Papers

• Just Label the Repeats for In-The-Wild Audio-to-Score Alignment

  Irmak Bukey, Michael Feffer, Chris Donahue
  International Society for Music Information Retrieval Conference (ISMIR), 2024

• Red-Teaming for Generative AI: Silver Bullet or Security Theater?

  Michael Feffer, Anusha Sinha, Wesley H. Deng, Zachary Lipton, Hoda Heidari
  AAAI/ACM Conference on Artificial Intelligence, Ethics and Society (AIES), 2024

• Prompt Exploration with Prompt Regression

  Michael Feffer*, Ronald Xu*, Yuekai Sun, Mikhail Yurochkin
  Conference on Language Modeling (COLM), 2024

• GenAudit: Fixing Factual Errors in Language Model Outputs with Evidence

  Kundan Krishna, Sanjana Ramprasad, Prakhar Gupta, Byron Wallace, Zachary Lipton, Jeffrey Bigham
  arXiv (preprint), 2024

• Auditing Fairness under Unobserved Confounding

  Emily Byun, Dylan Sam, Michael Oberst, Zachary Lipton, Bryan Wilder
  AISTATS, 2024

• Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression

  Shahriar Noroozizadeh, Jeremy Weiss, George Chen
  Machine Learning for Health (ML4H), 2023

• DeepDrake ft. BTS-GAN and TayloRVC: A Survey of Musical Deepfake Models

  Michael Feffer, Zachary Lipton, Chris Donahue
  Workshop on Human-Centric Music Information Research (HCMIR@ISMIR), 2023

• The AI Incident Database as an Educational Tool to Raise Awareness of Harms: A Classroom Exploration of Efficacy, Limitations, & Future Design Improvements

  Michael Feffer, Nikolas Martelaro, Hoda Heidari
  ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), 2023

• From Preference Elicitation to Participatory ML: A Critical Survey & Guidelines for Future Research

  Michael Feffer, Michael Skirpan, Zachary Lipton*, Hoda Heidari*
  AAAI/ACM Conference on Artificial Intelligence, Ethics and Society (AIES), 2023

• A Suite of Fairness Datasets for Tabular Classification

  Martin Hirzel, Michael Feffer
  arXiv, 2023

• USB: A Unified Summarization Benchmark Across Tasks and Domains

  Kundan Krishna, Sanjana Ramprasad, Prakhar Gupta, Byron Wallace, Jeffrey Bigham, Zachary Lipton
  EMNLP (Findings), 2023

• Downstream Datasets Make Surprisingly Good Pretraining Corpora

  Kundan Krishna, Saurabh Garg, Jeffrey Bigham, Zachary Lipton
  ACL 2023, 2023

• Moral Machine or Tyranny of the Majority?

  Michael Feffer, Hoda Heidari*, Zachary Lipton*
  Association for the Advancement of Artificial Intelligence (AAAI), 2023

• Searching for Fairer Machine Learning Ensembles

  Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar
  International Conference on Automated Machine Learning (AutoML), 2023

• Discovering Optimal Scoring Mechanisms in Causal Strategic Prediction

  Tom Yan, Shantanu Gupta, Zachary Lipton
  arXiv (preprint), 2023

• Model Evaluation in Medical Datasets Over Time

  Helen Zhou*, Yuwen Chen*, Zachary Lipton
  Conference on Health, Inference, and Learning (CHIL), 2023

• Domain Adaptation under Missingness Shift

  Helen Zhou, Sivaraman Balakrishnan, Zachary Lipton
  Artificial Intelligence and Statistics (AISTATS), 2023

• Disentangling the Mechanisms Behind Implicit Regularization in SGD

  Zachary Novack, Simran Kaur, Tanya Marwah, Saurabh Garg, Zachary Lipton
  International Conference on Learning Representations (ICLR), 2023

• Deconstructing distributions: A pointwise framework of learning

  Gal Kaplun, Nkhil Ghosh, Saurabh Garg, Boaz Barak, Preetum Nakkiran
  International Conference on Learning Representations (ICLR), 2023

• Chils: Zero-shot image classification with hierarchical label sets

  Zachary Novack, Julian McAuley, Zachary Lipton, Saurabh Garg
  arXiv preprint arXiv:2302.02551, 2023

• RLSbench: Domain Adaptation Under Relaxed Label Shift

  Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary Lipton
  arXiv preprint arXiv:2302.03020, 2023

• Local Causal Discovery for Estimating Causal Effects

  Shantanu Gupta, David Childers, Zachary Lipton
  Causal Learning and Reasoning (CLeaR), 2023

• Factorial SDE for Multi-Output Gaussian Process Regression

  Daniel Jeong, Seyoung Kim
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2023

• Temporal Supervised Contrastive Learning with Applications to Tabular Time Series Data

  Shahriar Noroozizadeh, Jeremy Weiss, George Chen
  AAAI Workshop on Representation Learning for Responsible Human-Centric AI (R2HCAI), 2023

• ET tu, CLIP? Addressing Common Object Errors for Unseen Environments

  Ye Won Byun*, Cathy Jiao*, Shahriar Noroozizadeh*, Jimin Sun*, Rosa Vitiello*
  Conference on Computer Vision and Pattern Recognition - Embodied AI Workshop, 2022

• Improving the Robustness of Summarization Models by Detecting and Removing Input Noise

  Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J. Liu
  arXiv (preprint), 2022

• Assistive alignment of in-the-wild sheet music and performances

  Michael Feffer, Zachary Lipton, Chris Donahue
  23rd Int. Society for Music Information Retrieval Conf. (ISMIR LBD), 2022

• Doubly Mixed-Effects Gaussian Process Regression

  Jun Ho Yoon, Daniel Jeong, Seyoung Kim
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2022

• Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19

  Helen Zhou, Cheng Cheng, Kelly Shields, Gursimran Kocchar, Tariq Cheema, Zachary Lipton, Jeremy Weiss
  American Medical Informatics Association (AMIA) Annual Symposium, 2022

• A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms

  Nil-Jana Akpinar, Manish Nagireddy, Logan Stapleton, Hao-Fei Cheng, Haiyi Zhu, Steven Wu, Hoda Heidari
  Poster at the Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO 2022), 2022

• Unsupervised Learning under Latent Label Shift

  Manley Roberts*, Pranav Mani*, Saurabh Garg, Zachary Lipton
  Advances in Neural Information Processing Systems (NeurIPS), 2022

• Domain Adaptation under Open Set Label Shift

  Saurabh Garg, Sivaraman Balakrishnan, Zachary Lipton
  Advances in Neural Information Processing Systems (NeurIPS), 2022

• Counterfactual Metrics for Auditing Black-Box Recommender Systems for Ethical Concerns

  Nil-Jana Akpinar*, Liu Leqi*, Dylan Hadfield-Menell, Zachary Lipton
  Workshop on Responsible Decision Making in Dynamic Environments. International Conference on Machine Learning (ICML 2022), 2022

• Resolving the Human Subjects Status of Machine Learning's Crowdworkers

  Divyansh Kaushik, Zachary Lipton, Alex John London
  arXiv Preprint, 2022

• Practical Benefits of Feature Feedback Under Distribution Shift

  Anurag Katakkar*, Clay Yoo*, Weiqin Wang*, Zachary Lipton, Divyansh Kaushik
  BlackBox NLP workshop, 2022

• Long-Term Dynamics of Fairness Intervention in Connection Recommender Systems

  Nil-Jana Akpinar, Cyrus DiCiccio, Preetam Nandy, Kinjal Basu
  Conference on Artificial Intelligence, Ethics, and Society (AIES), 2022

• Modeling Attrition in Recommender Systems with Departing Bandits

  Omer Ben-Porat*, Lee Cohen*, Liu Leqi*, Zachary Lipton, Yishay Mansour
  Association for the Advancement of Artificial Intelligence (AAAI), 2022

• Off-Policy Risk Assessment for Markov Decision Processes

  Audrey Huang, Liu Leqi, Kamyar Azizzadenesheli, Zachary Lipton
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2022

• Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations

  Siddhant Arora*, Danish Pruthi*, Norman Sadeh, William Cohen, Zachary Lipton, Graham Neubig
  Association for the Advancement of Artificial Intelligence (AAAI), 2022

• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance

  Saurabh Garg, Sivaraman Balakrishnan, Zachary Lipton, Hanie Sedghi, Behnam Neyshabur
  International Conference on Learning Representations (ICLR), 2022

• Mixture Proportion Estimation and PU Learning: A Modern Approach

  Saurabh Garg, Yifan Wu, Alex Smola, Sivaraman Balakrishnan, Zachary Lipton
  Advances in Neural Information Processing Systems (NeurIPS), 2021

• Rebounding Bandits for Modeling Satiation Effects

  Liu Leqi, Fatma Kilinc-Karzan, Zachary Lipton, Alan Montgomery
  Advances in Neural Information Processing Systems (NeurIPS), 2021

• Parametric Complexity Bounds for Approximating PDEs with Neural Networks

  Tanya Marwah, Zachary Lipton, Andrej Risteski
  Advances in Neural Information Processing Systems (NeurIPS), 2021

• Does Pretraining for Summarization Require Knowledge Transfer?

  Kundan Krishna, Jeffrey Bigham, Zachary Lipton
  Findings of EMNLP, 2021

• Evaluating Explanations: How much do explanations from the teacher aid students?

  Danish Pruthi, Rachit Bansal, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary Lipton, Graham Neubig, William Cohen
  Transactions of the Association for Computational Linguistics (TACL), 2021

• Efficient Online Estimation of Causal Effects by Deciding What to Observe

  Shantanu Gupta, Zachary Lipton, David Childers
  Advances in Neural Information Processing Systems (NeurIPS), 2021

• The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studies

  Riccardo Fogliato, Alexandra Chouldechova, Zachary Lipton
  Computer-supported cooperative work (CSCW), 2021

• When Curation Becomes Creation: Algorithms, microcontent, and the vanishing distinction between platforms and creators

  Liu Leqi, Zachary Lipton, Dylan Hadfield-Menell
  Communications of the ACM (CACM), 2021

• Off-Policy Risk Assessment in Contextual Bandits

  Audrey Huang, Liu Leqi, Zachary Lipton, Kamyar Azizzadenesheli
  Advances in Neural Information Processing Systems (NeurIPS), 2021

• On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study

  Divyansh Kaushik, Douwe Kiela, Zachary Lipton, Wen-tau Yih
  Association for Computational Linguistics and International Joint Conference on Natural Language Processing (ACL-IJCNLP), 2021

• Unpacking the Drop in COVID-19 Case Fatality Rates: A Study of National and Florida Line-Level Data

  Cheng Cheng*, Helen Zhou*, Jeremy Weiss, Zachary Lipton
  American Medical Informatics Association (AMIA) Annual Symposium, 2021

• Correcting Exposure Bias for Link Recommendation

  Shantanu Gupta, Hao Wang, Zachary Lipton, Yuyang Wang
  International Conference on Machine Learning (ICML), 2021

• The math is not the territory: navigating the free energy principle

  Mel Andrews
  Biology & Philosophy., 2021

• Dynabench: Rethinking Benchmarking in NLP

  Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, Zhiyi Ma, Tristan Thrush, Sebastian Riedel, Zeerak Waseem, Pontus Stenetorp, Robin Jia, Mohit Bansal, Christopher Potts, Adina Williams
  North American Chapter of the Association for Computational Linguistics (NAACL), 2021

• Estimating Treatment Effects with Observed Confounders and Mediators

  Shantanu Gupta, Zachary Lipton, David Childers
  Uncertainty in Artificial Intelligence (UAI), 2021

• On the Validity of Arrest as a Proxy for Offense: Race and the Likelihood of Arrest for Violent Crimes

  Riccardo Fogliato, Alice Xiang, Zachary Lipton, Daniel Nagin, Alexandra Chouldechova
  Conference on AI, Ethics, and Society (AIES), 2021

• RATT: Leveraging Unlabeled Data to Guarantee Generalization

  Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary Lipton
  International Conference on Machine Learning (ICML), 2021

• On Proximal Policy Optimization’s Heavy-Tailed Gradients

  Saurabh Garg, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Zachary Lipton, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Pradeep Ravikumar
  International Conference on Machine Learning (ICML), 2021

• Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability

  Jeremy Cohen, Simran Kaur, Yuanzhi Li, J. Zico Kolter, Ameet Talwalkar
  International Conference on Learning Representations (ICLR), 2021

• Do Context-Aware Translation Models Pay the Right Attention?

  Kayo Yin, Patrick Fernandes, Danish Pruthi, Aditi Chaudhary, André F. T. Martins, Graham Neubig
  Association for Computational Linguistics (ACL), 2021

• Generating SOAP Notes from Doctor-Patient Conversations

  Kundan Krishna, Sopan Khosla, Jeffrey Bigham, Jeffrey Bigham
  Association for Computational Linguistics (ACL), 2021

• Fair Machine Learning Under Partial Compliance

  Jessica Dai, Sina Fazelpour, Zachary Lipton
  Conference on Artificial Intelligence, Ethics, and Society (AIES), 2021

• The effect of differential victim crime reporting on predictive policing systems

  Nil-Jana Akpinar, Maria De-Arteaga, Alexandra Chouldechova
  Conference on Fairness, Accountability, and Transparency (FAccT 2021), 2021

• Explaining The Efficacy of Counterfactually-Augmented Data

  Divyansh Kaushik, Zachary Lipton
  International Conference on Learning Representations (ICLR), 2021

• Decoding and Diversity in Machine Translation

  Nick Roberts, Davis Liang, Graham Neubig, Zachary Lipton
  NeurIPS Resistance AI Workshop, 2020

• Weakly and Semisupervised Evidence Extraction

  Danish Pruthi, Bhuwan Dhingra, Graham Neubig, Zachary Lipton
  Findings of EMNLP, 2020

• On Negative Interference in Multilingual Models:Findings and A Meta-Learning Treatment

  Zirui Wang, Yulia Tsvetkov, Zachary Lipton
  Empirical Methods in Natural Language Processing (EMNLP), 2020

• Why and when should you pool? Analyzing Pooling in Recurrent Architectures

  Pratyush Maini, Keshav Kolluru, Danish Pruthi, Mausam
  Findings of EMNLP, 2020

• NeuSpell: A Neural Spelling Correction Toolkit

  Sai Muralidhar Jayanthi, Danish Pruthi, Graham Neubig
  EMNLP (Demo Track), 2020

• A Unified View of Label Shift Estimation

  Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary Lipton
  Advances in Neural Information Processing Systems (NeurIPS), 2020

• Algorithmic Fairness from a Non-Ideal Perspective

  Sina Fazelpour, Zachary Lipton
  AAAI/ACM Conference on Artificial Intelligence, Ethics and Society (AIES), 2020

• Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist Clinicians with COVID-19 ECMO Planning

  Helen Zhou*, Cheng Cheng*, Zachary Lipton, George Chen, Jeremy Weiss
  International Conference on Artificial Intelligence in Medicine (AIME), 2020

• Uncertainty-Aware Lookahead Factor Models for Quantitative Investing

  Lakshay Chauhan, John Alberg, Zachary Lipton
  International Conference on Machine Learning (ICML), 2020

• Uniform Convergence of Rank-weighted Learning

  Justin Khim, Liu Leqi, Adarsh Prasad, Pradeep Ravikumar
  International Conference on Machine Learning (ICML), 2020

• Automated Dependence Plots

  David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar
  Uncertainty in Artificial Intelligence (UAI), 2020

• Learning to Deceive with Attention-Based Explanations

  Danish Pruthi, Mansi Gupta, Bhuwan Dhingra, Graham Neubig, Zachary Lipton
  Association for Computational Linguistics (ACL), 2020

• Fairness Evaluation in the Presence of Biased Noisy Labels

  Riccardo Fogliato, Max G'Sell, Alexandra Chouldechova
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2020

• Analyzing Student Strategies In Blended Courses Using Clickstream Data

  Nil-Jana Akpinar, Aaditya Ramdas, Umut Acar
  Thirteenth International Conference on Educational Data Mining (EDM 2020), 2020

• A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores

  Maria De Arteaga*, Riccardo Fogliato*, Alexandra Chouldechova
  Conference on Human Factors in Computing Systems (CHI), 2020

• Efficient candidate screening under multiple tests and implications for fairness

  Lee Cohen, Zachary Lipton, Yishay Mansour
  Foundations of Responsible Computing Foundations of Responsible Computing (FORC), 2020

• Learning the Difference that Makes a Difference with Counterfactually-Augmented Data

  Divyansh Kaushik, Zachary Lipton
  International Conference on Learning Representations (ICLR), 2020

• Relative Pose Estimation for On-orbit Assembly using Convolutional Neural Networks

  Shubham Sonawani, Ryan Alimo, Renuad Detry, Daniel Jeong, Andrew Hess, Heni Ben Amor
  AIAA SciTech, 2020

• COSMIC: Content-based Onboard Summarization to Monitor Infrequent Change

  Gary Doran, Erik Langert, Steven Lu, Lukas Mandrake, Kiri Wagstaff, Jimmie Young, Anneliese Braunegg, Paul Horton, Daniel Jeong, Asher Trockman
  IEEE Aerospace Conference, 2020

• Sample Complexity Bounds for Recurrent Neural Networks with Application to Combinatorial Graph Problems

  Nil-Jana Akpinar, Bernhard Kratzwald, Stefan Feuerriegel
  Thirty-Fourth Conference on Artifical Intelligence (AAAI 2020) (Student Abstract), 2020

• Extracting Structured Data from Doctor-Patient Conversations by Predicting Noteworthy Utterances

  Kundan Krishna, Amy Pavel, Benjamin Schloss, Jeffrey Bigham, Zachary Lipton
  AAAI Workshop Health Intelligence, 2020

• Predicting Mortality in Liver Transplant Candidates

  Jonathon Byrd, Sivaraman Balakrishnan, Xiaoqian Jiang, Zachary Lipton
  AAAI Workshop Health Intelligence, 2020

• An end-to-end system for automatic characterization of iba1 immunopositive microglia in whole slide imaging

  Alexander D Kyriazis*, Shahriar Noroozizadeh*, Amir Refaee*, Woongcheol Choi*, Lap-Tak Chu*, Asma Bashir, Wai Hang Cheng, Rachel Zhao, Dhananjay R Namjoshi, Septimiu E Salcudean, Cheryl L Wellington, Guy Nir
  Neuroinfomatics Journal, 2019

• Causal Inference With Selectively-Deconfounded Data

  Kyra Gan, Andrew Li, Zachary Lipton, Sridhar Tayur
  NeurIPS Causality Workshop, 2019

• Are Perceptually-Aligned Gradients a General Property of Robust Classifiers?

  Simran Kaur, Jeremy Cohen, Zachary Lipton
  Neurips 2019 Science Meets Engineering of Deep Learning Workshop, 2019

• On Human-Aligned Risk Minimization

  Liu Leqi, Adarsh Prasad, Pradeep Ravikumar
  Advances in Neural Information Processing Systems (NeurIPS), 2019

• Learning Robust Global Representations by Penalizing Local Predictive Power

  Haohan Wang, Songwei Ge, Eric Xing, Zachary Lipton
  Advances in Neural Information Processing Systems (NeurIPS), 2019

• Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift

  Stefan Rabanser, Stephan Günnemann, Zachary Lipton
  Advances in Neural Information Processing Systems (NeurIPS), 2019

• Game Design for Eliciting Distinguishable Behavior

  Fan Yang, Liu Leqi, Yifan Wu, Zachary Lipton, Pradeep Ravikumar, William Cohen, Tom Mitchell
  Advances in Neural Information Processing Systems (NeurIPS), 2019

• Practical Obstacles to Deploying Active Learning

  David Lowell, Zachary Lipton, Byron Wallace
  Empirical Methods in Natural Language Processing (EMNLP), 2019

• Entity Projection via Machine Translation for Cross-Lingual NER

  Alankar Jain, Bhargavi Paranjape, Zachary Lipton
  Empirical Methods in Natural Language Processing (EMNLP), 2019

• AmazonQA: A Review-Based Question Answering Task

  Mansi Gupta, Nitish Kulkarni, Raghuveer Chanda, Anirudha Rayasam, Zachary Lipton
  International Joint Conference on Machine Learning (IJCAI), 2019

• Embryo staging with weakly-supervised region selection and dynamically- decoded predictions

  Tingfung Lau, Nathan Ng, Julian Gingold, Nina Desai, Julian McAuley, Zachary Lipton
  Machine Learning for Healthcare (MLHC), 2019

• Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment

  Yifan Wu, Divyansh Kaushik, Ezra Winston, Zachary Lipton
  International Conference on Machine Learning (ICML), 2019

• What is the Effect of Importance Weighting in Deep Learning?

  Jonathon Byrd, Zachary Lipton
  International Conference on Machine Learning (ICML), 2019

• Estimating brain age based on a uniform healthy population with deep learning and structural MRI

  Xinyang Feng, Zachary Lipton, Jie Yang, Scott Small, Frank Provenzano
  Neurobiology of Aging, 2019

• Combating Adversarial Misspellings with Robust Word Recognition

  Danish Pruthi, Bhuwan Dhingra, Zachary Lipton
  Association for Computational Linguistics (ACL), 2019

• Troubling Trends in Machine Learning Scholarship

  Zachary Lipton*, Jacob Steinhardt*
  Communications of the ACM (CACM), 2019

• Reinforcement Learning with Unknown Reward Functions

  Benjamin Eysenbach*, Jacob Tyo*, Shane Gu, Ruslan Salakhutdinov, Zachary Lipton, Sergey Levine
  Task-Agnostic Reinforcement Learning Workshop, ICLR, 2019

• Learning Robust Representations by Projecting Superficial Statistics Out

  Haohan Wang, Zexue Hu, Zachary Lipton, Eric Xing
  International Conference on Learning Representations (ICLR), 2019

• Active Learning with Partial Feedback

  Peiyun Hu, Zachary Lipton, Anima Anandkumar, Deva Ramanan
  International Conference on Learning Representations (ICLR), 2019

• Protein structure prediction with deep learning representations

  Iddo Drori, Darshan Thaker, Arjun Srivatsa, Daniel Jeong, Yueqi Wang, Linyong Nan, Fan Wu, Dimitri Leggas, Jinhao Lei, Weiyi Lu, Weilong Fu, Yuan Gao, Sashank Karri, Anand Kannan, Antonio Moretti, Chen Keasar, Itsik Pe’er
  NeurIPS Learning Meaningful Representations of Life Workshop, 2019

• Protein Structure Prediction by Embeddings and Deep Learning Representations

  Iddo Drori, Darshan Thaker, Arjun Srivatsa, Daniel Jeong, Yueqi Wang, Linyong Nan, Fan Wu, Dimitri Leggas, Jinhao Lei, Weiyi Lu, Weilong Fu, Yuan Gao, Sashank Karri, Anand Kannan, Antonio Moretti, Mohammed AlQuraishi, Chen Keasar, Itsik Pe’er
  Machine Learning in Computational Biology (MLCB), 2019

• Does Mitigating ML’s Impact Disparity Require Treatment Disparity?

  Zachary Lipton, Alexandra Chouldechova, Julian McAuley
  Advances in Neural Information Processing (NeurIPS), 2018

• How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks

  Divyansh Kaushik, Zachary Lipton
  Empirical Methods in Natural Language Processing (EMNLP), 2018

• Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study

  Aditya Siddhant, Zachary Lipton
  Empirical Methods in Natural Language Processing (EMNLP), 2018

• Learning Noise-Invariant Representations for Robust Speech Recognition

  Davis Liang, Zhiheng Huang, Zachary Lipton
  IEEE Speech and Language Technology (SLT), 2018

• Born Again Networks

  Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar
  International Conference on Machine Learning (ICML), 2018

• Detecting and Correcting for Label Shift with Black Box Predictors

  Zachary Lipton*, Yu-Xiang Wang*, Alex Smola
  International Conference on Machine Learning (ICML), 2018

• Semantically Decomposing the Latent Spaces of Generative Adversarial Networks

  Chris Donahue, Zachary Lipton, Akshay Balsubramani, Julian McAuley
  International Conference on Learning Representations (ICLR), 2018

• Stochastic Activation Pruning for Robust Adversarial Defense

  Guneet Dhillon Singh, Kamyar Azizzadenesheli, Jeremy Bernstein, Aran Khanna, Zachary Lipton, Anima Anandkumar
  International Conference on Learning Representations (ICLR), 2018

• Learning from Noisy Singly-Labeled Data

  Ashish Khetan, Zachary Lipton, Anima Anandkumar
  International Conference on Learning Representations (ICLR), 2018

• Deep Active Learning for Named Entity Recognition

  Yanyao Shen, Hyokun Yun, Zachary Lipton, Anima Anandkumar
  International Conference on Learning Representations (ICLR), 2018

• SPINE: SParse Interpretable Neural Embeddings

  Danish Pruthi*, Anant Subramanian*, Harsh Jhamtani*, Taylor Berg-Kirkpatrick, Eduard Hovy
  Association for the Advancement of Artificial Intelligence (AAAI), 2018

• Efficient exploration for dialogue policy learning with BBQ-networks

  Zachary Lipton, Xiujun Li, Jianfeng Gao, Lihong Li, Li Deng
  Association for the Advancement of Artificial Intelligence (AAAI), 2018

• Dance Dance Convolution

  Chris Donahue, Zachary Lipton, Julian McAuley
  International Conference on Machine Learning (ICML), 2017

• Predicting Surgery Duration with Neural Heteroscedastic Regression

  Nathan Ng, Julian McAuley, Charles Elkan, Zachary Lipton
  Machine Learning for Healthcare (MLHC), 2017

• Context Matters: Refining Object Detection in Video with Recurrent Neural Networks

  Zachary Lipton, Subarna Tripathi, Serge Belongie, Truong Nguyen
  British Machine Vision Conference (BMVC), 2016

• Modeling Missing Data in Clinical Time Series with RNNs

  Zachary Lipton, David Kale, Randall Wetzel
  Machine Learning for Healthcare (MLHC), 2016

• The Mythos of Model Interpretability

  Zachary Lipton
  Communications of the ACM (CACM), 2016

• Learning to Diagnose with LSTM Recurrent Neural Networks

  Zachary Lipton, David Kale*, Charles Elkan, Randall Wetzel
  International Conference on Learning Representations (ICLR), 2016

• Playing the Imitation Game with deep learning

  Zachary Lipton, Charles Elkan
  IEEE Spectrum, 2016

• Optimal thresholding of classifiers to maximize F1 measure

  Zachary Lipton, Balakrishnan Narayanaswamy, Charles Elkan
  European Conference on Machine Learning (ECML), 2014