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ORCIDZachary C. Lipton
Zachary Chase Lipton
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- affiliation: Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA
- affiliation (PhD 2017): University of California, San Diego, CA, USA
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2020 – today
- 2022
[j9]Danish Pruthi, Rachit Bansal, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C. Lipton, Graham Neubig, William W. Cohen:
Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students? Trans. Assoc. Comput. Linguistics 10: 359-375 (2022)- [c66]Audrey Huang, Liu Leqi, Zachary C. Lipton, Kamyar Azizzadenesheli:
Off-Policy Risk Assessment for Markov Decision Processes. AISTATS 2022: 5022-5050 - [i90]Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton, Behnam Neyshabur, Hanie Sedghi:
Leveraging Unlabeled Data to Predict Out-of-Distribution Performance. CoRR abs/2201.04234 (2022) - [i89]Yi-Fan Zhang, Hanlin Zhang, Zachary C. Lipton, Li Erran Li, Eric P. Xing:
Can Transformers be Strong Treatment Effect Estimators? CoRR abs/2202.01336 (2022) - [i88]Omer Ben-Porat, Lee Cohen, Liu Leqi, Zachary C. Lipton, Yishay Mansour:
Modeling Attrition in Recommender Systems with Departing Bandits. CoRR abs/2203.13423 (2022) - [i87]Riccardo Fogliato, Sina Fazelpour, Shantanu Gupta, Zachary C. Lipton, David Danks:
Homophily and Incentive Effects in Use of Algorithms. CoRR abs/2205.09701 (2022) - [i86]Divyansh Kaushik, Zachary C. Lipton, Alex John London:
Resolving the Human Subjects Status of Machine Learning's Crowdworkers. CoRR abs/2206.04039 (2022) - [i85]Simran Kaur, Jeremy M. Cohen, Zachary C. Lipton:
On the Maximum Hessian Eigenvalue and Generalization. CoRR abs/2206.10654 (2022) - 2021
- [j8]Liu Leqi, Dylan Hadfield-Menell, Zachary C. Lipton:
When curation becomes creation. Commun. ACM 64(12): 44-47 (2021) - [j7]Riccardo Fogliato, Alexandra Chouldechova, Zachary C. Lipton:
The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studies. Proc. ACM Hum. Comput. Interact. 5(CSCW2): 1-24 (2021) - [j6]Liu Leqi, Dylan Hadfield-Menell, Zachary C. Lipton:
When Curation Becomes Creation: Algorithms, microcontent, and the vanishing distinction between platforms and creators. ACM Queue 19(3): 11-15 (2021) - [c65]Aashiq Muhamed, Liang Li, Xingjian Shi, Suri Yaddanapudi, Wayne Chi, Dylan Jackson, Rahul Suresh, Zachary C. Lipton, Alexander J. Smola:
Symbolic Music Generation with Transformer-GANs. AAAI 2021: 408-417 - [c64]Kundan Krishna, Sopan Khosla, Jeffrey P. Bigham, Zachary C. Lipton:
Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques. ACL/IJCNLP (1) 2021: 4958-4972 - [c63]Divyansh Kaushik, Douwe Kiela, Zachary C. Lipton, Wen-tau Yih:
On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study. ACL/IJCNLP (1) 2021: 6618-6633 - [c62]Jessica Dai, Sina Fazelpour, Zachary C. Lipton:
Fair Machine Learning Under Partial Compliance. AIES 2021: 55-65 - [c61]Riccardo Fogliato, Alice Xiang, Zachary C. Lipton, Daniel Nagin, Alexandra Chouldechova:
On the Validity of Arrest as a Proxy for Offense: Race and the Likelihood of Arrest for Violent Crimes. AIES 2021: 100-111 - [c60]Kyra Gan, Andrew A. Li, Zachary Chase Lipton, Sridhar R. Tayur:
Causal Inference with Selectively Deconfounded Data. AISTATS 2021: 2791-2799 - [c59]Jacob Tyo, Bhuwan Dhingra, Zachary C. Lipton:
Siamese Bert for Authorship Verification. CLEF (Working Notes) 2021: 2169-2177 - [c58]Kundan Krishna, Jeffrey P. Bigham, Zachary C. Lipton:
Does Pretraining for Summarization Require Knowledge Transfer? EMNLP (Findings) 2021: 3178-3189 - [c57]David Lowell, Brian E. Howard, Zachary C. Lipton, Byron C. Wallace:
Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data. EMNLP (1) 2021: 4992-5001 - [c56]Divyansh Kaushik, Amrith Setlur, Eduard H. Hovy, Zachary Chase Lipton:
Explaining the Efficacy of Counterfactually Augmented Data. ICLR 2021 - [c55]Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary C. Lipton:
RATT: Leveraging Unlabeled Data to Guarantee Generalization. ICML 2021: 3598-3609 - [c54]Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Zachary C. Lipton, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Pradeep Ravikumar:
On Proximal Policy Optimization's Heavy-tailed Gradients. ICML 2021: 3610-3619 - [c53]Shantanu Gupta, Hao Wang, Zachary C. Lipton, Yuyang Wang:
Correcting Exposure Bias for Link Recommendation. ICML 2021: 3953-3963 - [c52]Liu Leqi, Fatma Kilinç-Karzan, Zachary C. Lipton, Alan L. Montgomery:
Rebounding Bandits for Modeling Satiation Effects. NeurIPS 2021: 4003-4014 - [c51]Saurabh Garg, Yifan Wu, Alexander J. Smola, Sivaraman Balakrishnan, Zachary C. Lipton:
Mixture Proportion Estimation and PU Learning: A Modern Approach. NeurIPS 2021: 8532-8544 - [c50]Tanya Marwah, Zachary C. Lipton, Andrej Risteski:
Parametric Complexity Bounds for Approximating PDEs with Neural Networks. NeurIPS 2021: 15044-15055 - [c49]Shantanu Gupta, Zachary C. Lipton, David Childers:
Efficient Online Estimation of Causal Effects by Deciding What to Observe. NeurIPS 2021: 20995-21007 - [c48]Audrey Huang, Liu Leqi, Zachary C. Lipton, Kamyar Azizzadenesheli:
Off-Policy Risk Assessment in Contextual Bandits. NeurIPS 2021: 23714-23726 - [c47]Shantanu Gupta, Zachary C. Lipton, David Childers:
Estimating treatment effects with observed confounders and mediators. UAI 2021: 982-991 - [i84]Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Sivaraman Balakrishnan, Zachary C. Lipton, Ruslan Salakhutdinov, Pradeep Ravikumar:
On Proximal Policy Optimization's Heavy-tailed Gradients. CoRR abs/2102.10264 (2021) - [i83]Tanya Marwah, Zachary C. Lipton, Andrej Risteski:
Parametric Complexity Bounds for Approximating PDEs with Neural Networks. CoRR abs/2103.02138 (2021) - [i82]Audrey Huang, Liu Leqi, Zachary C. Lipton, Kamyar Azizzadenesheli:
On the Convergence and Optimality of Policy Gradient for Markov Coherent Risk. CoRR abs/2103.02827 (2021) - [i81]Audrey Huang, Liu Leqi, Zachary C. Lipton, Kamyar Azizzadenesheli:
Off-Policy Risk Assessment in Contextual Bandits. CoRR abs/2104.08977 (2021) - [i80]Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary C. Lipton:
RATT: Leveraging Unlabeled Data to Guarantee Generalization. CoRR abs/2105.00303 (2021) - [i79]Divyansh Kaushik, Douwe Kiela, Zachary C. Lipton, Wen-tau Yih:
On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study. CoRR abs/2106.00872 (2021) - [i78]Shantanu Gupta, Hao Wang, Zachary C. Lipton, Yuyang Wang:
Correcting Exposure Bias for Link Recommendation. CoRR abs/2106.07041 (2021) - [i77]Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola:
Dive into Deep Learning. CoRR abs/2106.11342 (2021) - [i76]Liu Leqi, Dylan Hadfield-Menell, Zachary C. Lipton:
When Curation Becomes Creation: Algorithms, Microcontent, and the Vanishing Distinction between Platforms and Creators. CoRR abs/2107.00441 (2021) - [i75]Shantanu Gupta, Zachary C. Lipton, David Childers:
Efficient Online Estimation of Causal Effects by Deciding What to Observe. CoRR abs/2108.09265 (2021) - [i74]Riccardo Fogliato, Alexandra Chouldechova, Zachary C. Lipton:
The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studies. CoRR abs/2109.01443 (2021) - [i73]Kundan Krishna, Jeffrey P. Bigham, Zachary C. Lipton:
Does Pretraining for Summarization Require Knowledge Transfer? CoRR abs/2109.04953 (2021) - [i72]Anurag Katakkar, Weiqin Wang, Clay H. Yoo, Zachary C. Lipton, Divyansh Kaushik:
Practical Benefits of Feature Feedback Under Distribution Shift. CoRR abs/2110.07566 (2021) - [i71]Saurabh Garg, Yifan Wu, Alex Smola, Sivaraman Balakrishnan, Zachary C. Lipton:
Mixture Proportion Estimation and PU Learning: A Modern Approach. CoRR abs/2111.00980 (2021) - [i70]Siddhant Arora, Danish Pruthi, Norman M. Sadeh, William W. Cohen, Zachary C. Lipton, Graham Neubig:
Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations. CoRR abs/2112.09669 (2021) - 2020
- [j5]Jean Kossaifi, Zachary C. Lipton, Arinbjörn Kolbeinsson, Aran Khanna, Tommaso Furlanello, Anima Anandkumar:
Tensor Regression Networks. J. Mach. Learn. Res. 21: 123:1-123:21 (2020) - [c46]Danish Pruthi, Mansi Gupta, Bhuwan Dhingra, Graham Neubig, Zachary C. Lipton:
Learning to Deceive with Attention-Based Explanations. ACL 2020: 4782-4793 - [c45]Sina Fazelpour, Zachary C. Lipton:
Algorithmic Fairness from a Non-ideal Perspective. AIES 2020: 57-63 - [c44]Helen Zhou
, Cheng Cheng, Zachary C. Lipton, George H. Chen, Jeremy C. Weiss:
Mortality Risk Score for Critically Ill Patients with Viral or Unspecified Pneumonia: Assisting Clinicians with COVID-19 ECMO Planning. AIME 2020: 336-347 - [c43]Danish Pruthi, Bhuwan Dhingra, Graham Neubig, Zachary C. Lipton:
Weakly- and Semi-supervised Evidence Extraction. EMNLP (Findings) 2020: 3965-3970 - [c42]Zirui Wang, Zachary C. Lipton, Yulia Tsvetkov:
On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment. EMNLP (1) 2020: 4438-4450 - [c41]Lee Cohen, Zachary C. Lipton, Yishay Mansour:
Efficient Candidate Screening Under Multiple Tests and Implications for Fairness. FORC 2020: 1:1-1:20 - [c40]Divyansh Kaushik, Eduard H. Hovy, Zachary Chase Lipton:
Learning The Difference That Makes A Difference With Counterfactually-Augmented Data. ICLR 2020 - [c39]Lakshay Chauhan, John Alberg, Zachary C. Lipton:
Uncertainty-Aware Lookahead Factor Models for Quantitative Investing. ICML 2020: 1489-1499 - [c38]Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton:
A Unified View of Label Shift Estimation. NeurIPS 2020 - [c37]Nihar B. Shah, Zachary C. Lipton:
SIGMOD 2020 Tutorial on Fairness and Bias in Peer Review and Other Sociotechnical Intelligent Systems. SIGMOD Conference 2020: 2637-2640 - [c36]Zachary C. Lipton:
Machine Learning for Healthcare: Beyond i.i.d. Prediction. HSDM@WSDM 2020: 1 - [i69]Sina Fazelpour, Zachary C. Lipton:
Algorithmic Fairness from a Non-ideal Perspective. CoRR abs/2001.09773 (2020) - [i68]Jacob Tyo, Zachary C. Lipton:
How Transferable are the Representations Learned by Deep Q Agents? CoRR abs/2002.10021 (2020) - [i67]Kyra Gan, Andrew A. Li, Zachary C. Lipton, Sridhar R. Tayur:
Causal Inference With Selectively-Deconfounded Data. CoRR abs/2002.11096 (2020) - [i66]Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton:
A Unified View of Label Shift Estimation. CoRR abs/2003.07554 (2020) - [i65]Shantanu Gupta, Zachary C. Lipton, David Childers:
Estimating Treatment Effects with Observed Confounders and Mediators. CoRR abs/2003.11991 (2020) - [i64]Kundan Krishna, Sopan Khosla, Jeffrey P. Bigham, Zachary C. Lipton:
Generating SOAP Notes from Doctor-Patient Conversations. CoRR abs/2005.01795 (2020) - [i63]Helen Zhou, Cheng Cheng, Zachary C. Lipton, George H. Chen, Jeremy C. Weiss:
Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist Clinicians with COVID-19 ECMO Planning. CoRR abs/2006.01898 (2020) - [i62]Lakshay Chauhan, John Alberg, Zachary C. Lipton:
Uncertainty-Aware Lookahead Factor Models for Quantitative Investing. CoRR abs/2007.04082 (2020) - [i61]Kundan Krishna, Amy Pavel, Benjamin Schloss, Jeffrey P. Bigham, Zachary C. Lipton:
Extracting Structured Data from Physician-Patient Conversations By Predicting Noteworthy Utterances. CoRR abs/2007.07151 (2020) - [i60]Divyansh Kaushik, Amrith Setlur, Eduard H. Hovy, Zachary C. Lipton:
Explaining The Efficacy of Counterfactually-Augmented Data. CoRR abs/2010.02114 (2020) - [i59]Zirui Wang, Zachary C. Lipton, Yulia Tsvetkov:
On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment. CoRR abs/2010.03017 (2020) - [i58]David Lowell, Brian E. Howard, Zachary C. Lipton, Byron C. Wallace:
Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data. CoRR abs/2010.11966 (2020) - [i57]Danish Pruthi, Bhuwan Dhingra, Graham Neubig, Zachary C. Lipton:
Weakly- and Semi-supervised Evidence Extraction. CoRR abs/2011.01459 (2020) - [i56]Jessica Dai, Sina Fazelpour, Zachary C. Lipton:
Fair Machine Learning Under Partial Compliance. CoRR abs/2011.03654 (2020) - [i55]Liu Leqi, Fatma Kilinç-Karzan, Zachary C. Lipton, Alan L. Montgomery:
Rebounding Bandits for Modeling Satiation Effects. CoRR abs/2011.06741 (2020) - [i54]Nicholas Roberts, Davis Liang, Graham Neubig, Zachary C. Lipton:
Decoding and Diversity in Machine Translation. CoRR abs/2011.13477 (2020) - [i53]Danish Pruthi, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C. Lipton, Graham Neubig, William W. Cohen:
Evaluating Explanations: How much do explanations from the teacher aid students? CoRR abs/2012.00893 (2020)
2010 – 2019
- 2019
- [j4]Zachary C. Lipton, Jacob Steinhardt:
Research for practice: troubling trends in machine-learning scholarship. Commun. ACM 62(6): 45-53 (2019) - [j3]Zachary C. Lipton, Jacob Steinhardt:
Troubling Trends in Machine Learning Scholarship. ACM Queue 17(1): 80 (2019) - [c35]Danish Pruthi, Bhuwan Dhingra, Zachary C. Lipton:
Combating Adversarial Misspellings with Robust Word Recognition. ACL (1) 2019: 5582-5591 - [c34]David Lowell, Zachary C. Lipton, Byron C. Wallace:
Practical Obstacles to Deploying Active Learning. EMNLP/IJCNLP (1) 2019: 21-30 - [c33]Alankar Jain, Bhargavi Paranjape, Zachary C. Lipton:
Entity Projection via Machine Translation for Cross-Lingual NER. EMNLP/IJCNLP (1) 2019: 1083-1092 - [c32]Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, Deva Ramanan:
Active Learning with Partial Feedback. ICLR (Poster) 2019 - [c31]Haohan Wang, Zexue He, Zachary C. Lipton, Eric P. Xing:
Learning Robust Representations by Projecting Superficial Statistics Out. ICLR 2019 - [c30]Jonathon Byrd, Zachary Chase Lipton:
What is the Effect of Importance Weighting in Deep Learning? ICML 2019: 872-881 - [c29]Yifan Wu, Ezra Winston, Divyansh Kaushik, Zachary C. Lipton:
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment. ICML 2019: 6872-6881 - [c28]Mansi Gupta, Nitish Kulkarni, Raghuveer Chanda, Anirudha Rayasam, Zachary C. Lipton:
AmazonQA: A Review-Based Question Answering Task. IJCAI 2019: 4996-5002 - [c27]Tingfung Lau, Nathan Ng, Julian Gingold, Nina Desai, Julian J. McAuley, Zachary C. Lipton:
Embryo Staging with Weakly-Supervised Region Selection and Dynamically-Decoded Predictions. MLHC 2019: 663-679 - [c26]Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton:
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. NeurIPS 2019: 1394-1406 - [c25]Fan Yang, Liu Leqi, Yifan Wu, Zachary Chase Lipton, Pradeep Ravikumar, Tom M. Mitchell, William W. Cohen:
Game Design for Eliciting Distinguishable Behavior. NeurIPS 2019: 4686-4695 - [c24]Haohan Wang, Songwei Ge, Zachary C. Lipton, Eric P. Xing:
Learning Robust Global Representations by Penalizing Local Predictive Power. NeurIPS 2019: 10506-10518 - [i52]Yifan Wu, Ezra Winston, Divyansh Kaushik, Zachary C. Lipton:
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment. CoRR abs/1903.01689 (2019) - [i51]Haohan Wang, Zexue He, Zachary C. Lipton, Eric P. Xing:
Learning Robust Representations by Projecting Superficial Statistics Out. CoRR abs/1903.06256 (2019) - [i50]Tingfung Lau, Nathan Ng, Julian Gingold, Nina Desai, Julian J. McAuley, Zachary C. Lipton:
Embryo staging with weakly-supervised region selection and dynamically-decoded predictions. CoRR abs/1904.04419 (2019) - [i49]Mohammad Taha Bahadori, Zachary Chase Lipton:
Temporal-Clustering Invariance in Irregular Healthcare Time Series. CoRR abs/1904.12206 (2019) - [i48]Danish Pruthi, Bhuwan Dhingra, Zachary C. Lipton:
Combating Adversarial Misspellings with Robust Word Recognition. CoRR abs/1905.11268 (2019) - [i47]Lee Cohen, Zachary C. Lipton, Yishay Mansour:
Efficient candidate screening under multiple tests and implications for fairness. CoRR abs/1905.11361 (2019) - [i46]Haohan Wang, Songwei Ge, Eric P. Xing, Zachary C. Lipton:
Learning Robust Global Representations by Penalizing Local Predictive Power. CoRR abs/1905.13549 (2019) - [i45]Amy Zhang, Zachary C. Lipton, Luis Pineda, Kamyar Azizzadenesheli, Anima Anandkumar, Laurent Itti, Joelle Pineau, Tommaso Furlanello:
Learning Causal State Representations of Partially Observable Environments. CoRR abs/1906.10437 (2019) - [i44]Xinyang Feng, Zachary C. Lipton, Jie Yang, Scott A. Small, Frank A. Provenzano:
Estimating brain age based on a healthy population with deep learning and structural MRI. CoRR abs/1907.00943 (2019) - [i43]Mansi Gupta, Nitish Kulkarni, Raghuveer Chanda, Anirudha Rayasam, Zachary C. Lipton:
AmazonQA: A Review-Based Question Answering Task. CoRR abs/1908.04364 (2019) - [i42]Alankar Jain, Bhargavi Paranjape, Zachary C. Lipton:
Entity Projection via Machine Translation for Cross-Lingual NER. CoRR abs/1909.05356 (2019) - [i41]Danish Pruthi, Mansi Gupta, Bhuwan Dhingra, Graham Neubig, Zachary C. Lipton:
Learning to Deceive with Attention-Based Explanations. CoRR abs/1909.07913 (2019) - [i40]Divyansh Kaushik, Eduard H. Hovy, Zachary C. Lipton:
Learning the Difference that Makes a Difference with Counterfactually-Augmented Data. CoRR abs/1909.12434 (2019) - [i39]Angela H. Jiang, Daniel L.-K. Wong, Giulio Zhou, David G. Andersen, Jeffrey Dean, Gregory R. Ganger, Gauri Joshi, Michael Kaminsky, Michael Kozuch, Zachary C. Lipton, Padmanabhan Pillai:
Accelerating Deep Learning by Focusing on the Biggest Losers. CoRR abs/1910.00762 (2019) - [i38]Simran Kaur, Jeremy M. Cohen, Zachary C. Lipton:
Are Perceptually-Aligned Gradients a General Property of Robust Classifiers? CoRR abs/1910.08640 (2019) - [i37]Fan Yang, Liu Leqi, Yifan Wu, Zachary C. Lipton, Pradeep Ravikumar, William W. Cohen, Tom M. Mitchell:
Game Design for Eliciting Distinguishable Behavior. CoRR abs/1912.06074 (2019) - 2018
- [j2]Zachary C. Lipton:
The mythos of model interpretability. Commun. ACM 61(10): 36-43 (2018) - [j1]Zachary C. Lipton:
The Mythos of Model Interpretability. ACM Queue 16(3): 30 (2018) - [c23]Zachary C. Lipton, Xiujun Li, Jianfeng Gao, Lihong Li, Faisal Ahmed, Li Deng:
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems. AAAI 2018: 5237-5244 - [c22]Aditya Siddhant, Zachary C. Lipton:
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study. EMNLP 2018: 2904-2909 - [c21]Divyansh Kaushik, Zachary C. Lipton:
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks. EMNLP 2018: 5010-5015 - [c20]Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, Animashree Anandkumar:
Stochastic Activation Pruning for Robust Adversarial Defense. ICLR (Poster) 2018 - [c19]Chris Donahue, Zachary C. Lipton, Akshay Balsubramani, Julian J. McAuley:
Semantically Decomposing the Latent Spaces of Generative Adversarial Networks. ICLR (Poster) 2018 - [c18]Ashish Khetan, Zachary C. Lipton, Animashree Anandkumar:
Learning From Noisy Singly-labeled Data. ICLR (Poster) 2018 - [c17]Nathan H. Ng, Julian J. McAuley, Julian Gingold, Nina Desai, Zachary C. Lipton:
Predicting Embryo Morphokinetics in Videos with Late Fusion Nets & Dynamic Decoders. ICLR (Workshop) 2018 - [c16]Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, Animashree Anandkumar:
Deep Active Learning for Named Entity Recognition. ICLR (Poster) 2018 - [c15]Tommaso Furlanello, Zachary Chase Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar:
Born-Again Neural Networks. ICML 2018: 1602-1611 - [c14]Zachary C. Lipton, Yu-Xiang Wang, Alexander J. Smola:
Detecting and Correcting for Label Shift with Black Box Predictors. ICML 2018: 3128-3136 - [c13]Zachary C. Lipton, Julian J. McAuley, Alexandra Chouldechova:
Does mitigating ML's impact disparity require treatment disparity? NeurIPS 2018: 8136-8146 - [c12]Davis Liang, Zhiheng Huang, Zachary C. Lipton:
Learning Noise-Invariant Representations for Robust Speech Recognition. SLT 2018: 56-63 - [i36]Zachary C. Lipton, Yu-Xiang Wang, Alexander J. Smola:
Detecting and Correcting for Label Shift with Black Box Predictors. CoRR abs/1802.03916 (2018) - [i35]Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, Deva Ramanan:
Active Learning with Partial Feedback. CoRR abs/1802.07427 (2018) - [i34]Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, Anima Anandkumar:
Stochastic Activation Pruning for Robust Adversarial Defense. CoRR abs/1803.01442 (2018) - [i33]Subarna Tripathi, Zachary C. Lipton, Truong Q. Nguyen:
Correction by Projection: Denoising Images with Generative Adversarial Networks. CoRR abs/1803.04477 (2018) - [i32]Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar:
Born Again Neural Networks. CoRR abs/1805.04770 (2018) - [i31]Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Emma Brunskill, Zachary C. Lipton, Animashree Anandkumar:
Sample-Efficient Deep RL with Generative Adversarial Tree Search. CoRR abs/1806.05780 (2018) - [i30]Zachary C. Lipton, Jacob Steinhardt:
Troubling Trends in Machine Learning Scholarship. CoRR abs/1807.03341 (2018) - [i29]