Optimal Thresholding of Classifiers to Maximize F1 Measure. - Zachary Chase Lipton; Charles Elkan; Balakrishnan Narayanaswamy (ECML/PKDD (2), 2014)

Differential Privacy and Machine Learning: a Survey and Review. - Zhanglong Ji; Zachary Chase Lipton; Charles Elkan (CoRR, 2014)

F1-Optimal Thresholding in the Multi-Label Setting. - Zachary Chase Lipton; Charles Elkan; Balakrishnan Narayanaswamy (CoRR, 2014)

A Critical Review of Recurrent Neural Networks for Sequence Learning. - Zachary Chase Lipton (CoRR, 2015)

Efficient Elastic Net Regularization for Sparse Linear Models. - Zachary Chase Lipton; Charles Elkan (CoRR, 2015)

Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks. - Zachary Chase Lipton; David C. Kale; Randall C. Wetzel (CoRR, 2015)

Capturing Meaning in Product Reviews with Character-Level Generative Text Models. - Zachary Chase Lipton; Sharad Vikram; Julian J. McAuley (CoRR, 2015)

Context Matters: Refining Object Detection in Video with Recurrent Neural Networks. - Subarna Tripathi; Zachary C. Lipton; Serge J. Belongie; Truong Q. Nguyen (BMVC, 2016)

Directly Modeling Missing Data in Sequences with RNNs: Improved Classification of Clinical Time Series. - Zachary C. Lipton; David C. Kale; Randall C. Wetzel (MLHC, 2016)

A User Simulator for Task-Completion Dialogues. - Xiujun Li; Zachary C. Lipton; Bhuwan Dhingra; Lihong Li 001; Jianfeng Gao 001; Yun-Nung Chen (CoRR, 2016)

Stuck in a What? Adventures in Weight Space. - Zachary Chase Lipton (CoRR, 2016)

Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear. - Zachary C. Lipton; Jianfeng Gao 001; Lihong Li 001; Jianshu Chen; Li Deng 001 (CoRR, 2016)

Efficient Exploration for Dialog Policy Learning with Deep BBQ Networks & Replay Buffer Spiking. - Zachary C. Lipton; Jianfeng Gao 001; Lihong Li 001; Xiujun Li; Faisal Ahmed 001; Li Deng 001 (CoRR, 2016)

Learning to Diagnose with LSTM Recurrent Neural Networks. - Zachary Chase Lipton; David C. Kale; Charles Elkan; Randall C. Wetzel (ICLR (Poster), 2016)

Tensor Contraction Layers for Parsimonious Deep Nets. - Jean Kossaifi; Aran Khanna; Zachary C. Lipton; Tommaso Furlanello; Anima Anandkumar (CVPR Workshops, 2017)

Dance Dance Convolution. - Chris Donahue; Zachary C. Lipton; Julian J. McAuley (ICLR (Workshop), 2017)

Precise Recovery of Latent Vectors from Generative Adversarial Networks. - Zachary C. Lipton; Subarna Tripathi (ICLR (Workshop), 2017)

Estimating Reactions and Recommending Products with Generative Models of Reviews. - Jianmo Ni; Zachary C. Lipton; Sharad Vikram; Julian J. McAuley (IJCNLP(1), 2017)

Predicting Surgery Duration with Neural Heteroscedastic Regression. - Nathan H. Ng; Rodney A. Gabriel; Julian J. McAuley; Charles Elkan; Zachary C. Lipton (MLHC, 2017)

Deep Active Learning for Named Entity Recognition. - Yanyao Shen; Hyokun Yun; Zachary Chase Lipton; Yakov Kronrod; Animashree Anandkumar (Rep4NLP@ACL, 2017)

Tensor Regression Networks. - Jean Kossaifi; Zachary C. Lipton; Aran Khanna; Tommaso Furlanello; Anima Anandkumar (CoRR, 2017)

Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals. - John Alberg; Zachary C. Lipton (CoRR, 2017)

Does mitigating ML's disparate impact require disparate treatment? - Zachary C. Lipton; Alexandra Chouldechova; Julian J. McAuley (CoRR, 2017)

Learning from Temporally-Structured Human Activities Data. - Zachary Chase Lipton (2017)

BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems. - Zachary C. Lipton; Xiujun Li; Jianfeng Gao 001; Lihong Li 001; Faisal Ahmed 001; Li Deng 001 (AAAI, 2018)

How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks. - Divyansh Kaushik; Zachary C. Lipton (EMNLP, 2018)

Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study. - Aditya Siddhant; Zachary C. Lipton (EMNLP, 2018)

Stochastic Activation Pruning for Robust Adversarial Defense. - Guneet S. Dhillon; Kamyar Azizzadenesheli; Zachary C. Lipton; Jeremy Bernstein; Jean Kossaifi; Aran Khanna; Animashree Anandkumar (ICLR (Poster), 2018)

Semantically Decomposing the Latent Spaces of Generative Adversarial Networks. - Chris Donahue; Zachary C. Lipton; Akshay Balsubramani; Julian J. McAuley (ICLR (Poster), 2018)

Learning From Noisy Singly-labeled Data. - Ashish Khetan; Zachary C. Lipton; Animashree Anandkumar (ICLR (Poster), 2018)

Predicting Embryo Morphokinetics in Videos with Late Fusion Nets & Dynamic Decoders. - Nathan H. Ng; Julian J. McAuley; Julian Gingold; Nina Desai; Zachary C. Lipton (ICLR (Workshop), 2018)

Born-Again Neural Networks. - Tommaso Furlanello; Zachary Chase Lipton; Michael Tschannen; Laurent Itti; Anima Anandkumar (ICML, 2018)

Detecting and Correcting for Label Shift with Black Box Predictors. - Zachary C. Lipton; Yu-Xiang Wang 003; Alexander J. Smola (ICML, 2018)

Does mitigating ML's impact disparity require treatment disparity? - Zachary C. Lipton; Julian J. McAuley; Alexandra Chouldechova (NeurIPS, 2018)

Learning Noise-Invariant Representations for Robust Speech Recognition. - Davis Liang; Zhiheng Huang; Zachary C. Lipton (SLT, 2018)

The mythos of model interpretability. - Zachary C. Lipton (Commun. ACM, 2018)

Correction by Projection: Denoising Images with Generative Adversarial Networks. - Subarna Tripathi; Zachary C. Lipton; Truong Q. Nguyen (CoRR, 2018)

Sample-Efficient Deep RL with Generative Adversarial Tree Search. - Kamyar Azizzadenesheli; Brandon Yang; Weitang Liu; Emma Brunskill; Zachary C. Lipton; Animashree Anandkumar (CoRR, 2018)

How transferable are the datasets collected by active learners? - David Lowell; Zachary C. Lipton; Byron C. Wallace (CoRR, 2018)

Weighted Risk Minimization & Deep Learning. - Jonathon Byrd; Zachary C. Lipton (CoRR, 2018)

Combating Adversarial Misspellings with Robust Word Recognition. - Danish Pruthi; Bhuwan Dhingra; Zachary C. Lipton (ACL (1), 2019)

Entity Projection via Machine Translation for Cross-Lingual NER. - Alankar Jain; Bhargavi Paranjape; Zachary C. Lipton (EMNLP/IJCNLP (1), 2019)

Practical Obstacles to Deploying Active Learning. - David Lowell; Zachary C. Lipton; Byron C. Wallace (EMNLP/IJCNLP (1), 2019)

Active Learning with Partial Feedback. - Peiyun Hu; Zachary C. Lipton; Anima Anandkumar; Deva Ramanan (ICLR (Poster), 2019)

Learning Robust Representations by Projecting Superficial Statistics Out. - Haohan Wang; Zexue He; Zachary C. Lipton; Eric P. Xing (ICLR, 2019)

What is the Effect of Importance Weighting in Deep Learning? - Jonathon Byrd; Zachary Chase Lipton (ICML, 2019)

Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment. - Yifan Wu; Ezra Winston; Divyansh Kaushik; Zachary C. Lipton (ICML, 2019)

AmazonQA: A Review-Based Question Answering Task. - Mansi Gupta; Nitish Kulkarni; Raghuveer Chanda; Anirudha Rayasam; Zachary C. Lipton (IJCAI, 2019)

Embryo Staging with Weakly-Supervised Region Selection and Dynamically-Decoded Predictions. - Tingfung Lau; Nathan Ng 001; Julian Gingold; Nina Desai; Julian J. McAuley; Zachary C. Lipton (MLHC, 2019)

Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. - Stephan Rabanser; Stephan Günnemann; Zachary C. Lipton (NeurIPS, 2019)

Learning Robust Global Representations by Penalizing Local Predictive Power. - Haohan Wang; Songwei Ge; Zachary C. Lipton; Eric P. Xing (NeurIPS, 2019)

Game Design for Eliciting Distinguishable Behavior. - Fan Yang 058; Liu Leqi; Yifan Wu; Zachary Chase Lipton; Pradeep Ravikumar; Tom M. Mitchell; William W. Cohen (NeurIPS, 2019)

Research for practice: troubling trends in machine-learning scholarship. - Zachary C. Lipton; Jacob Steinhardt (Commun. ACM, 2019)

Temporal-Clustering Invariance in Irregular Healthcare Time Series. - Mohammad Taha Bahadori; Zachary Chase Lipton (CoRR, 2019)

Learning Causal State Representations of Partially Observable Environments. - Amy Zhang 001; Zachary C. Lipton; Luis Pineda; Kamyar Azizzadenesheli; Anima Anandkumar; Laurent Itti; Joelle Pineau; Tommaso Furlanello (CoRR, 2019)

Estimating brain age based on a healthy population with deep learning and structural MRI. - Xinyang Feng; Zachary C. Lipton; Jie Yang 041; Scott A. Small; Frank A. Provenzano (CoRR, 2019)

Accelerating Deep Learning by Focusing on the Biggest Losers. - 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 (CoRR, 2019)

Are Perceptually-Aligned Gradients a General Property of Robust Classifiers? - Simran Kaur 001; Jeremy Cohen 001; Zachary C. Lipton (CoRR, 2019)

Troubling Trends in Machine Learning Scholarship. - Zachary C. Lipton; Jacob Steinhardt (ACM Queue, 2019)

Learning to Deceive with Attention-Based Explanations. - Danish Pruthi; Mansi Gupta; Bhuwan Dhingra; Graham Neubig; Zachary C. Lipton (ACL, 2020)

Algorithmic Fairness from a Non-ideal Perspective. - Sina Fazelpour; Zachary C. Lipton (AIES, 2020)

Mortality Risk Score for Critically Ill Patients with Viral or Unspecified Pneumonia: Assisting Clinicians with COVID-19 ECMO Planning. - Helen Zhou; Cheng Cheng 001; Zachary C. Lipton; George H. Chen; Jeremy C. Weiss (AIME, 2020)

Weakly- and Semi-supervised Evidence Extraction. - Danish Pruthi; Bhuwan Dhingra; Graham Neubig; Zachary C. Lipton (EMNLP (Findings), 2020)

On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment. - Zirui Wang; Zachary C. Lipton; Yulia Tsvetkov (EMNLP (1), 2020)

Efficient Candidate Screening Under Multiple Tests and Implications for Fairness. - Lee Cohen 001; Zachary C. Lipton; Yishay Mansour (FORC, 2020)

Learning The Difference That Makes A Difference With Counterfactually-Augmented Data. - Divyansh Kaushik; Eduard H. Hovy; Zachary Chase Lipton (ICLR, 2020)

Uncertainty-Aware Lookahead Factor Models for Quantitative Investing. - Lakshay Chauhan; John Alberg; Zachary C. Lipton (ICML, 2020)

A Unified View of Label Shift Estimation. - Saurabh Garg; Yifan Wu; Sivaraman Balakrishnan; Zachary C. Lipton (NeurIPS, 2020)

SIGMOD 2020 Tutorial on Fairness and Bias in Peer Review and Other Sociotechnical Intelligent Systems. - Nihar B. Shah; Zachary C. Lipton (SIGMOD Conference, 2020)

Machine Learning for Healthcare: Beyond i.i.d. Prediction. - Zachary C. Lipton (HSDM@WSDM, 2020)

How Transferable are the Representations Learned by Deep Q Agents? - Jacob Tyo; Zachary C. Lipton (CoRR, 2020)

Generating SOAP Notes from Doctor-Patient Conversations. - Kundan Krishna; Sopan Khosla; Jeffrey P. Bigham; Zachary C. Lipton (CoRR, 2020)

Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist Clinicians with COVID-19 ECMO Planning. - Helen Zhou; Cheng Cheng 001; Zachary C. Lipton; George H. Chen; Jeremy C. Weiss (CoRR, 2020)

Extracting Structured Data from Physician-Patient Conversations By Predicting Noteworthy Utterances. - Kundan Krishna; Amy Pavel; Benjamin Schloss; Jeffrey P. Bigham; Zachary C. Lipton (CoRR, 2020)

Decoding and Diversity in Machine Translation. - Nicholas Roberts; Davis Liang; Graham Neubig; Zachary C. Lipton (CoRR, 2020)

Evaluating Explanations: How much do explanations from the teacher aid students? - Danish Pruthi; Bhuwan Dhingra; Livio Baldini Soares; Michael Collins 001; Zachary C. Lipton; Graham Neubig; William W. Cohen (CoRR, 2020)

Tensor Regression Networks. - Jean Kossaifi; Zachary C. Lipton; Arinbjörn Kolbeinsson; Aran Khanna; Tommaso Furlanello; Anima Anandkumar (J. Mach. Learn. Res., 2020)

Symbolic Music Generation with Transformer-GANs. - Aashiq Muhamed; Liang Li; Xingjian Shi; Suri Yaddanapudi; Wayne Chi; Dylan Jackson; Rahul Suresh; Zachary C. Lipton; Alexander J. Smola (AAAI, 2021)

On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study. - Divyansh Kaushik; Douwe Kiela; Zachary C. Lipton; Wen-tau Yih (ACL/IJCNLP (1), 2021)

Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques. - Kundan Krishna; Sopan Khosla; Jeffrey P. Bigham; Zachary C. Lipton (ACL/IJCNLP (1), 2021)

Fair Machine Learning Under Partial Compliance. - Jessica Dai; Sina Fazelpour; Zachary C. Lipton (AIES, 2021)

On the Validity of Arrest as a Proxy for Offense: Race and the Likelihood of Arrest for Violent Crimes. - Riccardo Fogliato; Alice Xiang; Zachary C. Lipton; Daniel Nagin; Alexandra Chouldechova (AIES, 2021)

Causal Inference with Selectively Deconfounded Data. - Kyra Gan; Andrew A. Li; Zachary Chase Lipton; Sridhar R. Tayur (AISTATS, 2021)

Unpacking the Drop in COVID-19 Case Fatality Rates: A Study of National and Florida Line-Level Data. - Cheng Cheng 001; Helen Zhou; Jeremy C. Weiss; Zachary C. Lipton (AMIA, 2021)

Siamese Bert for Authorship Verification. - Jacob Tyo; Bhuwan Dhingra; Zachary C. Lipton (CLEF (Working Notes), 2021)

Does Pretraining for Summarization Require Knowledge Transfer? - Kundan Krishna; Jeffrey P. Bigham; Zachary C. Lipton (EMNLP (Findings), 2021)

Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data. - David Lowell; Brian E. Howard; Zachary C. Lipton; Byron C. Wallace (EMNLP (1), 2021)

Explaining the Efficacy of Counterfactually Augmented Data. - Divyansh Kaushik; Amrith Setlur; Eduard H. Hovy; Zachary Chase Lipton (ICLR, 2021)

RATT: Leveraging Unlabeled Data to Guarantee Generalization. - Saurabh Garg; Sivaraman Balakrishnan; J. Zico Kolter; Zachary C. Lipton (ICML, 2021)

On Proximal Policy Optimization's Heavy-tailed Gradients. - Saurabh Garg; Joshua Zhanson; Emilio Parisotto; Adarsh Prasad; J. Zico Kolter; Zachary C. Lipton; Sivaraman Balakrishnan; Ruslan Salakhutdinov; Pradeep Ravikumar (ICML, 2021)

Correcting Exposure Bias for Link Recommendation. - Shantanu Gupta; Hao Wang 014; Zachary C. Lipton; Yuyang Wang 001 (ICML, 2021)

Mixture Proportion Estimation and PU Learning: A Modern Approach. - Saurabh Garg; Yifan Wu; Alexander J. Smola; Sivaraman Balakrishnan; Zachary C. Lipton (NeurIPS, 2021)

Efficient Online Estimation of Causal Effects by Deciding What to Observe. - Shantanu Gupta; Zachary C. Lipton; David Childers (NeurIPS, 2021)

Off-Policy Risk Assessment in Contextual Bandits. - Audrey Huang; Liu Leqi; Zachary C. Lipton; Kamyar Azizzadenesheli (NeurIPS, 2021)

Rebounding Bandits for Modeling Satiation Effects. - Liu Leqi; Fatma Kilinç-Karzan; Zachary C. Lipton; Alan L. Montgomery (NeurIPS, 2021)

Parametric Complexity Bounds for Approximating PDEs with Neural Networks. - Tanya Marwah; Zachary C. Lipton; Andrej Risteski (NeurIPS, 2021)

Estimating treatment effects with observed confounders and mediators. - Shantanu Gupta; Zachary C. Lipton; David Childers (UAI, 2021)

When curation becomes creation. - Liu Leqi; Dylan Hadfield-Menell; Zachary C. Lipton (Commun. ACM, 2021)

On the Convergence and Optimality of Policy Gradient for Markov Coherent Risk. - Audrey Huang; Liu Leqi; Zachary C. Lipton; Kamyar Azizzadenesheli (CoRR, 2021)

Dive into Deep Learning. - Aston Zhang; Zachary C. Lipton; Mu Li 003; Alexander J. Smola (CoRR, 2021)

The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studies. - Riccardo Fogliato; Alexandra Chouldechova; Zachary C. Lipton (Proc. ACM Hum. Comput. Interact., 2021)

When Curation Becomes Creation: Algorithms, microcontent, and the vanishing distinction between platforms and creators. - Liu Leqi; Dylan Hadfield-Menell; Zachary C. Lipton (ACM Queue, 2021)

Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations. - Siddhant Arora; Danish Pruthi; Norman M. Sadeh; William W. Cohen; Zachary C. Lipton; Graham Neubig (AAAI, 2022)

Modeling Attrition in Recommender Systems with Departing Bandits. - Omer Ben-Porat; Lee Cohen 001; Liu Leqi; Zachary C. Lipton; Yishay Mansour (AAAI, 2022)

Off-Policy Risk Assessment for Markov Decision Processes. - Audrey Huang; Liu Leqi; Zachary C. Lipton; Kamyar Azizzadenesheli (AISTATS, 2022)

Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19. - Helen Zhou; Cheng Cheng 001; Kelly J. Shields; Gursimran Kochhar; Tariq Cheema; Zachary C. Lipton; Jeremy C. Weiss (AMIA, 2022)

Practical Benefits of Feature Feedback Under Distribution Shift. - Anurag Katakkar; Clay H. Yoo; Weiqin Wang; Zachary C. Lipton; Divyansh Kaushik (BlackboxNLP@EMNLP, 2022)

Homophily and Incentive Effects in Use of Algorithms. - Riccardo Fogliato; Sina Fazelpour; Shantanu Gupta; Zachary C. Lipton; David Danks (CogSci, 2022)

On the Maximum Hessian Eigenvalue and Generalization. - Simran Kaur 001; Jeremy Cohen 001; Zachary Chase Lipton (ICBINB, 2022)

Leveraging unlabeled data to predict out-of-distribution performance. - Saurabh Garg; Sivaraman Balakrishnan; Zachary Chase Lipton; Behnam Neyshabur; Hanie Sedghi (ICLR, 2022)

Supervised Learning with General Risk Functionals. - Liu Leqi; Audrey Huang; Zachary C. Lipton; Kamyar Azizzadenesheli (ICML, 2022)

Domain Adaptation under Open Set Label Shift. - Saurabh Garg; Sivaraman Balakrishnan; Zachary C. Lipton (NeurIPS, 2022)

Characterizing Datapoints via Second-Split Forgetting. - Pratyush Maini; Saurabh Garg; Zachary C. Lipton; J. Zico Kolter (NeurIPS, 2022)

Unsupervised Learning under Latent Label Shift. - Manley Roberts; Pranav Mani; Saurabh Garg; Zachary C. Lipton (NeurIPS, 2022)

Can Transformers be Strong Treatment Effect Estimators? - Yifan Zhang 004; Hanlin Zhang 002; Zachary C. Lipton; Li Erran Li; Eric P. Xing (CoRR, 2022)

Resolving the Human Subjects Status of Machine Learning's Crowdworkers. - Divyansh Kaushik; Zachary C. Lipton; Alex John London (CoRR, 2022)

On the State of the Art in Authorship Attribution and Authorship Verification. - Jacob Tyo; Bhuwan Dhingra; Zachary C. Lipton (CoRR, 2022)

Data drift correction via time-varying importance weight estimator. - Rasool Fakoor; Jonas Mueller 001; Zachary C. Lipton; Pratik Chaudhari; Alexander J. Smola (CoRR, 2022)

Model Evaluation in Medical Datasets Over Time. - Helen Zhou; Yuwen Chen; Zachary C. Lipton (CoRR, 2022)

Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students? - Danish Pruthi; Rachit Bansal; Bhuwan Dhingra; Livio Baldini Soares; Michael Collins 001; Zachary C. Lipton; Graham Neubig; William W. Cohen (Trans. Assoc. Comput. Linguistics, 2022)

Moral Machine or Tyranny of the Majority? - Michael Feffer; Hoda Heidari; Zachary C. Lipton (AAAI, 2023)

Downstream Datasets Make Surprisingly Good Pretraining Corpora. - Kundan Krishna; Saurabh Garg; Jeffrey P. Bigham; Zachary C. Lipton (ACL (1), 2023)

From Preference Elicitation to Participatory ML: A Critical Survey & Guidelines for Future Research. - Michael Feffer; Michael Skirpan; Zachary C. Lipton; Hoda Heidari (AIES, 2023)

Domain Adaptation under Missingness Shift. - Helen Zhou; Sivaraman Balakrishnan; Zachary C. Lipton (AISTATS, 2023)

A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed Bandits. - Liu Leqi; Giulio Zhou; Fatma Kilinç-Karzan; Zachary C. Lipton; Alan L. Montgomery (CHI, 2023)

Evaluating Model Performance in Medical Datasets Over Time. - Helen Zhou; Yuwen Chen; Zachary C. Lipton (CHIL, 2023)

Local Causal Discovery for Estimating Causal Effects. - Shantanu Gupta; David Childers; Zachary Chase Lipton (CLeaR, 2023)

USB: A Unified Summarization Benchmark Across Tasks and Domains. - Kundan Krishna; Prakhar Gupta; Sanjana Ramprasad; Byron C. Wallace; Jeffrey P. Bigham; Zachary C. Lipton (EMNLP (Findings), 2023)

Model-tuning Via Prompts Makes NLP Models Adversarially Robust. - Mrigank Raman; Pratyush Maini; J. Zico Kolter; Zachary C. Lipton; Danish Pruthi (EMNLP, 2023)

DeepDrake ft. BTS-GAN and TayloRVC: An Exploratory Analysis of Musical Deepfakes and Hosting Platforms. - Michael Feffer; Zachary C. Lipton; Chris Donahue (HCMIR@ISMIR, 2023)

Disentangling the Mechanisms Behind Implicit Regularization in SGD. - Zachary Novack; Simran Kaur 001; Tanya Marwah; Saurabh Garg; Zachary Chase Lipton (ICLR, 2023)

RLSbench: Domain Adaptation Under Relaxed Label Shift. - Saurabh Garg; Nick Erickson; James Sharpnack; Alex Smola; Sivaraman Balakrishnan; Zachary Chase Lipton (ICML, 2023)

Can Neural Network Memorization Be Localized? - Pratyush Maini; Michael Curtis Mozer; Hanie Sedghi; Zachary Chase Lipton; J. Zico Kolter; Chiyuan Zhang (ICML, 2023)

Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective. - Tanya Marwah; Zachary Chase Lipton; Jianfeng Lu 001; Andrej Risteski (ICML, 2023)

CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets. - Zachary Novack; Julian J. McAuley; Zachary Chase Lipton; Saurabh Garg (ICML, 2023)

Valla: Standardizing and Benchmarking Authorship Attribution and Verification Through Empirical Evaluation and Comparative Analysis. - Jacob Tyo; Bhuwan Dhingra; Zachary C. Lipton (IJCNLP (1), 2023)

Reliable deep learning in dynamic environments. - Zachary Chase Lipton (Computer-Aided Diagnosis, 2023)

Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms. - Dheeraj Baby; Saurabh Garg; Tzu-Ching Yen; Sivaraman Balakrishnan; Zachary C. Lipton; Yu-Xiang Wang 003 (NeurIPS, 2023)

Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift. - Saurabh Garg; Amrith Setlur; Zachary C. Lipton; Sivaraman Balakrishnan; Virginia Smith; Aditi Raghunathan (NeurIPS, 2023)

Deep Equilibrium Based Neural Operators for Steady-State PDEs. - Tanya Marwah; Ashwini Pokle; J. Zico Kolter; Zachary C. Lipton; Jianfeng Lu 001; Andrej Risteski (NeurIPS, 2023)

Risk-limiting financial audits via weighted sampling without replacement. - Shubhanshu Shekhar; Ziyu Xu 001; Zachary C. Lipton; Pierre J. Liang; Aaditya Ramdas (UAI, 2023)

Meta-Learning Mini-Batch Risk Functionals. - Jacob Tyo; Zachary C. Lipton (CoRR, 2023)

Discovering Optimal Scoring Mechanisms in Causal Strategic Prediction. - Tom Yan; Shantanu Gupta; Zachary C. Lipton (CoRR, 2023)

Users are the North Star for AI Transparency. - Alex Mei; Michael Saxon; Shiyu Chang; Zachary C. Lipton; William Yang Wang (CoRR, 2023)

PromptNER: Prompting For Named Entity Recognition. - Dhananjay Ashok; Zachary C. Lipton (CoRR, 2023)

MUDD: A New Re-Identification Dataset with Efficient Annotation for Off-Road Racers in Extreme Conditions. - Jacob Tyo; Motolani Olarinre; Youngseog Chung; Zachary C. Lipton (CoRR, 2023)

Reading Between the Mud: A Challenging Motorcycle Racer Number Dataset. - Jacob Tyo; Youngseog Chung; Motolani Olarinre; Zachary C. Lipton (CoRR, 2023)

MoCo-Transfer: Investigating out-of-distribution contrastive learning for limited-data domains. - Yuwen Chen; Helen Zhou; Zachary C. Lipton (CoRR, 2023)

Perspectives on the State and Future of Deep Learning - 2023. - Micah Goldblum; Anima Anandkumar; Richard G. Baraniuk; Tom Goldstein; Kyunghyun Cho; Zachary C. Lipton; Melanie Mitchell; Preetum Nakkiran; Max Welling; Andrew Gordon Wilson (CoRR, 2023)

Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation. - Yifan Zhang 004; Hanlin Zhang 002; Zachary Chase Lipton; Li Erran Li; Eric P. Xing (Trans. Mach. Learn. Res., 2023)

Analyzing LLM Behavior in Dialogue Summarization: Unveiling Circumstantial Hallucination Trends. - Sanjana Ramprasad; Elisa Ferracane; Zachary C. Lipton (ACL (1), 2024)

Red-Teaming for Generative AI: Silver Bullet or Security Theater? - Michael Feffer; Anusha Sinha; Wesley H. Deng; Zachary C. Lipton; Hoda Heidari (AIES (1), 2024)

Auditing Fairness under Unobserved Confounding. - Yewon Byun; Dylan Sam; Michael Oberst; Zachary C. Lipton; Bryan Wilder (AISTATS, 2024)

Timing as an Action: Learning When to Observe and Act. - Helen Zhou; Audrey Huang; Kamyar Azizzadenesheli; David Childers; Zachary C. Lipton (AISTATS, 2024)

Partially Interpretable Models with Guarantees on Coverage and Accuracy. - Nave Frost; Zachary C. Lipton; Yishay Mansour; Michal Moshkovitz (ALT, 2024)

The Future of Web Data Mining: Insights from Multimodal and Code-based Extraction Methods. - Evan Fellman; Jacob Tyo; Zachary C. Lipton (CASE, 2024)

Scaling Laws for Data Filtering - Data Curation Cannot be Compute Agnostic. - Sachin Goyal; Pratyush Maini; Zachary C. Lipton; Aditi Raghunathan; J. Zico Kolter (CVPR, 2024)

Goodhart's Law Applies to NLP's Explanation Benchmarks. - Jennifer Hsia; Danish Pruthi; Aarti Singh; Zachary C. Lipton (EACL (Findings), 2024)

Evaluating the Factuality of Zero-shot Summarizers Across Varied Domains. - Sanjana Ramprasad; Kundan Krishna; Zachary C. Lipton; Byron C. Wallace (EACL (2), 2024)

Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? - Daniel P. Jeong; Saurabh Garg; Zachary C. Lipton; Michael Oberst (EMNLP, 2024)

The Impact of Differential Feature Under-reporting on Algorithmic Fairness. - Nil-Jana Akpinar; Zachary C. Lipton; Alexandra Chouldechova (FAccT, 2024)

Time-Varying Propensity Score to Bridge the Gap between the Past and Present. - Rasool Fakoor; Jonas Mueller 001; Zachary Chase Lipton; Pratik Chaudhari; Alex Smola (ICLR, 2024)

T-MARS: Improving Visual Representations by Circumventing Text Feature Learning. - Pratyush Maini; Sachin Goyal; Zachary Chase Lipton; J. Zico Kolter; Aditi Raghunathan (ICLR, 2024)

Foundations of Testing for Finite-Sample Causal Discovery. - Tom Yan; Ziyu Xu 001; Zachary Chase Lipton (ICML, 2024)

Understanding Hallucinations in Diffusion Models through Mode Interpolation. - Sumukh K. Aithal; Pratyush Maini; Zachary C. Lipton; J. Zico Kolter (NeurIPS, 2024)

The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning. - Jake Fawkes; Nic Fishman; Mel Andrews; Zachary C. Lipton (NeurIPS, 2024)

Post-Hoc Reversal: Are We Selecting Models Prematurely? - Rishabh Ranjan; Saurabh Garg; Mrigank Raman; Carlos Guestrin; Zachary C. Lipton (NeurIPS, 2024)

Rethinking LLM Memorization through the Lens of Adversarial Compression. - Avi Schwarzschild; Zhili Feng; Pratyush Maini; Zachary C. Lipton; J. Zico Kolter (NeurIPS, 2024)

A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning. - Tom Yan; Zachary C. Lipton (NeurIPS, 2024)

TOFU: A Task of Fictitious Unlearning for LLMs. - Pratyush Maini; Zhili Feng; Avi Schwarzschild; Zachary C. Lipton; J. Zico Kolter (CoRR, 2024)

Red-Teaming for Generative AI: Silver Bullet or Security Theater? - Michael Feffer; Anusha Sinha; Zachary C. Lipton; Hoda Heidari (CoRR, 2024)

Personalized Language Modeling from Personalized Human Feedback. - Xinyu Li; Zachary C. Lipton; Liu Leqi (CoRR, 2024)

Contrastive Multiple Instance Learning for Weakly Supervised Person ReID. - Jacob Tyo; Zachary C. Lipton (CoRR, 2024)

Beyond the Mud: Datasets and Benchmarks for Computer Vision in Off-Road Racing. - Jacob Tyo; Motolani Olarinre; Youngseog Chung; Zachary C. Lipton (CoRR, 2024)

GenAudit: Fixing Factual Errors in Language Model Outputs with Evidence. - Kundan Krishna; Sanjana Ramprasad; Prakhar Gupta; Byron C. Wallace; Zachary C. Lipton; Jeffrey P. Bigham (CoRR, 2024)

Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions. - Hua Shen 005; Tiffany Knearem; Reshmi Ghosh; Kenan Alkiek; Kundan Krishna; Yachuan Liu; Ziqiao Ma 001; Savvas Petridis; Yi-Hao Peng; Li Qiwei; Sushrita Rakshit; Chenglei Si; et al. (CoRR, 2024)

A Unified Causal Framework for Auditing Recommender Systems for Ethical Concerns. - Vibhhu Sharma; Shantanu Gupta; Nil-Jana Akpinar; Zachary C. Lipton; Liu Leqi (CoRR, 2024)

Towards characterizing the value of edge embeddings in Graph Neural Networks. - Dhruv Rohatgi; Tanya Marwah; Zachary Chase Lipton; Jianfeng Lu 001; Ankur Moitra; Andrej Risteski (CoRR, 2024)

Failure Modes of LLMs for Causal Reasoning on Narratives. - Khurram Yamin; Shantanu Gupta; Gaurav R. Ghosal; Zachary C. Lipton; Bryan Wilder (CoRR, 2024)

Online Data Collection for Efficient Semiparametric Inference. - Shantanu Gupta; Zachary C. Lipton; David Childers (CoRR, 2024)

The Limited Impact of Medical Adaptation of Large Language and Vision-Language Models. - Daniel P. Jeong; Pranav Mani; Saurabh Garg; Zachary C. Lipton; Michael Oberst (CoRR, 2024)

Generate to Discriminate: Expert Routing for Continual Learning. - Yewon Byun; Sanket Vaibhav Mehta; Saurabh Garg; Emma Strubell; Michael Oberst; Bryan Wilder; Zachary C. Lipton (CoRR, 2024)

Resolving the Human-subjects Status of Machine Learning's Crowdworkers: What ethical framework should govern the interaction of ML researchers and crowdworkers? - Divyansh Kaushik; Zachary C. Lipton; Alex John London (ACM Queue, 2024)

Resolving the Human-Subjects Status of ML's Crowdworkers. - Divyansh Kaushik; Zachary C. Lipton; Alex John London (Commun. ACM, 2024)

AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation. - 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; et al. (J. Am. Medical Informatics Assoc., 2025)

Expert Routing with Synthetic Data for Domain Incremental Learning. - Yewon Byun; Sanket Vaibhav Mehta; Saurabh Garg; Emma Strubell; Michael Oberst; Bryan Wilder; Zachary Chase Lipton (Trans. Mach. Learn. Res., 2025)

LLM-Select: Feature Selection with Large Language Models. - Daniel P. Jeong; Zachary Chase Lipton; Pradeep Kumar Ravikumar (Trans. Mach. Learn. Res., 2025)

AI Mentors for Student Projects: Spotting Early Issues in Computer Science Proposals. - Gati Aher; Robin Schmucker; Tom M. Mitchell; Zachary C. Lipton (CoRR, 2025)

No Free Lunch: Non-Asymptotic Analysis of Prediction-Powered Inference. - Pranav Mani; Peng Xu; Zachary C. Lipton; Michael Oberst (CoRR, 2025)

OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics. - Vineeth Dorna; Anmol Mekala; Wenlong Zhao 001; Andrew McCallum; Zachary C. Lipton; J. Zico Kolter; Pratyush Maini (CoRR, 2025)

Using Imperfect Synthetic Data in Downstream Inference Tasks. - Yewon Byun; Shantanu Gupta; Zachary C. Lipton; Rachel Leah Childers; Bryan Wilder (CoRR, 2025)