Carnegie Mellon University

Zachary Lipton

Zachary Chase Lipton

Assistant Professor of Business Technologies

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  • TEP - Tepper Building - Room 4116
  • 412-268-8837
Address
5000 Forbes Avenue
Pittsburgh, PA 15213

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

  • 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