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

A professor named Hal Daumé III. He wields appointments in Computer Science and Language Science at UMD; he is currently on leave from UMD, spending time in the machine learning and fairness groups at Microsoft Research NYC. He and his wonderful advisees study questions related to how to get machines to becomes more adept at human language, 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 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's discussed interactive learning informally recently in a Talking Machines Podcost and more technically in recent talks; and has discussed fairness/bias in broad terms in a recent blog post. Hal is committed to promoting an inclusive scientific environment; if you are thinking of inviting him for a talk or to participate in an event, please ensure that the event is consistent with this goal (see the first question on the FAQ).

Hal is super fortunate to have awesome colleagues in the Computional Linguistics and Information Processing Lab (which he currently directs). He maintains the structured prediction framework in VW. If you want to contact him, email is your best bet; you can also find him on @haldaume3 on Twitter. Or, in person, in the CLIP lab (AVW 3126) or his office (AVW 3227). If you're a prospective grad student or grad applicant, please read his FAQ!

Recent Publications:

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},
}


Non-Monotonic Sequential Text Generation
Sean Welleck, Kianté Brantley, Hal Daumé III and Kyunghyun Cho
arxiv, 2019
[Abstract] [BibTeX]

Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation. Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree. Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy's own preferences. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order, while achieving competitive performance with conventional left-to-right generation.

@inproceedings{daume19nonmon,
title = {Non-Monotonic Sequential Text Generation},
author = {Sean Welleck and Kiant\'e Brantley and Hal {Daum\'e III} and Kyunghyun
Cho},
booktitle = {arxiv},
year = {2019},
url = {http://hal3.name/docs/#daume19nonmon},
}


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

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 me},
booktitle = {arxiv},
year = {2019},
url = {http://hal3.name/docs/#daume19melee},
}


Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford and Sahand N Negahban
arxiv, 2019
[Abstract] [BibTeX]

We investigate the feasibility of learning from both fully-labeled supervised data and contextual bandit data. We specifically consider settings in which the underlying learning signal may be different between these two data sources. Theoretically, we state and prove no-regret algorithms for learning that is robust to divergences between the two sources. Empirically, we evaluate some of these algorithms on a large selection of datasets, showing that our approaches are feasible, and helpful in practice.

@inproceedings{daume19awesome,
title = {Warm-starting Contextual Bandits: Robustly Combining Supervised and
Bandit Feedback},
author = {Chicheng Zhang and Alekh Agarwal and Hal {Daum\'e III} and John
Langford and Sahand N Negahban},
booktitle = {arxiv},
year = {2019},
url = {http://hal3.name/docs/#daume19awesome},
}


Hierarchical Imitation and Reinforcement Learning
Hoang M Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue and Hal Daumé III
ICML, 2018
[Abstract] [BibTeX]

We study the problem of learning policies over long time horizons. We present a framework that leverages and integrates two key concepts. First, we utilize hierarchical policy classes that enable planning over different time scales, i.e., the high level planner proposes a sequence of subgoals for the low level planner to achieve. Second, we utilize expert demonstrations within the hierarchical action space to dramatically reduce cost of exploration. Our framework is flexible and can incorporate different combinations of imitation learning (IL) and reinforcement \ learning (RL) at different levels of the hierarchy. Using long-horizon benchmarks, including Montezuma’s Revenge, we empirically demonstrate that our approach can learn significantly faster compared to hierarchical RL, and can be significantly more labeland sample-efficient compared to flat IL. We also provide theoretical analysis of the labeling cost for certain instantiations of our framework.

@inproceedings{daume18hierilrl,
title = {Hierarchical Imitation and Reinforcement Learning},
author = {Hoang M Le and Nan Jiang and Alekh Agarwal and Miroslav Dud\'ik and
Yisong Yue and Hal {Daum\'e III}},
booktitle = {ICML},
year = {2018},
url = {http://hal3.name/docs/#daume18hierilrl},
}


More papers please!

Recent Talks:

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

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

Locally optimal learning to search and distant supervision
December 2015, UMD CS Research Seminar
[PDF] [ODP] [Video]

Imitation learning and recurrent neural networks mashup
December 2015, CIFAR NCAP Workshop
[PDF] [ODP]

Algorithms that learn to think of their feet
October 2015, UC Santa Cruz
[PDF] [ODP]

More talks please!

Contact information:

    email: me AT hal3 DOT name               skype: haldaume3
    phone: 301-405-1073                    twitter: haldaume3
   office: AVW 3227                         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 february, two thousand nineteen.