Hal Daumé III

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Principal Researcher at Microsoft Research in New York City, Associate Professor at the University of Maryland, Ph.D, University of Southern California, 2006

Active Imitation Learning from Multiple Non-Deterministic Teachers: Formulation, Challenges, and Algorithms

We formulate the problem of learning to imitate multiple, non-determinis...
06/14/2020 ∙ by Khanh Nguyen, et al. ∙ 11 ∙ share

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Language (Technology) is Power: A Critical Survey of "Bias" in NLP

We survey 146 papers analyzing "bias" in NLP systems, finding that their...
05/28/2020 ∙ by Su Lin Blodgett, et al. ∙ 0 ∙ share

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Operationalizing the Legal Principle of Data Minimization for Personalization

Article 5(1)(c) of the European Union's General Data Protection Regulati...
05/28/2020 ∙ by Asia J. Biega, et al. ∙ 0 ∙ share

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Active Imitation Learning with Noisy Guidance

Imitation learning algorithms provide state-of-the-art results on many s...
05/26/2020 ∙ by Kianté Brantley, et al. ∙ 9 ∙ share

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Meta-Learning for Few-Shot NMT Adaptation

We present META-MT, a meta-learning approach to adapt Neural Machine Tra...
04/06/2020 ∙ by Amr Sharaf, et al. ∙ 0 ∙ share

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Toward Gender-Inclusive Coreference Resolution

Correctly resolving textual mentions of people fundamentally entails mak...
10/30/2019 ∙ by Yang Trista Cao, et al. ∙ 0 ∙ share

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Weight of Evidence as a Basis for Human-Oriented Explanations

Interpretability is an elusive but highly sought-after characteristic of...
10/29/2019 ∙ by David Alvarez-Melis, et al. ∙ 53 ∙ share

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Global Voices: Crossing Borders in Automatic News Summarization

We construct Global Voices, a multilingual dataset for evaluating cross-...
10/01/2019 ∙ by Khanh Nguyen, et al. ∙ 0 ∙ share

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Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning

Mobile agents that can leverage help from humans can potentially accompl...
09/04/2019 ∙ by Khanh Nguyen, et al. ∙ 6 ∙ share

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Reinforcement Learning with Convex Constraints

In standard reinforcement learning (RL), a learning agent seeks to optim...
06/21/2019 ∙ by Sobhan Miryoosefi, et al. ∙ 14 ∙ share

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Answer-based Adversarial Training for Generating Clarification Questions

We present an approach for generating clarification questions with the g...
04/04/2019 ∙ by Sudha Rao, et al. ∙ 0 ∙ share

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Non-Monotonic Sequential Text Generation

Standard sequential generation methods assume a pre-specified generation...
02/05/2019 ∙ by Sean Welleck, et al. ∙ 8 ∙ share

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Meta-Learning for Contextual Bandit Exploration

We describe MELEE, a meta-learning algorithm for learning a good explora...
01/23/2019 ∙ by Amr Sharaf, et al. ∙ 18 ∙ share

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Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

We investigate the feasibility of learning from both fully-labeled super...
01/02/2019 ∙ by Chicheng Zhang, et al. ∙ 6 ∙ share

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Improving fairness in machine learning systems: What do industry practitioners need?

The potential for machine learning (ML) systems to amplify social inequi...
12/13/2018 ∙ by Kenneth Holstein, et al. ∙ 0 ∙ share

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Content Selection in Deep Learning Models of Summarization

We carry out experiments with deep learning models of summarization acro...
10/29/2018 ∙ by Chris Kedzie, et al. ∙ 0 ∙ share

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Contextual Memory Trees

We design and study a Contextual Memory Tree (CMT), a learning memory co...
07/17/2018 ∙ by Wen Sun, et al. ∙ 6 ∙ share

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Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information

Inquiry is fundamental to communication, and machines cannot effectively...
05/12/2018 ∙ by Sudha Rao, et al. ∙ 0 ∙ share

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When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks

Attacks against machine learning systems represent a growing threat as h...
03/19/2018 ∙ by Octavian Suciu, et al. ∙ 0 ∙ share

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Hierarchical Imitation and Reinforcement Learning

We study the problem of learning policies over long time horizons. We pr...
03/01/2018 ∙ by Hoang M. Le, et al. ∙ 0 ∙ share

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Towards Linguistically Generalizable NLP Systems: A Workshop and Shared Task

This paper presents a summary of the first Workshop on Building Linguist...
11/04/2017 ∙ by Allyson Ettinger, et al. ∙ 0 ∙ share

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The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task

We describe the University of Maryland machine translation systems submi...
08/03/2017 ∙ by Amr Sharaf, et al. ∙ 0 ∙ share

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Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback

Machine translation is a natural candidate problem for reinforcement lea...
07/24/2017 ∙ by Khanh Nguyen, et al. ∙ 0 ∙ share

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Active Learning for Cost-Sensitive Classification

We design an active learning algorithm for cost-sensitive multiclass cla...
03/03/2017 ∙ by Akshay Krishnamurthy, et al. ∙ 0 ∙ share

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The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives

Visual narrative is often a combination of explicit information and judi...
11/16/2016 ∙ by Mohit Iyyer, et al. ∙ 0 ∙ share

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Logarithmic Time One-Against-Some

We create a new online reduction of multiclass classification to binary ...
06/15/2016 ∙ by Hal Daumé III, et al. ∙ 0 ∙ share

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Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text

The ability to comprehend wishes or desires and their fulfillment is imp...
11/30/2015 ∙ by Snigdha Chaturvedi, et al. ∙ 0 ∙ share

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Modeling Dynamic Relationships Between Characters in Literary Novels

Studying characters plays a vital role in computationally representing a...
11/30/2015 ∙ by Snigdha Chaturvedi, et al. ∙ 0 ∙ share

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Parser for Abstract Meaning Representation using Learning to Search

We develop a novel technique to parse English sentences into Abstract Me...
10/26/2015 ∙ by Sudha Rao, et al. ∙ 0 ∙ share

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Learning to Search for Dependencies

We demonstrate that a dependency parser can be built using a credit assi...
03/18/2015 ∙ by Kai-Wei Chang, et al. ∙ 0 ∙ share

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Learning to Search Better Than Your Teacher

Methods for learning to search for structured prediction typically imita...
02/08/2015 ∙ by Kai-Wei Chang, et al. ∙ 0 ∙ share

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Bayesian Multitask Learning with Latent Hierarchies

We learn multiple hypotheses for related tasks under a latent hierarchic...
08/09/2014 ∙ by Hal Daumé III, et al. ∙ 0 ∙ share

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Flexible Modeling of Latent Task Structures in Multitask Learning

Multitask learning algorithms are typically designed assuming some fixed...
06/27/2012 ∙ by Alexandre Passos, et al. ∙ 0 ∙ share

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A Binary Classification Framework for Two-Stage Multiple Kernel Learning

With the advent of kernel methods, automating the task of specifying a s...
06/27/2012 ∙ by Abhishek Kumar, et al. ∙ 0 ∙ share

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Learning Task Grouping and Overlap in Multi-task Learning

In the paradigm of multi-task learning, mul- tiple related prediction ta...
06/27/2012 ∙ by Abhishek Kumar, et al. ∙ 0 ∙ share

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Efficient Protocols for Distributed Classification and Optimization

In distributed learning, the goal is to perform a learning task over dat...
04/16/2012 ∙ by Hal Daumé III, et al. ∙ 0 ∙ share

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Protocols for Learning Classifiers on Distributed Data

We consider the problem of learning classifiers for labeled data that ha...
02/27/2012 ∙ by Hal Daumé III, et al. ∙ 0 ∙ share

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Hal Daumé III

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