John Duchi
Associate Professor
Studies statistical machine learning, stochastic optimization and robustness.
John Duchi's lab at Stanford works on learning theory & optimization and trustworthy & safe ML. 17 papers since 2023, 7 at top venues, mostly NeurIPS and ICML. 7 PhD students. 18 alumni. Applications close .
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 41%
- PhD students
- 7
- MS students
- 0
- alumni listed
- 18
Papers by yearfrom the lab homepage, 2023–2026
- 4 papers2026 · T1 1 · collecting
- 3 papers2025 · T1 2
- 5 papers2024 · T1 1
- 5 papers2023 · T1 3
T1 venuesOtherVenues: NeurIPS, ICML, COLT, JMLR
Research focuspaper titles that use each topic's words
- Learning theory & optimization5 papers
- stochastic optimization2 papers
- stochastic2 papers
- Trustworthy & safe ML0 papers
- studies statistical0 papers
Paper list
From the lab homepage.
20264 papers
- NeurIPS (T1)A Bitter Lesson for Data Filtering (opens in a new tab)
- Mathematics of Operations ResearchGeometry, Computation, and Optimality in Stochastic Optimization (opens in a new tab)
- Journal of the American Statistical AssociationHow many labelers do you have? A closer look at gold-standard labels (opens in a new tab)
- Annals of Applied StatisticsPPI++: Efficient Prediction-Powered Inference (opens in a new tab)
20253 papers
20245 papers
- JMLR (T1)Predictive Inference with Weak Supervision (opens in a new tab)
- Journal of the American Statistical AssociationRobust Validation: Confident Predictions Even When Distributions Shift (opens in a new tab)
- Annals of StatisticsThe Right Complexity Measure in Locally Private Estimation: It is not the Fisher Information (opens in a new tab)
- Conference on Computational Learning Theory (COLT)Two fundamental limits for uncertainty quantification in predictive inference (opens in a new tab)
- Conference on Learning Theory (COLT)Universally Instance-Optimal Mechanisms for Private Statistical Estimation (opens in a new tab)
20235 papers
- COLT (T1)A Fast Algorithm for Adaptive Private Mean Estimation (opens in a new tab)
- NeurIPS (T1)Collaboratively Learning Linear Models with Structured Missing Data (opens in a new tab)
- ICML (T1)Differentially private heavy hitter detection using federated analytics (opens in a new tab)
- Artificial Intelligence and Statistics (AISTATS)Federated Asymptotics: a model to compare federated learning algorithms (opens in a new tab)
- Mathematical Programming, Series ALower Bounds for Non-Convex Stochastic Optimization (opens in a new tab)
Where alumni went18 listed
Apply to StanfordFall 2027
Stanford Computer Science PhD
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- Single round
- Opens
- GRE
- See program page
- English
- TOEFL/IELTS
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Confirmed on the program page.
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Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
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