Papers

• A Unified View of Label Shift Estimation

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

• Estimating Treatment Effects with Observed Confounders and Mediators

  Shantanu Gupta, Zachary Lipton, David Childers
  arXiv, 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

• 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 Counter-factually Augmented Data

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

• 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

• 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

• 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