Carnegie Mellon University

Zachary Lipton

Zachary Chase Lipton

Assistant Professor of Operations Research and Machine Learning

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Address
5000 Forbes Avenue
Pittsburgh, PA 15213

Areas of Study

Operations Research

Education

  • University of California, San Diego - Ph D (Computer Science) - 2017
  • University of California, San Diego - MS (Computer Science) - 2015
  • Columbia University - BA (Economics-Mathematics) - 2007

Research

Machine Learning Algorithms, Machine Learning for Healthcare, Empirical Deep Learning, Foundations of Deep Learning, Natural Language Processing, Social and Economic Impacts of Machine Learning, Reinforcement Learning, Robustness to Distribution Shift, and Causal Inference and Discovery (recently).

Publications

  • On Proximal Policy Optimization's Heavy-tailed Gradients
    (author(s): Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Zachary C. Lipton, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Pradeep Kumar Ravikumar)
    International Conference on Machine Learning (ICML), 2021
  • RATT: Leveraging Unlabeled Data to Guarantee Generalization
    (author(s): Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary C. Lipton)
    International Conference on Machine Learning (ICML), 2021
  • Correcting Exposure Bias for Link Recommendation
    (author(s): Shantanu Gupta, Zachary C. Lipton, Hao Wang)
    International Conference on Machine Learning (ICML), 2021
  • Estimating Treatment Effects with Observed Confounders and Mediators
    (author(s): Shantanu Gupta, Zachary C. Lipton, David Childers)
    Uncertainty in Artificial Intelligence (UAI), 2021
  • Generating SOAP Notes From Doctor-Patient Conversations
    (author(s): Kundan Krishna, Jeffrey Bigham, Zachary C. Lipton)
    Association for Computational Linguistics (ACL), 2021
  • On the Validity of Arrest as a Proxy for Offense: Race and the Likelihood of Arrest for Violent Crimes
    (author(s): Riccardo Fogliato, Alice Xiang, Dan Nagin, Zachary C. Lipton, Alex Chouldechova)
    Conference on Artificial Intelligence, Ethics, and Society (AIES), 2021
  • Fair Machine Learning Under Partial Compliance
    (author(s): Jessica Dai, Sina Fazelpour, Zachary C. Lipton)
    Conference on Artificial Intelligence, Ethics, and Society (AIES), 2021
  • Symbolic Music Generation with Transformer-GANs
    (author(s): Aashiq Muhamed, Liang Li, Xingjian Shi, Suri Yaddanapudi, Wayne Chi, Dylan Jackson, Rahul Suresh, Zachary C. Lipton, Alexander J. Smola)
    Association for the Advancement of Artificial Intelligence (AAAI), 2021
  • Explaining the Efficacy of Counterfactually-Augmented Data
    (author(s): Divyansh Kaushik, Amrith Setlur, Ed Hovy, Zachary C. Lipton)
    International Conference on Learning Representations (ICLR), 2021
  • Estimating Treatment Effects with Selectively-Deconfounded Data
    (author(s): Kyra Gan, Andrew Li, Zachary C. Lipton, Sridhar Tayur)
    Artificial Intelligence and Statistics (AISTATS), 2021
  • Tensor Regression Networks
    (author(s): Jean Kossaifi, Zachary C. Lipton, Tommaso Furlanello, Anima Anandkumar)
    Journal for Machine Learning Research (JMLR), 2020
  • Estimating Brain Age Based on a Uniform Healthy Population with Deep Learning and Structural MRI
    (author(s): Xinyang Feng, Zachary C. Lipton, Jie Yang, Scott A. Small, Frank A. Provenzano)
    Neurobiology of Aging, 2020
  • Mortality Risk Score for Critically Ill Patients with Viral or Unspecified Pneumonia: Assisting Clinicians with COVID-19 ECMO Planning
    (author(s): Helen Zhou, Cheng Cheng, Zachary C. Lipton, George Chen, Jeremy C. Weiss)
    International Conference on Artificial Intelligence in Medicine (AIME), 2020
  • A Unified View of Label Shift Estimation
    (author(s): Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton)
    Advances in Neural Information Processing (NeurIPS), 2020
  • Weakly-and Semi-supervised Evidence Extraction
    (author(s): Danish Pruthi, Bhuwan Dhingra, Graham Neubig, Zachary C. Lipton)
    Findings of EMNLP, 2020
  • On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment
    (author(s): Zirui Wang, Zachary C. Lipton, Yulia Tsvetkov)
    Empirical Methods in Natural Language Processing (EMNLP), 2020
  • Learning to Deceive with Attention-Based Explanations
    (author(s): Danish Pruthi, Mansi Gupta, Bhuwan Dhingra, Graham Neubig, Zachary C. Lipton)
    Association for Computational Linguistics (ACL), 2020
  • Efficient Candidate Screening under Multiple Tests and Implications for Fairness
    (author(s): Lee Cohen, Zachary C. Lipton, Yishay Mansour)
    Foundations of Responsible Computing Foundations of Responsible Computing (FORC), 2020
  • Algorithmic Fairness from a Non-Ideal Perspective
    (author(s): Sina Fazelpour, Zachary C. Lipton)
    AAAI/ACM Conference on Artificial Intelligence, Ethics and Society (AIES), 2020
  • Learning the Difference that Makes a Difference with Counter-factually Augmented Data
    (author(s): Divyansh Kaushik, Ed Hovy, Zachary C. Lipton)
    International Conference on Representation Learning (ICLR), 2020
  • Learning Robust Global Representations by Penalizing Local Predictive Power
    (author(s): Haohan Wang, Songwei Ge, Eric P Xing, Zachary C, Lipton)
    Advances in Neural Information Processing Systems (NeurIPS), 2019
  • Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
    (author(s): Stephan Rabanaser, Stephan Gunneman, Zachary C. Lipton)
    Advances in Neural Information Processing Systems (NeurIPS), 2019
  • Game Design for Eliciting Distinguishable Behavior
    (author(s): Fan Yang, Leqi Liu, Yifan Wu, Zachary C. Lipton, Pradeep Ravikumar, Tom Mitchell, William Cohen)
    Advances in Neural Information Processing Systems (NeurIPS), 2019
  • Combating Adversarial Misspellings with Robust Word Recognition
    (author(s): Danish Pruthi, Bhuwan Dhingra, Zachary C. Lipton)
    Association for Computational Linguistics (ACL), 2019
  • Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment
    (author(s): Yifan Wu, Ezra Winston, Divyansh Kaushik, Zachary C. Lipton)
    International Conference on Machine Learning (ICML), 2019
  • What is the Effect of Importance Weighting in Deep Learning?
    (author(s): Jonathon Byrd, Zachary C. Lipton)
    International Conference on Machine Learning (ICML), 2019
  • Troubling Trends in Machine Learning Scholarship
    (author(s): Zachary C. Lipton, Jacob Steinhardt)
    Communications of the ACM, June 2019
  • Embryo Staging with Weakly-supervised Region Selection and Dynamically-Decoded Predictions
    (author(s): Tingfung Lau, Nathan Ng, Julian Gingold, Nina Desai, Julian McAuley, Zachary C. Lipton)
    Machine Learning for Healthcare (MLHC), 2019
  • AmazonQA: A Review-Based Question Answering Task
    (author(s): Mansi Gupta, Nitish Kulkarni, Raghuveer Chanda, Anirudha Rayasam, and Zachary C. Lipton)
    International Joint Conference on Artificial Intelligence (IJCAI), 2019
  • Learning Robust Representations by Projecting Superficial Statistics Out
    (author(s): Haohan Wang, Zexue Hu, Zachary C. Lipton, Eric Xing)
    International Conference on Learning Representations (ICLR), 2019, Oral Presentation (top 2% of papers)
  • Active Learning with Partial Feedback
    (author(s): Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, Deva Ramanan)
    International Conference on Learning Representations (ICLR), 2019
  • The Mythos of Model Interpretability
    (author(s): Zachary C. Lipton)
    Communications of the ACM, October 2018
  • Does Mitigating ML’s Impact Disparity Require Treatment Disparity?
    (author(s): Zachary C. 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
    (author(s): Divyansh Kaushik, Zachary C. Lipton)
    Empirical Methods in Natural Language Processing (EMNLP), 2018, Best Short Paper
  • Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study
    (author(s): Aditya Siddhant, Zachary C. Lipton)
    Empirical Methods in Natural Language Processing (EMNLP), 2018
  • Learning Noise-Invariant Representations for Robust Speech Recognition
    (author(s): Davis Liang, Zhiheng Huang, Zachary C. Lipton)
    IEEE Speech and Language Technology (SLT), 2018
  • Detecting and Correcting for Label Shift with Black Box Predictors
    (author(s): Zachary C. Lipton, Yu-Xiang Wang, Alex Smola)
    International Conference on Machine Learning (ICML), 2018
  • Born Again Networks
    (author(s): Tommaso Furlanello, Zachary C Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar)
    International Conference on Machine Learning (ICML), 2018
  • Semantically Decomposing the Latent Spaces of Generative Adversarial Networks
    (author(s): Chris Donahue, Zachary C. Lipton, Akshay Balsubramani, Julian McAuley)
    International Conference on Learning Representations (ICLR), 2018
  • Stochastic Activation Pruning for Robust Adversarial Defense
    (author(s): Guneet Dhillon Singh, Kamyar Azizzadenesheli, Jeremy Bernstein, Aran Khanna, Zachary C. Lipton, Anima Anandkumar )
    International Conference on Learning Representations (ICLR), 2018
  • Learning from Noisy Singly-Labeled Data
    (author(s): Ashish Khetan, Zachary C. Lipton, Anima Anandkumar)
    International Conference on Learning Representations (ICLR), 2018
  • Deep Active Learning for Named Entity Recognition
    (author(s): Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Anima Anandkumar)
    International Conference on Learning Representations (ICLR), 2018
  • Efficient Exploration for Dialogue Policy Learning with BBQ-Networks
    (author(s): Zachary C. Lipton, Xiujun Li, Jianfeng Gao, Lihong Li, Li Deng)
    Association for the Advancement of Artificial Intelligence (AAAI), 2018
  • Dance Dance Convolution
    (author(s): Chris Donahue, Zachary C. Lipton, Julian McAuley)
    International Conference on Machine Learning (ICML) 2017
  • Predicting Surgery Duration with Neural Heteroscedastic Regression
    (author(s): Nathan Ng, Julian McAuley, Charles Elkan, Zachary C. Lipton)
    Machine Learning for Healthcare (MLHC), 2017
  • Playing the Imitation Game with Deep Learning
    (author(s): Zachary C. Lipton, Charles Elkan)
    IEEE Spectrum, January 2016
  • Context Matters: Refining Object Detection in Video with Recurrent Neural Networks
    (author(s): Subarna Tripathi, Zachary C. Lipton, Serge Belongie, Truong Nguyen)
    British Machine Vision Conference (BMVC), 2016
  • Modeling Missing Data in Clinical Time Series with RNNs
    (author(s): Zachary C. Lipton, David Kale, Randall Wetzel)
    Machine Learning for Healthcare (MLHC), 2016
  • Learning to Diagnose with LSTM Recurrent Neural Networks
    (author(s): Zachary C. Lipton, David Kale, Charles Elkan, Randall Wetzel)
    International Conference on Learning Representations (ICLR), 2016
  • Optimal Thresholding of Classifiers to Maximize F1 Measure
    (author(s): Zachary C. Lipton, Balakrishnan Narayanaswamy, Charles Elkan)
    European Conference on Machine Learning (ECML), 2014

Working Papers

  • Algorithmic Fairness & the Situated Dynamics of Justice
    (author(s): Sina Fazelpour, David Danks, Zachary C. Lipton)
     2021
  • Rebounding Bandits for Modeling Satiation Effects
    (author(s): Leqi Liu, Fatma Kilinc-Karzan, Zachary C. Lipton, Alan Montgomery)
    2021
  • On the Convergence and Optimality of Policy Gradient for Coherent Risk
    (author(s): Audrey Huang, Leqi Liu, Zachary C. Lipton, Kamyar Azzizadenesheli)
    2021
  • Parametric Complexity Bounds for Approximating PDEs with Neural Networks
    (author(s): Tanya Marwah, Zachary C. Lipton, Andrej Risteski)
    2021
  • Off-Policy Risk Assessment in Contextual Bandits
    (author(s): Audrey Huang, Leqi Liu, Zachary C. Lipton, Kamyar Azzizadenesheli)
    2021
  • Evaluating Explanations: How much do Explanations from the Teacher Aid Students?
    (author(s): Danish Pruthi, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary Lipton, William Cohen, Graham Neubig)
    2021