to read the NeurIPS D&I survey results (joint with Katherine Heller), Humberto Corona's collection of resources/advice, or two blog posts on this topic.

I acknowledge that I live and work on the ancestral and unceded lands of the Piscataway People, who were among the first in the Western Hemisphere to encounter European colonists, as well as the lands of the Lenape and Nacotchtank people.

Recent Publications:

Meta-Learning for Contextual Bandit Exploration
Amr Sharaf and Hal Daumé III
arxiv, 2019
[Abstract] [BibTeX] [Code/Data]

We describe MELEE, a meta-learning algorithm for learning a good exploration policy in the interactive contextual bandit setting. Here, an algorithm must take actions based on contexts, and learn based only on a reward signal from the action taken, thereby generating an exploration/exploitation trade-off. MELEE addresses this trade-off by learning a good exploration strategy for offline tasks based on synthetic data, on which it can simulate the contextual bandit setting. Based on these simulations, MELEE uses an imitation learning strategy to learn a good exploration policy that can then be applied to true contextual bandit tasks at test time. We compare MELEE to seven strong baseline contextual bandit algorithms on a set of three hundred real-world datasets, on which it outperforms alternatives in most settings, especially when differences in rewards are large. Finally, we demonstrate the importance of having a rich feature representation for learning how to explore.

@inproceedings{daume19melee,
title = {Meta-Learning for Contextual Bandit Exploration},
author = {Amr Sharaf and Hal {Daum\'e III}},
booktitle = {arxiv},
year = {2019},
link =
{https://www.dropbox.com/sh/dc3v8po5cbu8zaw/AACu1f_4c4wIZxD1e7W0KVZ0a?dl=0},
url = {http://hal3.name/docs/#daume19melee},
}


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 = {},
}


Global Voices: Crossing Borders in Automatic News Summarization
Khanh Nguyen and Hal Daumé III
EMNLP Summarization Workshop, 2019
[Abstract] [BibTeX]

We construct Global Voices, a multilingual dataset for evaluating cross-lingual summarization methods. We extract social-network descriptions of Global Voices news articles to cheaply collect evaluation data for into-English and from-English summarization in 15 languages. Especially, for the into-English summarization task, we crowd-source a high-quality evaluation dataset based on guidelines that emphasize accuracy, coverage, and understandability. To ensure the quality of this dataset, we collect human ratings to filter out bad summaries, and conduct a survey on humans, which shows that the remaining summaries are preferred over the social-network summaries. We study the effect of translation quality in cross-lingual summarization, comparing a translate-then-summarize approach with several baselines. Our results highlight the limitations of the ROUGE metric that are overlooked in monolingual summarization.

@inproceedings{daume19global,
title = {Global Voices: Crossing Borders in Automatic News Summarization},
author = {Khanh Nguyen and me},
booktitle = {EMNLP Summarization Workshop},
year = {2019},
url = {http://hal3.name/docs/#daume19global},
}


Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts
Elissa Redmiles, Lisa Maszkiewicz, Emily Hwang, Dhruv Kuchhal, Everst Liu, Miraida Morales, Denis Peskov, Sudha Rao, Rock Stevens, Kristina Gligorić, Sean Kross, Michelle L. Mazurek and Hal Daumé III
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019
[Abstract] [BibTeX] [Code/Data]

The readability of a digital text can influence people's ability to learn new things about a range of topics from digital resources (e.g., Wikipedia, WebMD). Readability also impacts search rankings, and is used to evaluate the performance of NLP systems. Despite this, we lack a thorough understanding of how to validly measure readability at scale, especially for domain-specific texts. In this work, we present a comparison of the validity of well-known readability measures and introduce a novel approach, Smart Cloze, which is designed to address short-comings of existing measures. We compare these approaches across four different corpora: crowdworker-generated stories, Wikipedia articles, security and privacy advice, and health information. On these corpora, we evaluate the convergent and content validity of each measure, and detail tradeoffs in score precision, domain-specificity, and participant burden. These results provide a foundation for more accurate readability measurements and better evaluation of new natural-language-processing systems and tools.

@inproceedings{daume19readability,
title = {Comparing and Developing Tools to Measure the Readability of
Domain-Specific Texts},
author = {Elissa Redmiles and Lisa Maszkiewicz and Emily Hwang and Dhruv Kuchhal
and Everst Liu and Miraida Morales and Denis Peskov and Sudha Rao
and Rock Stevens and Kristina Gligori\'c and Sean Kross and
Michelle L. Mazurek and me},
booktitle = {Proceedings of the Conference on Empirical Methods in Natural
Language Processing (EMNLP)},
year = {2019},
link = {http://github.com/SP2-MC2/Readability-Resources},
url = {http://hal3.name/docs/#daume19readability},
}


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},
}


More papers please!

Recent Talks:

(Meta-)Learning from Interaction
NYU Machine Learning Reading Group 2019
[PDF] [ODP]

Out of Order! Flexible neural language generation
NAACL 2019 NeuralGen Workshop
[PPTx]

Beyond demonstrations: Learning behavior from higher-level supervision
ICML 2019 I3 Workshop
[PPTX]

Imitation Learning
Vector Institute Reinforcement Learning Summer School 2018
[PDF] [ODP]

Learning language through interaction
December 2016, Georgetown, Amazon, USC, GATech, UW, ...
[PDF] [ODP] [Video]

Bias in AI
November 2016, UMD MCWIC Diversity Summit
[PDF] [ODP] [PPTx (exported)] [Blog Post]

More talks please!

Contact information:

    email: me AT hal3 DOT name               skype: haldaume3
    phone: 301-405-1073                    twitter: haldaume3
   office: IRB 4150                         github: hal3

I can't reply to all prospective students email; please read this before emailing me.

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 twenty seven february, two thousand twenty.