| 2025 | ICML | Online Conformal Prediction via Online Optimization. | Felipe Areces, Christopher Mohri, Tatsunori Hashimoto, John C. Duchi |
| 2024 | COLT | Two fundamental limits for uncertainty quantification in predictive inference. | Felipe Areces, Chen Cheng, John C. Duchi, Kuditipudi Rohith |
| 2024 | COLT | Universally Instance-Optimal Mechanisms for Private Statistical Estimation. | Hilal Asi, John C. Duchi, Saminul Haque, Zewei Li, Feng Ruan |
| 2024 | COLT | An information-theoretic lower bound in time-uniform estimation. | John C. Duchi, Saminul Haque |
| 2023 | AISTATS | Federated Asymptotics: a model to compare federated learning algorithms. | Gary Cheng, Karan N. Chadha, John C. Duchi |
| 2023 | COLT | A Pretty Fast Algorithm for Adaptive Private Mean Estimation. | Rohith Kuditipudi, John C. Duchi, Saminul Haque |
| 2022 | COLT | Memorize to generalize: on the necessity of interpolation in high dimensional linear regression. | Chen Cheng, John C. Duchi, Rohith Kuditipudi |
| 2022 | ICML | Private optimization in the interpolation regime: faster rates and hardness results. | Hilal Asi, Karan N. Chadha, Gary Cheng, John C. Duchi |
| 2022 | ICML | Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization. | Karan N. Chadha, Gary Cheng, John C. Duchi |
| 2021 | AISTATS | A constrained risk inequality for general losses. | John C. Duchi, Feng Ruan |
| 2021 | AISTATS | Misspecification in Prediction Problems and Robustness via Improper Learning. | Annie Marsden, John C. Duchi, Gregory Valiant |
| 2021 | ICML | Private Adaptive Gradient Methods for Convex Optimization. | Hilal Asi, John C. Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar |
| 2020 | COLT | Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. | Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan |
| 2020 | ICML | Understanding and Mitigating the Tradeoff between Robustness and Accuracy. | Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang |
| 2020 | ICML | FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis. | Aman Sinha, Matthew O'Kelly, Hongrui Zheng, Rahul Mangharam, John C. Duchi, Russ Tedrake |
| 2019 | AISTATS | Modeling simple structures and geometry for better stochastic optimization algorithms. | Hilal Asi, John C. Duchi |
| 2019 | COLT | A Rank-1 Sketch for Matrix Multiplicative Weights. | Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian |
| 2019 | COLT | Lower Bounds for Locally Private Estimation via Communication Complexity. | John C. Duchi, Ryan Rogers |
| 2018 | AISTATS | Derivative Free Optimization Via Repeated Classification. | Tatsunori Hashimoto, Steve Yadlowsky, John C. Duchi |
| 2018 | COLT | Minimax Bounds on Stochastic Batched Convex Optimization. | John C. Duchi, Feng Ruan, Chulhee Yun |
| 2018 | ICLR | Certifying Some Distributional Robustness with Principled Adversarial Training. | Aman Sinha, Hongseok Namkoong, John C. Duchi |
| 2017 | ICML | "Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions. | Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford |
| 2017 | ICML | Adaptive Sampling Probabilities for Non-Smooth Optimization. | Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John C. Duchi |
| 2016 | ICML | Estimation from Indirect Supervision with Linear Moments. | Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang |
| 2015 | COLT | Minimax rates for memory-bounded sparse linear regression. | Jacob Steinhardt, John C. Duchi |
| 2013 | CIDR | MLbase: A Distributed Machine-learning System. | Tim Kraska, Ameet Talwalkar, John C. Duchi, Rean Griffith, Michael J. Franklin, Michael I. Jordan |
| 2013 | COLT | Divide and Conquer Kernel Ridge Regression. | Yuchen Zhang, John C. Duchi, Martin J. Wainwright |
| 2013 | FOCS | Local Privacy and Statistical Minimax Rates. | John C. Duchi, Michael I. Jordan, Martin J. Wainwright |
| 2010 | COLT | Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. | John C. Duchi, Elad Hazan, Yoram Singer |
| 2010 | COLT | Composite Objective Mirror Descent. | John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Ambuj Tewari |
| 2010 | ICML | On the Consistency of Ranking Algorithms. | John C. Duchi, Lester W. Mackey, Michael I. Jordan |
| 2009 | ICML | Boosting with structural sparsity. | John C. Duchi, Yoram Singer |
| 2008 | ICML | Efficient projections onto the | John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Tushar Chandra |
| 2008 | UAI | Projected Subgradient Methods for Learning Sparse Gaussians. | John C. Duchi, Stephen Gould, Daphne Koller |
| 2008 | UAI | Constrained Approximate Maximum Entropy Learning of Markov Random Fields. | Varun Ganapathi, David Vickrey, John C. Duchi, Daphne Koller |