Rare Class Classification by Support Vector Machine. - He He 001; Ali Ghodsi 001 (ICPR, 2010)

Single image super-resolution using Gaussian process regression. - He He 001; Wan-Chi Siu (CVPR, 2011)

Besting the Quiz Master: Crowdsourcing Incremental Classification Games. - Jordan L. Boyd-Graber; Brianna Satinoff; He He 001; Hal Daumé III (EMNLP-CoNLL, 2012)

Imitation Learning by Coaching. - He He 001; Hal Daumé III; Jason Eisner (NIPS, 2012)

Dynamic Feature Selection for Dependency Parsing. - He He 001; Hal Daumé III; Jason Eisner (EMNLP, 2013)

Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation. - Alvin Grissom II; He He 001; Jordan L. Boyd-Graber; John Morgan; Hal Daumé III (EMNLP, 2014)

Learning to Search in Branch and Bound Algorithms. - He He 001; Hal Daumé III; Jason Eisner (NIPS, 2014)

Temporal supervised learning for inferring a dialog policy from example conversations. - Lihong Li 001; He He 001; Jason D. Williams (SLT, 2014)

Syntax-based Rewriting for Simultaneous Machine Translation. - He He 001; Alvin Grissom II; John Morgan; Jordan L. Boyd-Graber; Hal Daumé III (EMNLP, 2015)

Crowdsourcing with multi-dimensional trust. - Xiangyang Liu; He He 001; John S. Baras (FUSION, 2015)

Trust-aware optimal crowdsourcing with budget constraint. - Xiangyang Liu; He He 001; John S. Baras (ICC, 2015)

Hands-on Learning to Search for Structured Prediction. - Hal Daumé III; John Langford 001; Kai-Wei Chang; He He 001; Sudha Rao (HLT-NAACL, 2015)

Learning to Search for Dependencies. - Kai-Wei Chang; He He 001; Hal Daumé III; John Langford 001 (CoRR, 2015)

Opponent Modeling in Deep Reinforcement Learning. - He He 001; Jordan L. Boyd-Graber (ICML, 2016)

Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation. - He He 001; Jordan L. Boyd-Graber; Hal Daumé III (HLT-NAACL, 2016)

A Credit Assignment Compiler for Joint Prediction. - Kai-Wei Chang; He He 001; Stéphane Ross; Hal Daumé III; John Langford 001 (NIPS, 2016)

Object detection in 20 questions. - Xi Stephen Chen; He He 001; Larry S. Davis (WACV, 2016)

Opponent Modeling in Deep Reinforcement Learning. - He He 001; Jordan L. Boyd-Graber; Kevin Kwok; Hal Daumé III (CoRR, 2016)

Active Information Acquisition. - He He 001; Paul Mineiro; Nikos Karampatziakis (CoRR, 2016)

Sequential Decisions and Predictions in Natural Language Processing. - He He 001 (2016)

Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings. - He He 001; Anusha Balakrishnan; Mihail Eric; Percy Liang (ACL (1), 2017)

Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context. - Urvashi Khandelwal; He He 001; Peng Qi 003; Dan Jurafsky (ACL (1), 2018)

QuAC: Question Answering in Context. - Eunsol Choi; He He 001; Mohit Iyyer; Mark Yatskar; Wen-tau Yih; Yejin Choi 001; Percy Liang; Luke Zettlemoyer (EMNLP, 2018)

Decoupling Strategy and Generation in Negotiation Dialogues. - He He 001; Derek Chen; Anusha Balakrishnan; Percy Liang (EMNLP, 2018)

Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer. - Juncen Li; Robin Jia; He He 001; Percy Liang (NAACL-HLT, 2018)

Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual. - He He 001; Sheng Zha; Haohan Wang (DeepLo@EMNLP-IJCNLP, 2019)

Dive into Deep Learning for Natural Language Processing. - Haibin Lin; Xingjian Shi; Leonard Lausen; Aston Zhang; He He 001; Sheng Zha; Alexander J. Smola (EMNLP/IJCNLP (2), 2019)

Pun Generation with Surprise. - He He 001; Nanyun Peng 001; Percy Liang (NAACL-HLT (1), 2019)

A Dynamic Strategy Coach for Effective Negotiation. - Yiheng Zhou; He He 001; Alan W. Black; Yulia Tsvetkov (SIGdial, 2019)

Quizbowl: The Case for Incremental Question Answering. - Pedro Rodriguez 001; Shi Feng 005; Mohit Iyyer; He He 001; Jordan L. Boyd-Graber (CoRR, 2019)

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. - Jian Guo; He He 001; Tong He 002; Leonard Lausen; Mu Li 003; Haibin Lin; Xingjian Shi; Chenguang Wang 001; Junyuan Xie; Sheng Zha; Aston Zhang; Hang Zhang 005; et al. (CoRR, 2019)

FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization. - Esin Durmus; He He 001; Mona T. Diab (ACL, 2020)

Text Generation by Learning from Off-Policy Demonstrations. - Richard Yuanzhe Pang; He He 001 (CoRR, 2020)

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. - Jian Guo; He He 001; Tong He 002; Leonard Lausen; Mu Li 003; Haibin Lin; Xingjian Shi; Chenguang Wang 001; Junyuan Xie; Sheng Zha; Aston Zhang; Hang Zhang 005; et al. (J. Mach. Learn. Res., 2020)

An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models. - Lifu Tu; Garima Lalwani; Spandana Gella; He He 001 (Trans. Assoc. Comput. Linguistics, 2020)

Unsupervised Extractive Summarization using Pointwise Mutual Information. - Vishakh Padmakumar; He He 001 (EACL, 2021)

Text Generation by Learning from Demonstrations. - Richard Yuanzhe Pang; He He 001 (ICLR, 2021)

IRM - when it works and when it doesn't: A test case of natural language inference. - Yana Dranker; He He 001; Yonatan Belinkov (NeurIPS, 2021)

Amortized Noisy Channel Neural Machine Translation. - Richard Yuanzhe Pang; He He 001; Kyunghyun Cho (CoRR, 2021)

Meta-learning via Language Model In-context Tuning. - Yanda Chen; Ruiqi Zhong; Sheng Zha; George Karypis; He He 001 (ACL (1), 2022)

An Investigation of the (In)effectiveness of Counterfactually Augmented Data. - Nitish Joshi; He He 001 (ACL (1), 2022)

Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization. - Faisal Ladhak; Esin Durmus; He He 001; Claire Cardie; Kathleen R. McKeown (ACL (1), 2022)

Help me write a Poem - Instruction Tuning as a Vehicle for Collaborative Poetry Writing. - Tuhin Chakrabarty; Vishakh Padmakumar; He He 001 (EMNLP, 2022)

Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens. - Nitish Joshi; Xiang Pan 001; He He 001 (EMNLP, 2022)

Improving Faithfulness by Augmenting Negative Summaries from Fake Documents. - Tianshu Wang; Faisal Ladhak; Esin Durmus; He He 001 (EMNLP, 2022)

Machine-in-the-Loop Rewriting for Creative Image Captioning. - Vishakh Padmakumar; He He 001 (NAACL-HLT, 2022)

Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning. - Vishakh Padmakumar; Leonard Lausen; Miguel Ballesteros; Sheng Zha; He He 001; George Karypis (NAACL-HLT, 2022)

QuALITY: Question Answering with Long Input Texts, Yes! - Richard Yuanzhe Pang; Alicia Parrish; Nitish Joshi; Nikita Nangia; Jason Phang; Angelica Chen; Vishakh Padmakumar; Johnny Ma; Jana Thompson; He He 001; Samuel R. Bowman (NAACL-HLT, 2022)

Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. - Aahlad Manas Puli; Nitish Joshi; He He 001; Rajesh Ranganath (CoRR, 2022)

Reward Gaming in Conditional Text Generation. - Richard Yuanzhe Pang; Vishakh Padmakumar; Thibault Sellam; Ankur P. Parikh; He He 001 (ACL (1), 2023)

Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. - Chenglei Si; Dan Friedman; Nitish Joshi; Shi Feng 005; Danqi Chen 001; He He 001 (ACL (1), 2023)

Efficient Shapley Values Estimation by Amortization for Text Classification. - Chenghao Yang 001; Fan Yin; He He 001; Kai-Wei Chang; Xiaofei Ma 001; Bing Xiang (ACL (1), 2023)

On the Relation between Sensitivity and Accuracy in In-Context Learning. - Yanda Chen; Chen Zhao; Zhou Yu; Kathleen R. McKeown; He He 001 (EMNLP (Findings), 2023)

Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought. - Abulhair Saparov; He He 001 (ICLR, 2023)

Extrapolative Controlled Sequence Generation via Iterative Refinement. - Vishakh Padmakumar; Richard Yuanzhe Pang; He He 001; Ankur P. Parikh (ICML, 2023)

Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples. - Abulhair Saparov; Richard Yuanzhe Pang; Vishakh Padmakumar; Nitish Joshi; Mehran Kazemi; Najoung Kim; He He 001 (NeurIPS, 2023)

ARGUS: Visualization of AI-Assisted Task Guidance in AR. - Sonia Castelo; João Rulff; Erin McGowan; Bea Steers; Guande Wu; Shaoyu Chen; Irán R. Román; Roque Lopez; Ethan Brewer; Chen Zhao; Jing Qian; Kyunghyun Cho; et al. (CoRR, 2023)

Parallel Structures in Pre-training Data Yield In-Context Learning. - Yanda Chen; Chen Zhao; Zhou Yu; Kathleen R. McKeown; He He 001 (ACL (1), 2024)

Your Co-Workers Matter: Evaluating Collaborative Capabilities of Language Models in Blocks World. - Guande Wu; Chen Zhao; Cláudio T. Silva; He He 001 (ACL (Findings), 2024)

Leveraging Implicit Feedback from Deployment Data in Dialogue. - Richard Yuanzhe Pang; Stephen Roller; Kyunghyun Cho; He He 001; Jason Weston (EACL (2), 2024)

Personas as a Way to Model Truthfulness in Language Models. - Nitish Joshi; Javier Rando; Abulhair Saparov; Najoung Kim; He He 001 (EMNLP, 2024)

LLMs Are Prone to Fallacies in Causal Inference. - Nitish Joshi; Abulhair Saparov; Yixin Wang; He He 001 (EMNLP, 2024)

Does Writing with Language Models Reduce Content Diversity? - Vishakh Padmakumar; He He 001 (ICLR, 2024)

Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations. - Yanda Chen; Ruiqi Zhong; Narutatsu Ri; Chen Zhao; He He 001; Jacob Steinhardt; Zhou Yu; Kathleen R. McKeown (ICML, 2024)

Show Your Work with Confidence: Confidence Bands for Tuning Curves. - Nicholas Lourie; Kyunghyun Cho; He He 001 (NAACL-HLT, 2024)

The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models. - Hannah Rose Kirk; Alexander Whitefield; Paul Röttger; Andrew M. Bean; Katerina Margatina; Rafael Mosquera Gómez; Juan Ciro; Max Bartolo; Adina Williams; He He 001; Bertie Vidgen; Scott Hale (NeurIPS, 2024)

Iterative Reasoning Preference Optimization. - Richard Yuanzhe Pang; Weizhe Yuan; He He 001; Kyunghyun Cho; Sainbayar Sukhbaatar; Jason Weston (NeurIPS, 2024)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models. - Usman Anwar; Abulhair Saparov; Javier Rando; Daniel Paleka; Miles Turpin; Peter Hase; Ekdeep Singh Lubana; Erik Jenner; Stephen Casper; Oliver Sourbut; Benjamin L. Edelman; Zhaowei Zhang; et al. (CoRR, 2024)

The PRISM Alignment Project: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models. - Hannah Rose Kirk; Alexander Whitefield; Paul Röttger; Andrew M. Bean; Katerina Margatina; Juan Ciro; Rafael Mosquera; Max Bartolo; Adina Williams; He He 001; Bertie Vidgen; Scott A. Hale (CoRR, 2024)

Spontaneous Reward Hacking in Iterative Self-Refinement. - Jane Pan; He He 001; Samuel R. Bowman; Shi Feng 005 (CoRR, 2024)

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization. - Vishakh Padmakumar; Chuanyang Jin; Hannah Rose Kirk; He He 001 (CoRR, 2024)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models. - Usman Anwar; Abulhair Saparov; Javier Rando; Daniel Paleka; Miles Turpin; Peter Hase; Ekdeep Singh Lubana; Erik Jenner; Stephen Casper; Oliver Sourbut; Benjamin L. Edelman; Zhaowei Zhang; et al. (Trans. Mach. Learn. Res., 2024)

Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. - Aahlad Manas Puli; Nitish Joshi; Yoav Wald; He He 001; Rajesh Ranganath (Trans. Mach. Learn. Res., 2024)

: Visualization of AI-Assisted Task Guidance in AR. - Sonia Castelo; João Rulff; Erin McGowan; Bea Steers; Guande Wu; Shaoyu Chen; Irán R. Román; Roque Lopez; Ethan Brewer; Chen Zhao; Jing Qian; Kyunghyun Cho; et al. (IEEE Trans. Vis. Comput. Graph., 2024)

When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback. - Jane Pan; Ryan Shar; Jacob Pfau; Ameet Talwalkar; He He 001; Valerie Chen (ACL (Findings), 2025)

Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning. - Yanda Chen; Chandan Singh; Xiaodong Liu 003; Simiao Zuo; Bin Yu 001; He He 001; Jianfeng Gao 001 (COLING, 2025)

Transformers Struggle to Learn to Search. - Abulhair Saparov; Srushti Ajay Pawar; Shreyas Pimpalgaonkar; Nitish Joshi; Richard Yuanzhe Pang; Vishakh Padmakumar; Mehran Kazemi; Najoung Kim; He He 001 (ICLR, 2025)

Adaptive Deployment of Untrusted LLMs Reduces Distributed Threats. - Jiaxin Wen; Vivek Hebbar; Caleb Larson; Aryan Bhatt; Ansh Radhakrishnan; Mrinank Sharma; Henry Sleight; Shi Feng 005; He He 001; Ethan Perez; Buck Shlegeris; Akbir Khan (ICLR, 2025)

Language Models Learn to Mislead Humans via RLHF. - Jiaxin Wen; Ruiqi Zhong; Akbir Khan; Ethan Perez; Jacob Steinhardt; Minlie Huang; Samuel R. Bowman; He He 001; Shi Feng 005 (ICLR, 2025)

Reasoning Models Know When They're Right: Probing Hidden States for Self-Verification. - Anqi Zhang; Yulin Chen; Jane Pan; Chen Zhao; Aurojit Panda; Jinyang Li; He He 001 (CoRR, 2025)

Beyond Memorization: Mapping the Originality-Quality Frontier of Language Models. - Vishakh Padmakumar; Chen Yueh-Han; Jane Pan; Valerie Chen; He He 001 (CoRR, 2025)

Unsupervised Elicitation of Language Models. - Jiaxin Wen; Zachary Ankner; Arushi Somani; Peter Hase; Samuel Marks; Jacob Goldman-Wetzler; Linda Petrini; Henry Sleight; Collin Burns; He He 001; Shi Feng 005; Ethan Perez; et al. (CoRR, 2025)

Monitoring Decomposition Attacks in LLMs with Lightweight Sequential Monitors. - Chen Yueh-Han; Nitish Joshi; Yulin Chen; Maksym Andriushchenko; Rico Angell; He He 001 (CoRR, 2025)