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ORCIDZachary C. Lipton
Zachary Chase Lipton
Person information
- 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
- 2023
[c90]Michael Feffer, Hoda Heidari, Zachary C. Lipton:
Moral Machine or Tyranny of the Majority? AAAI 2023: 5974-5982- [c89]Kundan Krishna, Saurabh Garg, Jeffrey P. Bigham, Zachary C. Lipton:
Downstream Datasets Make Surprisingly Good Pretraining Corpora. ACL (1) 2023: 12207-12222 - [c88]Michael Feffer
, Michael Skirpan, Zachary C. Lipton, Hoda Heidari:
From Preference Elicitation to Participatory ML: A Critical Survey & Guidelines for Future Research. AIES 2023: 38-48 - [c87]Helen Zhou, Sivaraman Balakrishnan, Zachary C. Lipton:
Domain Adaptation under Missingness Shift. AISTATS 2023: 9577-9606 - [c86]Liu Leqi, Giulio Zhou, Fatma Kilinç-Karzan, Zachary C. Lipton, Alan L. Montgomery:
A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed Bandits. CHI 2023: 504:1-504:16 - [c85]Helen Zhou, Yuwen Chen, Zachary C. Lipton:
Evaluating Model Performance in Medical Datasets Over Time. CHIL 2023: 498-508 - [c84]Shantanu Gupta, David Childers, Zachary Chase Lipton:
Local Causal Discovery for Estimating Causal Effects. CLeaR 2023: 408-447 - [c83]Zachary Novack, Simran Kaur, Tanya Marwah, Saurabh Garg, Zachary Chase Lipton:
Disentangling the Mechanisms Behind Implicit Regularization in SGD. ICLR 2023 - [c82]Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton:
RLSbench: Domain Adaptation Under Relaxed Label Shift. ICML 2023: 10879-10928 - [c81]Pratyush Maini, Michael Curtis Mozer, Hanie Sedghi, Zachary Chase Lipton, J. Zico Kolter, Chiyuan Zhang:
Can Neural Network Memorization Be Localized? ICML 2023: 23536-23557 - [c80]Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Andrej Risteski:
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective. ICML 2023: 24139-24172 - [c79]Zachary Novack, Julian J. McAuley, Zachary Chase Lipton, Saurabh Garg:
CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets. ICML 2023: 26342-26362 - [c78]Shubhanshu Shekhar, Ziyu Xu, Zachary C. Lipton, Pierre J. Liang, Aaditya Ramdas:
Risk-limiting financial audits via weighted sampling without replacement. UAI 2023: 1932-1941 - [i124]Jacob Tyo, Zachary C. Lipton:
Meta-Learning Mini-Batch Risk Functionals. CoRR abs/2301.11724 (2023) - [i123]Zachary Novack, Saurabh Garg, Julian J. McAuley, Zachary C. Lipton:
CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets. CoRR abs/2302.02551 (2023) - [i122]Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary C. Lipton:
RLSbench: Domain Adaptation Under Relaxed Label Shift. CoRR abs/2302.03020 (2023) - [i121]Tom Yan, Shantanu Gupta, Zachary C. Lipton:
Discovering Optimal Scoring Mechanisms in Causal Strategic Prediction. CoRR abs/2302.06804 (2023) - [i120]Shantanu Gupta, David Childers, Zachary C. Lipton:
Local Causal Discovery for Estimating Causal Effects. CoRR abs/2302.08070 (2023) - [i119]Alex Mei, Michael Saxon, Shiyu Chang, Zachary C. Lipton, William Yang Wang:
Users are the North Star for AI Transparency. CoRR abs/2303.05500 (2023) - [i118]Mrigank Raman, Pratyush Maini, J. Zico Kolter, Zachary C. Lipton, Danish Pruthi:
Model-tuning Via Prompts Makes NLP Models Adversarially Robust. CoRR abs/2303.07320 (2023) - [i117]Liu Leqi, Giulio Zhou, Fatma Kilinç-Karzan, Zachary C. Lipton, Alan L. Montgomery:
A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed Bandits. CoRR abs/2304.09088 (2023) - [i116]Shubhanshu Shekhar, Ziyu Xu, Zachary C. Lipton, Pierre J. Liang, Aaditya Ramdas:
Risk-limiting Financial Audits via Weighted Sampling without Replacement. CoRR abs/2305.06884 (2023) - [i115]Helen Zhou, Yuwen Chen, Zachary C. Lipton:
Evaluating Model Performance in Medical Datasets Over Time. CoRR abs/2305.13426 (2023) - [i114]Kundan Krishna, Prakhar Gupta, Sanjana Ramprasad, Byron C. Wallace, Jeffrey P. Bigham, Zachary C. Lipton:
USB: A Unified Summarization Benchmark Across Tasks and Domains. CoRR abs/2305.14296 (2023) - [i113]Dhananjay Ashok, Zachary C. Lipton:
PromptNER: Prompting For Named Entity Recognition. CoRR abs/2305.15444 (2023) - [i112]Michael Feffer, Hoda Heidari, Zachary C. Lipton:
Moral Machine or Tyranny of the Majority? CoRR abs/2305.17319 (2023) - [i111]Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan, Zachary Chase Lipton, Yu-Xiang Wang:
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms. CoRR abs/2305.19570 (2023) - [i110]Pratyush Maini, Sachin Goyal, Zachary C. Lipton, J. Zico Kolter, Aditi Raghunathan:
T-MARS: Improving Visual Representations by Circumventing Text Feature Learning. CoRR abs/2307.03132 (2023) - [i109]Pratyush Maini, Michael C. Mozer, Hanie Sedghi, Zachary C. Lipton, J. Zico Kolter, Chiyuan Zhang:
Can Neural Network Memorization Be Localized? CoRR abs/2307.09542 (2023) - [i108]Jennifer Hsia, Danish Pruthi, Aarti Singh, Zachary C. Lipton:
Goodhart's Law Applies to NLP's Explanation Benchmarks. CoRR abs/2308.14272 (2023) - [i107]Jacob Tyo, Motolani Olarinre, Youngseog Chung, Zachary C. Lipton:
MUDD: A New Re-Identification Dataset with Efficient Annotation for Off-Road Racers in Extreme Conditions. CoRR abs/2311.08488 (2023) - [i106]Jacob Tyo, Youngseog Chung, Motolani Olarinre, Zachary C. Lipton:
Reading Between the Mud: A Challenging Motorcycle Racer Number Dataset. CoRR abs/2311.09256 (2023) - [i105]Yuwen Chen, Helen Zhou, Zachary C. Lipton:
MoCo-Transfer: Investigating out-of-distribution contrastive learning for limited-data domains. CoRR abs/2311.09401 (2023) - [i104]Tanya Marwah, Ashwini Pokle, J. Zico Kolter, Zachary C. Lipton, Jianfeng Lu, Andrej Risteski:
Deep Equilibrium Based Neural Operators for Steady-State PDEs. CoRR abs/2312.00234 (2023) - 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) - [c77]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. AAAI 2022: 5277-5285 - [c76]Omer Ben-Porat, Lee Cohen, Liu Leqi, Zachary C. Lipton, Yishay Mansour:
Modeling Attrition in Recommender Systems with Departing Bandits. AAAI 2022: 6072-6079 - [c75]Audrey Huang, Liu Leqi, Zachary C. Lipton, Kamyar Azizzadenesheli:
Off-Policy Risk Assessment for Markov Decision Processes. AISTATS 2022: 5022-5050 - [c74]Helen Zhou, Cheng Cheng, Kelly J. Shields, Gursimran Kochhar, Tariq Cheema, Zachary C. Lipton, Jeremy C. Weiss:
Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19. AMIA 2022 - [c73]Anurag Katakkar, Clay H. Yoo, Weiqin Wang, Zachary C. Lipton, Divyansh Kaushik:
Practical Benefits of Feature Feedback Under Distribution Shift. BlackboxNLP@EMNLP 2022: 346-355 - [c72]Simran Kaur, Jeremy Cohen, Zachary Chase Lipton:
On the Maximum Hessian Eigenvalue and Generalization. ICBINB 2022: 51-65 - [c71]Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, Hanie Sedghi:
Leveraging unlabeled data to predict out-of-distribution performance. ICLR 2022 - [c70]Liu Leqi, Audrey Huang, Zachary C. Lipton, Kamyar Azizzadenesheli:
Supervised Learning with General Risk Functionals. ICML 2022: 12570-12592 - [c69]Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton:
Domain Adaptation under Open Set Label Shift. NeurIPS 2022 - [c68]Pratyush Maini, Saurabh Garg, Zachary C. Lipton, J. Zico Kolter:
Characterizing Datapoints via Second-Split Forgetting. NeurIPS 2022 - [c67]Manley Roberts, Pranav Mani, Saurabh Garg, Zachary C. Lipton:
Unsupervised Learning under Latent Label Shift. NeurIPS 2022 - [e1]Zachary C. Lipton, Rajesh Ranganath, Mark P. Sendak, Michael W. Sjoding, Serena Yeung:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2022, 5-6 August 2022, Durham, NC, USA. Proceedings of Machine Learning Research 182, PMLR 2022 [contents] - [i103]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) - [i102]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) - [i101]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) - [i100]Riccardo Fogliato, Sina Fazelpour, Shantanu Gupta, Zachary C. Lipton, David Danks:
Homophily and Incentive Effects in Use of Algorithms. CoRR abs/2205.09701 (2022) - [i99]Divyansh Kaushik, Zachary C. Lipton, Alex John London:
Resolving the Human Subjects Status of Machine Learning's Crowdworkers. CoRR abs/2206.04039 (2022) - [i98]Simran Kaur, Jeremy Cohen, Zachary C. Lipton:
On the Maximum Hessian Eigenvalue and Generalization. CoRR abs/2206.10654 (2022) - [i97]Liu Leqi, Audrey Huang, Zachary C. Lipton, Kamyar Azizzadenesheli:
Supervised Learning with General Risk Functionals. CoRR abs/2206.13648 (2022) - [i96]Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton:
Domain Adaptation under Open Set Label Shift. CoRR abs/2207.13048 (2022) - [i95]Manley Roberts, Pranav Mani, Saurabh Garg, Zachary C. Lipton:
Unsupervised Learning under Latent Label Shift. CoRR abs/2207.13179 (2022) - [i94]Helen Zhou, Cheng Cheng, Kelly J. Shields, Gursimran Kochhar, Tariq Cheema, Zachary C. Lipton, Jeremy C. Weiss:
Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19. CoRR abs/2208.13126 (2022) - [i93]Jacob Tyo, Bhuwan Dhingra, Zachary C. Lipton:
On the State of the Art in Authorship Attribution and Authorship Verification. CoRR abs/2209.06869 (2022) - [i92]Audrey Huang, Liu Leqi, Zachary Chase Lipton, Kamyar Azizzadenesheli:
Off-Policy Risk Assessment in Markov Decision Processes. CoRR abs/2209.10444 (2022) - [i91]Kundan Krishna, Saurabh Garg, Jeffrey P. Bigham, Zachary C. Lipton:
Downstream Datasets Make Surprisingly Good Pretraining Corpora. CoRR abs/2209.14389 (2022) - [i90]Rasool Fakoor, Jonas Mueller, Zachary C. Lipton, Pratik Chaudhari, Alexander J. Smola:
Data drift correction via time-varying importance weight estimator. CoRR abs/2210.01422 (2022) - [i89]Tanya Marwah, Zachary C. Lipton, Jianfeng Lu, Andrej Risteski:
Neural Network Approximations of PDEs Beyond Linearity: Representational Perspective. CoRR abs/2210.12101 (2022) - [i88]Pratyush Maini, Saurabh Garg, Zachary C. Lipton, J. Zico Kolter:
Characterizing Datapoints via Second-Split Forgetting. CoRR abs/2210.15031 (2022) - [i87]Helen Zhou, Sivaraman Balakrishnan, Zachary C. Lipton:
Domain Adaptation under Missingness Shift. CoRR abs/2211.02093 (2022) - [i86]Helen Zhou, Yuwen Chen, Zachary C. Lipton:
Model Evaluation in Medical Datasets Over Time. CoRR abs/2211.07165 (2022) - [i85]Zachary Novack, Simran Kaur, Tanya Marwah, Saurabh Garg, Zachary C. Lipton:
Disentangling the Mechanisms Behind Implicit Regularization in SGD. CoRR abs/2211.15853 (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): 428:1-428:24 (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) - [c66]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 - [c65]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 - [c64]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 - [c63]Jessica Dai, Sina Fazelpour, Zachary C. Lipton:
Fair Machine Learning Under Partial Compliance. AIES 2021: 55-65 - [c62]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 - [c61]Kyra Gan, Andrew A. Li, Zachary Chase Lipton, Sridhar R. Tayur:
Causal Inference with Selectively Deconfounded Data. AISTATS 2021: 2791-2799 - [c60]Cheng Cheng, Helen Zhou, Jeremy C. Weiss, Zachary C. Lipton:
Unpacking the Drop in COVID-19 Case Fatality Rates: A Study of National and Florida Line-Level Data. AMIA 2021 - [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]