Zachary C. Lipton
Associate Professor
Studies robust machine learning under distribution shift, machine learning for healthcare and the social impact of ML.
Zachary C. Lipton's lab at CMU works on trustworthy & safe ML. 52 papers since 2023, 33 at top venues, mostly NeurIPS and ICML. Applications close .
At a glance
- papers, 2023–2026
- 14/ year
- at T1 venues
- 63%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 1 papers2026 · T1 1 · collecting
- 8 papers2025 · T1 5
- 21 papers2024 · T1 13
- 22 papers2023 · T1 14
T1 venuesOtherVenues: NeurIPS, ICML, ICLR, AISTATS, AAAI/ACM AIES
Research focuspaper titles that use each topic's words
- Trustworthy & safe ML4 papers
- shift4 papers
- studies robust0 papers
- robust machine0 papers
- social impact0 papers
Paper list
From DBLP.
20261 paper
20258 papers
- J. Am. Medical Informatics Assoc.AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation (opens in a new tab)
- Trans. Mach. Learn. Res.Expert Routing with Synthetic Data for Domain Incremental Learning (opens in a new tab)
- Trans. Mach. Learn. Res.LLM-Select: Feature Selection with Large Language Models (opens in a new tab)
- NeurIPS (T1)OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics (opens in a new tab)
- NeurIPS (T1)Position: Towards Bidirectional Human-AI Alignment (opens in a new tab)
- NeurIPS (T1)Safety Pretraining: Toward the Next Generation of Safe AI (opens in a new tab)
- ICML (T1)Towards characterizing the value of edge embeddings in Graph Neural Networks (opens in a new tab)
- NeurIPS (T1)Valid Inference with Imperfect Synthetic Data (opens in a new tab)
202421 papers
- NeurIPS (T1)A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning (opens in a new tab)
- ACL (T1)Analyzing LLM Behavior in Dialogue Summarization: Unveiling Circumstantial Hallucination Trends (opens in a new tab)
- AISTATS (T1)Auditing Fairness under Unobserved Confounding (opens in a new tab)
- EACLEvaluating the Factuality of Zero-shot Summarizers Across Varied Domains (opens in a new tab)
- ICML (T1)Foundations of Testing for Finite-Sample Causal Discovery (opens in a new tab)
- Findings of EACLGoodhart's Law Applies to NLP's Explanation Benchmarks (opens in a new tab)
- EMNLP (T1)Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? (opens in a new tab)
- ALTPartially Interpretable Models with Guarantees on Coverage and Accuracy (opens in a new tab)
- NeurIPS (T1)Post-Hoc Reversal: Are We Selecting Models Prematurely? (opens in a new tab)
- AAAI/ACM AIESRed-Teaming for Generative AI: Silver Bullet or Security Theater? (opens in a new tab)
- ACM QueueResolving the Human-subjects Status of Machine Learning's Crowdworkers: What ethical framework should govern the interaction of ML researchers and crowdworkers? (opens in a new tab)
- Commun. ACMResolving the Human-Subjects Status of ML's Crowdworkers (opens in a new tab)
202322 papers
- CHI (T1)A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed Bandits (opens in a new tab)
- ICML (T1)Can Neural Network Memorization Be Localized? (opens in a new tab)
- ICML (T1)CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets (opens in a new tab)
- NeurIPS (T1)Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift (opens in a new tab)
- NeurIPS (T1)Deep Equilibrium Based Neural Operators for Steady-State PDEs (opens in a new tab)
- HCMIR@ISMIRDeepDrake ft. BTS-GAN and TayloRVC: An Exploratory Analysis of Musical Deepfakes and Hosting Platforms (opens in a new tab)
- ICLR (T1)Disentangling the Mechanisms Behind Implicit Regularization in SGD (opens in a new tab)
- AISTATS (T1)Domain Adaptation under Missingness Shift (opens in a new tab)
- ACL (T1)Downstream Datasets Make Surprisingly Good Pretraining Corpora (opens in a new tab)
- CHILEvaluating Model Performance in Medical Datasets Over Time (opens in a new tab)
- Trans. Mach. Learn. Res.Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation (opens in a new tab)
- AAAI/ACM AIESFrom Preference Elicitation to Participatory ML: A Critical Survey & Guidelines for Future Research (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to CMUFall 2027
CMU SCS Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL / IELTS / Duolingo (F-1/J-1 non-native speakers)
- Fee
- USD 100 (USD 80 by early deadline)
Confirmed on the program page.
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Data
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- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- The lab site and the paper record do not agree.
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