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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
- 2025
[j15]Shalmali Joshi, Iñigo Urteaga, Wouter A. C. van Amsterdam, George Hripcsak, Pierre A. Elias
, Benjamin R. C. Amor, Noémie Elhadad, James C. Fackler, Mark P. Sendak, Jenna Wiens, Kaivalya Deshpande, Yoav Wald, Madalina Fiterau, Zachary C. Lipton, Daniel Malinsky, Madhur Nayan, Hongseok Namkoong, Soojin Park, Julia E. Vogt, Rajesh Ranganath:
AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation. J. Am. Medical Informatics Assoc. 32(3): 589-594 (2025)- [j14]Yewon Byun, Sanket Vaibhav Mehta, Saurabh Garg, Emma Strubell, Michael Oberst, Bryan Wilder, Zachary Chase Lipton:
Expert Routing with Synthetic Data for Domain Incremental Learning. Trans. Mach. Learn. Res. 2025 (2025) - [j13]Daniel P. Jeong, Zachary Chase Lipton, Pradeep Kumar Ravikumar:
LLM-Select: Feature Selection with Large Language Models. Trans. Mach. Learn. Res. 2025 (2025) - [c121]Dhruv Rohatgi, Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Ankur Moitra, Andrej Risteski:
Towards characterizing the value of edge embeddings in Graph Neural Networks. ICML 2025 - [c120]Yewon Byun, Shantanu Gupta, Zachary C. Lipton, Rachel Leah Childers, Bryan Wilder:
Valid Inference with Imperfect Synthetic Data. NeurIPS 2025 - [c119]Hua Shen, Tiffany Knearem, Reshmi Ghosh, Kenan Alkiek, Kundan Krishna, Yachuan Liu, Savvas Petridis, Yi-Hao Peng, Li Qiwei, Chenglei Si, Yutong Xie, Jeffrey P. Bigham, Frank Bentley, Joyce Chai, Zachary C. Lipton, Qiaozhu Mei, Michael Terry, Diyi Yang, Meredith Ringel Morris, Paul Resnick, David Jurgens:
Position: Towards Bidirectional Human-AI Alignment. NeurIPS 2025 - [i154]Gati Aher, Robin Schmucker, Tom M. Mitchell, Zachary C. Lipton:
AI Mentors for Student Projects: Spotting Early Issues in Computer Science Proposals. CoRR abs/2503.05782 (2025) - [i153]Pranav Mani, Peng Xu, Zachary C. Lipton, Michael Oberst:
No Free Lunch: Non-Asymptotic Analysis of Prediction-Powered Inference. CoRR abs/2505.20178 (2025) - [i152]Vineeth Dorna, Anmol Mekala, Wenlong Zhao, Andrew McCallum, Zachary C. Lipton, J. Zico Kolter, Pratyush Maini:
OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics. CoRR abs/2506.12618 (2025) - [i151]Yewon Byun, Shantanu Gupta, Zachary C. Lipton, Rachel Leah Childers, Bryan Wilder:
Using Imperfect Synthetic Data in Downstream Inference Tasks. CoRR abs/2508.06635 (2025) - 2024
- [j12]Divyansh Kaushik, Zachary C. Lipton, Alex John London:
Resolving the Human-Subjects Status of ML's Crowdworkers. Commun. ACM 67(5): 52-59 (2024) - [j11]Divyansh Kaushik, Zachary C. Lipton, Alex John London:
Resolving the Human-subjects Status of Machine Learning's Crowdworkers: What ethical framework should govern the interaction of ML researchers and crowdworkers? ACM Queue 21(6): 101-127 (2024) - [c118]Sanjana Ramprasad, Elisa Ferracane, Zachary C. Lipton:
Analyzing LLM Behavior in Dialogue Summarization: Unveiling Circumstantial Hallucination Trends. ACL (1) 2024: 12549-12561 - [c117]Michael Feffer, Anusha Sinha, Wesley H. Deng, Zachary C. Lipton, Hoda Heidari:
Red-Teaming for Generative AI: Silver Bullet or Security Theater? AIES (1) 2024: 421-437 - [c116]Helen Zhou, Audrey Huang, Kamyar Azizzadenesheli, David Childers, Zachary C. Lipton:
Timing as an Action: Learning When to Observe and Act. AISTATS 2024: 3979-3987 - [c115]Yewon Byun, Dylan Sam, Michael Oberst, Zachary C. Lipton, Bryan Wilder:
Auditing Fairness under Unobserved Confounding. AISTATS 2024: 4339-4347 - [c114]Nave Frost, Zachary C. Lipton, Yishay Mansour, Michal Moshkovitz:
Partially Interpretable Models with Guarantees on Coverage and Accuracy. ALT 2024: 590-613 - [c113]Evan Fellman, Jacob Tyo, Zachary C. Lipton:
The Future of Web Data Mining: Insights from Multimodal and Code-based Extraction Methods. CASE 2024: 1-5 - [c112]Sachin Goyal, Pratyush Maini, Zachary C. Lipton, Aditi Raghunathan, J. Zico Kolter:
Scaling Laws for Data Filtering - Data Curation Cannot be Compute Agnostic. CVPR 2024: 22702-22711 - [c111]Sanjana Ramprasad, Kundan Krishna, Zachary C. Lipton, Byron C. Wallace:
Evaluating the Factuality of Zero-shot Summarizers Across Varied Domains. EACL (2) 2024: 50-59 - [c110]Jennifer Hsia, Danish Pruthi, Aarti Singh, Zachary C. Lipton:
Goodhart's Law Applies to NLP's Explanation Benchmarks. EACL (Findings) 2024: 1322-1335 - [c109]Daniel P. Jeong, Saurabh Garg, Zachary C. Lipton, Michael Oberst:
Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? EMNLP 2024: 12143-12170 - [c108]Nil-Jana Akpinar, Zachary C. Lipton, Alexandra Chouldechova:
The Impact of Differential Feature Under-reporting on Algorithmic Fairness. FAccT 2024: 1355-1382 - [c107]Rasool Fakoor, Jonas Mueller, Zachary Chase Lipton, Pratik Chaudhari, Alex Smola:
Time-Varying Propensity Score to Bridge the Gap between the Past and Present. ICLR 2024 - [c106]Pratyush Maini, Sachin Goyal, Zachary Chase Lipton, J. Zico Kolter, Aditi Raghunathan:
T-MARS: Improving Visual Representations by Circumventing Text Feature Learning. ICLR 2024 - [c105]Tom Yan, Ziyu Xu, Zachary Chase Lipton:
Foundations of Testing for Finite-Sample Causal Discovery. ICML 2024: 55859-55900 - [c104]Sumukh K. Aithal, Pratyush Maini, Zachary C. Lipton, J. Zico Kolter:
Understanding Hallucinations in Diffusion Models through Mode Interpolation. NeurIPS 2024 - [c103]Jake Fawkes, Nic Fishman, Mel Andrews, Zachary C. Lipton:
The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning. NeurIPS 2024 - [c102]Rishabh Ranjan, Saurabh Garg, Mrigank Raman, Carlos Guestrin, Zachary C. Lipton:
Post-Hoc Reversal: Are We Selecting Models Prematurely? NeurIPS 2024 - [c101]Avi Schwarzschild, Zhili Feng, Pratyush Maini, Zachary C. Lipton, J. Zico Kolter:
Rethinking LLM Memorization through the Lens of Adversarial Compression. NeurIPS 2024 - [c100]Tom Yan, Zachary C. Lipton:
A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning. NeurIPS 2024 - [e3]Kaivalya Deshpande, Madalina Fiterau, Shalmali Joshi, Zachary C. Lipton, Rajesh Ranganath, Iñigo Urteaga:
Machine Learning for Healthcare Conference, 16-17 August 2024, Toronto, Canada. Proceedings of Machine Learning Research 252, PMLR 2024 [contents] - [i150]Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C. Lipton, J. Zico Kolter:
TOFU: A Task of Fictitious Unlearning for LLMs. CoRR abs/2401.06121 (2024) - [i149]Nil-Jana Akpinar, Zachary C. Lipton, Alexandra Chouldechova:
The Impact of Differential Feature Under-reporting on Algorithmic Fairness. CoRR abs/2401.08788 (2024) - [i148]Michael Feffer, Anusha Sinha, Zachary C. Lipton, Hoda Heidari:
Red-Teaming for Generative AI: Silver Bullet or Security Theater? CoRR abs/2401.15897 (2024) - [i147]Sanjana Ramprasad, Kundan Krishna, Zachary C. Lipton, Byron C. Wallace:
Evaluating the Factuality of Zero-shot Summarizers Across Varied Domains. CoRR abs/2402.03509 (2024) - [i146]Xinyu Li, Zachary C. Lipton, Liu Leqi:
Personalized Language Modeling from Personalized Human Feedback. CoRR abs/2402.05133 (2024) - [i145]Jacob Tyo, Zachary C. Lipton:
Contrastive Multiple Instance Learning for Weakly Supervised Person ReID. CoRR abs/2402.07685 (2024) - [i144]Jacob Tyo, Motolani Olarinre, Youngseog Chung, Zachary C. Lipton:
Beyond the Mud: Datasets and Benchmarks for Computer Vision in Off-Road Racing. CoRR abs/2402.08025 (2024) - [i143]Kundan Krishna, Sanjana Ramprasad, Prakhar Gupta, Byron C. Wallace, Zachary C. Lipton, Jeffrey P. Bigham:
GenAudit: Fixing Factual Errors in Language Model Outputs with Evidence. CoRR abs/2402.12566 (2024) - [i142]Yewon Byun, Dylan Sam, Michael Oberst, Zachary C. Lipton, Bryan Wilder:
Auditing Fairness under Unobserved Confounding. CoRR abs/2403.14713 (2024) - [i141]Sachin Goyal, Pratyush Maini, Zachary C. Lipton, Aditi Raghunathan, J. Zico Kolter:
Scaling Laws for Data Filtering - Data Curation cannot be Compute Agnostic. CoRR abs/2404.07177 (2024) - [i140]Rishabh Ranjan, Saurabh Garg, Mrigank Raman, Carlos Guestrin, Zachary Chase Lipton:
Post-Hoc Reversal: Are We Selecting Models Prematurely? CoRR abs/2404.07815 (2024) - [i139]Avi Schwarzschild, Zhili Feng, Pratyush Maini, Zachary C. Lipton, J. Zico Kolter:
Rethinking LLM Memorization through the Lens of Adversarial Compression. CoRR abs/2404.15146 (2024) - [i138]Sanjana Ramprasad, Elisa Ferracane, Zachary C. Lipton:
Analyzing LLM Behavior in Dialogue Summarization: Unveiling Circumstantial Hallucination Trends. CoRR abs/2406.03487 (2024) - [i137]Hua Shen, Tiffany Knearem, Reshmi Ghosh, Kenan Alkiek, Kundan Krishna, Yachuan Liu, Ziqiao Ma, Savvas Petridis, Yi-Hao Peng, Li Qiwei, Sushrita Rakshit, Chenglei Si, Yutong Xie, Jeffrey P. Bigham, Frank Bentley, Joyce Chai, Zachary C. Lipton, Qiaozhu Mei, Rada Mihalcea, Michael Terry, Diyi Yang, Meredith Ringel Morris, Paul Resnick, David Jurgens:
Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions. CoRR abs/2406.09264 (2024) - [i136]Sumukh K. Aithal, Pratyush Maini, Zachary C. Lipton, J. Zico Kolter:
Understanding Hallucinations in Diffusion Models through Mode Interpolation. CoRR abs/2406.09358 (2024) - [i135]Daniel P. Jeong, Zachary C. Lipton, Pradeep Ravikumar:
LLM-Select: Feature Selection with Large Language Models. CoRR abs/2407.02694 (2024) - [i134]Vibhhu Sharma, Shantanu Gupta, Nil-Jana Akpinar, Zachary C. Lipton, Liu Leqi:
A Unified Causal Framework for Auditing Recommender Systems for Ethical Concerns. CoRR abs/2409.13210 (2024) - [i133]Jake Fawkes, Nic Fishman, Mel Andrews, Zachary C. Lipton:
The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning. CoRR abs/2410.09600 (2024) - [i132]Dhruv Rohatgi, Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Ankur Moitra, Andrej Risteski:
Towards characterizing the value of edge embeddings in Graph Neural Networks. CoRR abs/2410.09867 (2024) - [i131]Khurram Yamin, Shantanu Gupta, Gaurav R. Ghosal, Zachary C. Lipton, Bryan Wilder:
Failure Modes of LLMs for Causal Reasoning on Narratives. CoRR abs/2410.23884 (2024) - [i130]Shantanu Gupta, Zachary C. Lipton, David Childers:
Online Data Collection for Efficient Semiparametric Inference. CoRR abs/2411.03195 (2024) - [i129]Daniel P. Jeong, Saurabh Garg, Zachary C. Lipton, Michael Oberst:
Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? CoRR abs/2411.04118 (2024) - [i128]Daniel P. Jeong, Pranav Mani, Saurabh Garg, Zachary C. Lipton, Michael Oberst:
The Limited Impact of Medical Adaptation of Large Language and Vision-Language Models. CoRR abs/2411.08870 (2024) - [i127]Yewon Byun, Sanket Vaibhav Mehta, Saurabh Garg, Emma Strubell, Michael Oberst, Bryan Wilder, Zachary C. Lipton:
Generate to Discriminate: Expert Routing for Continual Learning. CoRR abs/2412.17009 (2024) - 2023
- [j10]Yifan Zhang, Hanlin Zhang, Zachary Chase Lipton, Li Erran Li, Eric P. Xing:
Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation. Trans. Mach. Learn. Res. 2023 (2023) - [c99]Michael Feffer, Hoda Heidari, Zachary C. Lipton:
Moral Machine or Tyranny of the Majority? AAAI 2023: 5974-5982 - [c98]Kundan Krishna, Saurabh Garg, Jeffrey P. Bigham, Zachary C. Lipton:
Downstream Datasets Make Surprisingly Good Pretraining Corpora. ACL (1) 2023: 12207-12222 - [c97]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 - [c96]Helen Zhou, Sivaraman Balakrishnan, Zachary C. Lipton:
Domain Adaptation under Missingness Shift. AISTATS 2023: 9577-9606 - [c95]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 - [c94]Helen Zhou, Yuwen Chen, Zachary C. Lipton:
Evaluating Model Performance in Medical Datasets Over Time. CHIL 2023: 498-508 - [c93]Shantanu Gupta, David Childers, Zachary Chase Lipton:
Local Causal Discovery for Estimating Causal Effects. CLeaR 2023: 408-447 - [c92]Kundan Krishna, Prakhar Gupta, Sanjana Ramprasad, Byron C. Wallace, Jeffrey P. Bigham, Zachary C. Lipton:
USB: A Unified Summarization Benchmark Across Tasks and Domains. EMNLP (Findings) 2023: 8826-8845 - [c91]Mrigank Raman, Pratyush Maini, J. Zico Kolter, Zachary C. Lipton, Danish Pruthi:
Model-tuning Via Prompts Makes NLP Models Adversarially Robust. EMNLP 2023: 9266-9286 - [c90]Michael Feffer, Zachary C. Lipton, Chris Donahue:
DeepDrake ft. BTS-GAN and TayloRVC: An Exploratory Analysis of Musical Deepfakes and Hosting Platforms. HCMIR@ISMIR 2023 - [c89]Zachary Novack, Simran Kaur, Tanya Marwah, Saurabh Garg, Zachary Chase Lipton:
Disentangling the Mechanisms Behind Implicit Regularization in SGD. ICLR 2023 - [c88]Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton:
RLSbench: Domain Adaptation Under Relaxed Label Shift. ICML 2023: 10879-10928 - [c87]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 - [c86]Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Andrej Risteski:
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective. ICML 2023: 24139-24172 - [c85]Zachary Novack, Julian J. McAuley, Zachary Chase Lipton, Saurabh Garg:
CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets. ICML 2023: 26342-26362 - [c84]Jacob Tyo, Bhuwan Dhingra, Zachary C. Lipton:
Valla: Standardizing and Benchmarking Authorship Attribution and Verification Through Empirical Evaluation and Comparative Analysis. IJCNLP (1) 2023: 649-660 - [c83]Zachary Chase Lipton:
Reliable deep learning in dynamic environments. Computer-Aided Diagnosis 2023 - [c82]Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan, Zachary C. Lipton, Yu-Xiang Wang:
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms. NeurIPS 2023 - [c81]Saurabh Garg, Amrith Setlur, Zachary C. Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan:
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. NeurIPS 2023 - [c80]Tanya Marwah, Ashwini Pokle, J. Zico Kolter, Zachary C. Lipton, Jianfeng Lu, Andrej Risteski:
Deep Equilibrium Based Neural Operators for Steady-State PDEs. NeurIPS 2023 - [c79]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 - [e2]Kaivalya Deshpande, Madalina Fiterau, Shalmali Joshi, Zachary C. Lipton, Rajesh Ranganath, Iñigo Urteaga, Serene Yeung:
Machine Learning for Healthcare Conference, MLHC 2023, 11-12 August 2023, New York, USA. Proceedings of Machine Learning Research 219, PMLR 2023 [contents] - [i126]Jacob Tyo, Zachary C. Lipton:
Meta-Learning Mini-Batch Risk Functionals. CoRR abs/2301.11724 (2023) - [i125]Zachary Novack, Saurabh Garg, Julian J. McAuley, Zachary C. Lipton:
CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets. CoRR abs/2302.02551 (2023) - [i124]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) - [i123]Tom Yan, Shantanu Gupta, Zachary C. Lipton:
Discovering Optimal Scoring Mechanisms in Causal Strategic Prediction. CoRR abs/2302.06804 (2023) - [i122]Shantanu Gupta, David Childers, Zachary C. Lipton:
Local Causal Discovery for Estimating Causal Effects. CoRR abs/2302.08070 (2023) - [i121]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) - [i120]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) - [i119]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) - [i118]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) - [i117]Helen Zhou, Yuwen Chen, Zachary C. Lipton:
Evaluating Model Performance in Medical Datasets Over Time. CoRR abs/2305.13426 (2023) - [i116]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) - [i115]Dhananjay Ashok, Zachary C. Lipton:
PromptNER: Prompting For Named Entity Recognition. CoRR abs/2305.15444 (2023) - [i114]Michael Feffer, Hoda Heidari, Zachary C. Lipton:
Moral Machine or Tyranny of the Majority? CoRR abs/2305.17319 (2023) - [i113]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) - [i112]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) - [i111]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) - [i110]Jennifer Hsia, Danish Pruthi, Aarti Singh, Zachary C. Lipton:
Goodhart's Law Applies to NLP's Explanation Benchmarks. CoRR abs/2308.14272 (2023) - [i109]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) - [i108]Jacob Tyo, Youngseog Chung, Motolani Olarinre, Zachary C. Lipton:
Reading Between the Mud: A Challenging Motorcycle Racer Number Dataset. CoRR abs/2311.09256 (2023) - [i107]Yuwen Chen, Helen Zhou, Zachary C. Lipton:
MoCo-Transfer: Investigating out-of-distribution contrastive learning for limited-data domains. CoRR abs/2311.09401 (2023) - [i106]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) - [i105]Saurabh Garg, Amrith Setlur, Zachary Chase Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan:
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. CoRR abs/2312.03318 (2023) - [i104]Micah Goldblum, Anima Anandkumar, Richard G. Baraniuk, Tom Goldstein, Kyunghyun Cho, Zachary C. Lipton, Melanie Mitchell, Preetum Nakkiran, Max Welling, Andrew Gordon Wilson:
Perspectives on the State and Future of Deep Learning - 2023. CoRR abs/2312.09323 (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) - [c78]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 - [c77]Omer Ben-Porat, Lee Cohen, Liu Leqi, Zachary C. Lipton, Yishay Mansour:
Modeling Attrition in Recommender Systems with Departing Bandits. AAAI 2022: 6072-6079 - [c76]Audrey Huang, Liu Leqi, Zachary C. Lipton, Kamyar Azizzadenesheli:
Off-Policy Risk Assessment for Markov Decision Processes. AISTATS 2022: 5022-5050 - [c75]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 - [c74]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 - [c73]Riccardo Fogliato, Sina Fazelpour, Shantanu Gupta, Zachary C. Lipton, David Danks:
Homophily and Incentive Effects in Use of Algorithms. CogSci 2022 - [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]Yifan 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]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 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]David Lowell, Zachary C. Lipton, Byron C. Wallace:
How transferable are the datasets collected by active learners? CoRR abs/1807.04801 (2018) - [i28]Davis Liang, Zhiheng Huang, Zachary C. Lipton:
Learning Noise-Invariant Representations for Robust Speech Recognition. CoRR abs/1807.06610 (2018) - [i27]Divyansh Kaushik, Zachary C. Lipton:
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks. CoRR abs/1808.04926 (2018) - [i26]Aditya Siddhant, Zachary C. Lipton:
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study. CoRR abs/1808.05697 (2018) - [i25]Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton:
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. CoRR abs/1810.11953 (2018) - [i24]Jonathon Byrd, Zachary C. Lipton:
Weighted Risk Minimization & Deep Learning. CoRR abs/1812.03372 (2018) - 2017
- [b1]Zachary Chase Lipton:
Learning from Temporally-Structured Human Activities Data. University of California, San Diego, USA, 2017 - [c11]Jean Kossaifi, Aran Khanna, Zachary C. Lipton, Tommaso Furlanello, Anima Anandkumar:
Tensor Contraction Layers for Parsimonious Deep Nets. CVPR Workshops 2017: 1940-1946 - [c10]Chris Donahue, Zachary C. Lipton, Julian J. McAuley:
Dance Dance Convolution. ICLR (Workshop) 2017 - [c9]Zachary C. Lipton, Subarna Tripathi:
Precise Recovery of Latent Vectors from Generative Adversarial Networks. ICLR (Workshop) 2017 - [c8]Chris Donahue, Zachary C. Lipton, Julian J. McAuley:
Dance Dance Convolution. ICML 2017: 1039-1048 - [c7]Jianmo Ni, Zachary C. Lipton, Sharad Vikram, Julian J. McAuley:
Estimating Reactions and Recommending Products with Generative Models of Reviews. IJCNLP(1) 2017: 783-791 - [c6]Nathan H. Ng, Rodney A. Gabriel, Julian J. McAuley, Charles Elkan, Zachary C. Lipton:
Predicting Surgery Duration with Neural Heteroscedastic Regression. MLHC 2017: 100-111 - [c5]Yanyao Shen, Hyokun Yun, Zachary Chase Lipton, Yakov Kronrod, Animashree Anandkumar:
Deep Active Learning for Named Entity Recognition. Rep4NLP@ACL 2017: 252-256 - [i23]Zachary C. Lipton, Subarna Tripathi:
Precise Recovery of Latent Vectors from Generative Adversarial Networks. CoRR abs/1702.04782 (2017) - [i22]Nathan Ng, Rodney A. Gabriel, Julian J. McAuley, Charles Elkan, Zachary C. Lipton:
Predicting Surgery Duration with Neural Heteroscedastic Regression. CoRR abs/1702.05386 (2017) - [i21]Chris Donahue, Zachary C. Lipton, Julian J. McAuley:
Dance Dance Convolution. CoRR abs/1703.06891 (2017) - [i20]Chris Donahue, Akshay Balsubramani, Julian J. McAuley, Zachary C. Lipton:
Semantically Decomposing the Latent Spaces of Generative Adversarial Networks. CoRR abs/1705.07904 (2017) - [i19]Jean Kossaifi, Aran Khanna, Zachary C. Lipton, Tommaso Furlanello, Anima Anandkumar:
Tensor Contraction Layers for Parsimonious Deep Nets. CoRR abs/1706.00439 (2017) - [i18]Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, Animashree Anandkumar:
Deep Active Learning for Named Entity Recognition. CoRR abs/1707.05928 (2017) - [i17]Jean Kossaifi, Zachary C. Lipton, Aran Khanna, Tommaso Furlanello, Anima Anandkumar:
Tensor Regression Networks. CoRR abs/1707.08308 (2017) - [i16]John Alberg, Zachary C. Lipton:
Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals. CoRR abs/1711.04837 (2017) - [i15]Zachary C. Lipton, Alexandra Chouldechova, Julian J. McAuley:
Does mitigating ML's disparate impact require disparate treatment? CoRR abs/1711.07076 (2017) - [i14]Ashish Khetan, Zachary C. Lipton, Anima Anandkumar:
Learning From Noisy Singly-labeled Data. CoRR abs/1712.04577 (2017) - 2016
- [c4]Subarna Tripathi, Zachary C. Lipton, Serge J. Belongie, Truong Q. Nguyen:
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks. BMVC 2016 - [c3]Zachary C. Lipton, David C. Kale, Randall C. Wetzel:
Directly Modeling Missing Data in Sequences with RNNs: Improved Classification of Clinical Time Series. MLHC 2016: 253-270 - [c2]Zachary Chase Lipton, David C. Kale, Charles Elkan, Randall C. Wetzel:
Learning to Diagnose with LSTM Recurrent Neural Networks. ICLR (Poster) 2016 - [i13]Zachary Chase Lipton:
Stuck in a What? Adventures in Weight Space. CoRR abs/1602.07320 (2016) - [i12]Zachary Chase Lipton:
The Mythos of Model Interpretability. CoRR abs/1606.03490 (2016) - [i11]Zachary Chase Lipton, David C. Kale, Randall C. Wetzel:
Directly Modeling Missing Data in Sequences with RNNs: Improved Classification of Clinical Time Series. CoRR abs/1606.04130 (2016) - [i10]Subarna Tripathi, Zachary C. Lipton, Serge J. Belongie, Truong Q. Nguyen:
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks. CoRR abs/1607.04648 (2016) - [i9]Zachary C. Lipton, Jianfeng Gao, Lihong Li, Xiujun Li, Faisal Ahmed, Li Deng:
Efficient Exploration for Dialog Policy Learning with Deep BBQ Networks \& Replay Buffer Spiking. CoRR abs/1608.05081 (2016) - [i8]Zachary C. Lipton, Jianfeng Gao, Lihong Li, Jianshu Chen, Li Deng:
Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear. CoRR abs/1611.01211 (2016) - [i7]Xiujun Li, Zachary C. Lipton, Bhuwan Dhingra, Lihong Li, Jianfeng Gao, Yun-Nung Chen:
A User Simulator for Task-Completion Dialogues. CoRR abs/1612.05688 (2016) - 2015
- [i6]Zachary Chase Lipton, Charles Elkan:
Efficient Elastic Net Regularization for Sparse Linear Models. CoRR abs/1505.06449 (2015) - [i5]Zachary Chase Lipton:
A Critical Review of Recurrent Neural Networks for Sequence Learning. CoRR abs/1506.00019 (2015) - [i4]Zachary Chase Lipton, David C. Kale, Randall C. Wetzel:
Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks. CoRR abs/1510.07641 (2015) - [i3]Zachary Chase Lipton, Sharad Vikram, Julian J. McAuley:
Capturing Meaning in Product Reviews with Character-Level Generative Text Models. CoRR abs/1511.03683 (2015) - 2014
- [c1]Zachary Chase Lipton, Charles Elkan, Balakrishnan Narayanaswamy:
Optimal Thresholding of Classifiers to Maximize F1 Measure. ECML/PKDD (2) 2014: 225-239 - [i2]Zachary Chase Lipton, Charles Elkan, Balakrishnan Narayanaswamy:
F1-Optimal Thresholding in the Multi-Label Setting. CoRR abs/1402.1892 (2014) - [i1]Zhanglong Ji, Zachary Chase Lipton, Charles Elkan:
Differential Privacy and Machine Learning: a Survey and Review. CoRR abs/1412.7584 (2014)
Coauthor Index
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