John C. Duchi

ORCID

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 100

Top Venues

University of California, Berkeley, USA (PHD)

Education History

University of California, Berkeley, USA

PHD

Publications

John C. Duchi, Saminul Haque
An information-theoretic lower bound in time-uniform estimation. CoRR (2024)
Felipe Areces, Chen Cheng, John C. Duchi, Kuditipudi Rohith
Two fundamental limits for uncertainty quantification in predictive inference. COLT (2024)
John C. Duchi, Suyash Gupta, Kuanhao Jiang, Pragya Sur
Predictive Inference in Multi-environment Scenarios. CoRR (2024)
Karan N. Chadha, Junye Chen, John C. Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar
Differentially Private Heavy Hitter Detection using Federated Analytics. SaTML (2024)
Karan N. Chadha, John C. Duchi, Rohith Kuditipudi
Resampling methods for Private Statistical Inference. CoRR (2024)
John C. Duchi, Saminul Haque
An information-theoretic lower bound in time-uniform estimation. COLT (2024)
Hilal Asi, John C. Duchi, Saminul Haque, Zewei Li, Feng Ruan
Universally Instance-Optimal Mechanisms for Private Statistical Estimation. COLT (2024)
Rohith Kuditipudi, John C. Duchi, Saminul Haque
A Pretty Fast Algorithm for Adaptive Private Mean Estimation. COLT (2023)
Gary Cheng, Karan N. Chadha, John C. Duchi
Federated Asymptotics: a model to compare federated learning algorithms. AISTATS (2023)
Chen Cheng, Gary Cheng, John C. Duchi
Collaboratively Learning Linear Models with Structured Missing Data. CoRR (2023)
John C. Duchi, Tatsunori Hashimoto, Hongseok Namkoong
Distributionally Robust Losses for Latent Covariate Mixtures. Oper. Res. 71 (2) (2023)
John C. Duchi, Saminul Haque, Rohith Kuditipudi
A Fast Algorithm for Adaptive Private Mean Estimation. CoRR (2023)
Chen Cheng, Gary Cheng, John C. Duchi
Collaboratively Learning Linear Models with Structured Missing Data. NeurIPS (2023)
Karan N. Chadha, Junye Chen, John C. Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar
Differentially Private Heavy Hitter Detection using Federated Analytics. CoRR (2023)
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth
Lower bounds for non-convex stochastic optimization. Math. Program. 199 (1) (2023)
Anastasios N. Angelopoulos, John C. Duchi, Tijana Zrnic
PPI++: Efficient Prediction-Powered Inference. CoRR (2023)
Karan N. Chadha, Gary Cheng, John C. Duchi
Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization. ICML (2022)
Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi
Predictive Inference with Weak Supervision. CoRR (2022)
Alon Kipnis, John C. Duchi
Mean Estimation From One-Bit Measurements. IEEE Trans. Inf. Theory 68 (9) (2022)
John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. NeurIPS (2022)
Audra McMillan, Omid Javidbakht, Kunal Talwar, Elliot Briggs, Mike Chatzidakis, Junye Chen, John C. Duchi, Vitaly Feldman, Yusuf Goren, Michael Hesse, Vojta Jina, Anil Katti, Albert Liu, Cheney Lyford, Joey Meyer, Alex Palmer, David Park, Wonhee Park, Gianni Parsa, Paul Pelzl, Rehan Rishi, Congzheng Song, Shan Wang, Shundong Zhou
Private Federated Statistics in an Interactive Setting. CoRR (2022)
Maxime Cauchois, John C. Duchi
Query-Adaptive Predictive Inference with Partial Labels. CoRR (2022)
Karan N. Chadha, Gary Cheng, John C. Duchi
Accelerated, Optimal, and Parallel: Some Results on Model-Based Stochastic Optimization. CoRR (2021)
Annie Marsden, John C. Duchi, Gregory Valiant
On Misspecification in Prediction Problems and Robustness via Improper Learning. CoRR (2021)
Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford
Lower bounds for finding stationary points II: first-order methods. Math. Program. 185 (1-2) (2021)
Hilal Asi, John C. Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar
Private Adaptive Gradient Methods for Convex Optimization. ICML (2021)
Maxime Cauchois, Suyash Gupta, John C. Duchi
Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction. J. Mach. Learn. Res. 22 (2021)
John C. Duchi, Feng Ruan
A constrained risk inequality for general losses. AISTATS (2021)
Hilal Asi, Daniel Levy, John C. Duchi
Adapting to function difficulty and growth conditions in private optimization. NeurIPS (2021)
Annie Marsden, John C. Duchi, Gregory Valiant
Misspecification in Prediction Problems and Robustness via Improper Learning. AISTATS (2021)
John C. Duchi, Peter W. Glynn, Hongseok Namkoong
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach. Math. Oper. Res. 46 (3) (2021)
Hilal Asi, John C. Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar
Private Adaptive Gradient Methods for Convex Optimization. CoRR (2021)
Hilal Asi, Daniel Levy, John C. Duchi
Adapting to Function Difficulty and Growth Conditions in Private Optimization. CoRR (2021)
Gary Cheng, Karan N. Chadha, John C. Duchi
Fine-tuning is Fine in Federated Learning. CoRR (2021)
Devavrat Shah, Guy Bresler, John C. Duchi, Po-Ling Loh, Yihong Wu, Christina Lee Yu
Editorial. IEEE J. Sel. Areas Inf. Theory 1 (3) (2020)
John C. Duchi, Oliver Hinder, Andrew Naber, Yinyu Ye
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices. NeurIPS (2020)
Hilal Asi, John C. Duchi
Near Instance-Optimality in Differential Privacy. CoRR (2020)
John C. Duchi, Tatsunori Hashimoto, Hongseok Namkoong
Distributionally Robust Losses for Latent Covariate Mixtures. CoRR (2020)
Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi
Robust Validation: Confident Predictions Even When Distributions Shift. CoRR (2020)
Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford
Large-Scale Methods for Distributionally Robust Optimization. CoRR (2020)
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang
Understanding and Mitigating the Tradeoff between Robustness and Accuracy. ICML (2020)
Maxime Cauchois, Suyash Gupta, John C. Duchi
Knowing what you know: valid confidence sets in multiclass and multilabel prediction. CoRR (2020)
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy. CoRR (2020)
Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford
Lower bounds for finding stationary points I. Math. Program. 184 (1) (2020)
Aman Sinha, Matthew O'Kelly, John C. Duchi, Russ Tedrake
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems. CoRR (2020)
Hilal Asi, Karan N. Chadha, Gary Cheng, John C. Duchi
Minibatch Stochastic Approximate Proximal Point Methods. NeurIPS (2020)
Yair Carmon, John C. Duchi
First-Order Methods for Nonconvex Quadratic Minimization. SIAM Rev. 62 (2) (2020)
Aman Sinha, Matthew O'Kelly, Hongrui Zheng, Rahul Mangharam, John C. Duchi, Russ Tedrake
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis. CoRR (2020)
Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford
Large-Scale Methods for Distributionally Robust Optimization. NeurIPS (2020)
Aman Sinha, Matthew O'Kelly, Hongrui Zheng, Rahul Mangharam, John C. Duchi, Russ Tedrake
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis. ICML (2020)
Aman Sinha, Matthew O'Kelly, Russ Tedrake, John C. Duchi
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems. NeurIPS (2020)
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. COLT (2020)
Hilal Asi, John C. Duchi
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms. NeurIPS (2020)
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. CoRR (2020)
Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian
A Rank-1 Sketch for Matrix Multiplicative Weights. COLT (2019)
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi
Unlabeled Data Improves Adversarial Robustness. CoRR (2019)
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth
Lower Bounds for Non-Convex Stochastic Optimization. CoRR (2019)
John C. Duchi, Ryan Rogers
Lower Bounds for Locally Private Estimation via Communication Complexity. COLT (2019)
Hilal Asi, John C. Duchi
Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity. SIAM J. Optim. 29 (3) (2019)
Alon Kipnis, John C. Duchi
Mean Estimation from One-Bit Measurements. CoRR (2019)
Hilal Asi, John C. Duchi
Modeling simple structures and geometry for better stochastic optimization algorithms. AISTATS (2019)
Yair Carmon, John C. Duchi
Gradient Descent Finds the Cubic-Regularized Nonconvex Newton Step. SIAM J. Optim. 29 (3) (2019)
Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian
A Rank-1 Sketch for Matrix Multiplicative Weights. CoRR (2019)
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang
Adversarial Training Can Hurt Generalization. CoRR (2019)
John C. Duchi, Hongseok Namkoong
Variance-based Regularization with Convex Objectives. J. Mach. Learn. Res. 20 (2019)
Daniel Levy, John C. Duchi
Necessary and Sufficient Geometries for Gradient Methods. NeurIPS (2019)
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang
Unlabeled Data Improves Adversarial Robustness. NeurIPS (2019)
Daniel Levy, John C. Duchi
Necessary and Sufficient Conditions for Adaptive, Mirror, and Standard Gradient Methods. CoRR (2019)
Yu Bai, John C. Duchi, Song Mei
Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs. CoRR (2019)
Hilal Asi, John C. Duchi, Omid Javidbakht
Element Level Differential Privacy: The Right Granularity of Privacy. CoRR (2019)
John C. Duchi, Feng Ruan
The Right Complexity Measure in Locally Private Estimation: It is not the Fisher Information. CoRR (2018)
Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, Russ Tedrake, John C. Duchi
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation. NeurIPS (2018)
Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford
Accelerated Methods for NonConvex Optimization. SIAM J. Optim. 28 (2) (2018)
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese
Generalizing to Unseen Domains via Adversarial Data Augmentation. NeurIPS (2018)
John C. Duchi, Feng Ruan, Chulhee Yun
Minimax Bounds on Stochastic Batched Convex Optimization. COLT (2018)
Tatsunori B. Hashimoto, Steve Yadlowsky, John C. Duchi
Derivative free optimization via repeated classification. CoRR (2018)
Aman Sinha, Hongseok Namkoong, John C. Duchi
Certifying Some Distributional Robustness with Principled Adversarial Training. ICLR (2018)
Tatsunori Hashimoto, Steve Yadlowsky, John C. Duchi
Derivative Free Optimization Via Repeated Classification. AISTATS (2018)
Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, John C. Duchi, Russ Tedrake
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation. CoRR (2018)
John C. Duchi, Hongseok Namkoong
Learning Models with Uniform Performance via Distributionally Robust Optimization. CoRR (2018)
Yair Carmon, John C. Duchi
Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems. NeurIPS (2018)
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese
Generalizing to Unseen Domains via Adversarial Data Augmentation. CoRR (2018)
Abhishek Bhowmick, John C. Duchi, Julien Freudiger, Gaurav Kapoor, Ryan Rogers
Protection Against Reconstruction and Its Applications in Private Federated Learning. CoRR (2018)
John C. Duchi, Feng Ruan
Stochastic Methods for Composite and Weakly Convex Optimization Problems. SIAM J. Optim. 28 (4) (2018)
Aman Sinha, Hongseok Namkoong, John C. Duchi
Certifiable Distributional Robustness with Principled Adversarial Training. CoRR (2017)
Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford
"Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions. ICML (2017)
Hongseok Namkoong, John C. Duchi
Variance-based Regularization with Convex Objectives. NIPS (2017)
Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John C. Duchi
Adaptive Sampling Probabilities for Non-Smooth Optimization. ICML (2017)
John C. Duchi, Feng Ruan
Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval. CoRR (2017)
Tatsunori B. Hashimoto, Percy Liang, John C. Duchi
Unsupervised Transformation Learning via Convex Relaxations. NIPS (2017)
Alon Kipnis, John C. Duchi
Mean estimation from adaptive one-bit measurements. Allerton (2017)
Hongseok Namkoong, John C. Duchi
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences. NIPS (2016)
Yair Carmon, John C. Duchi
Gradient Descent Efficiently Finds the Cubic-Regularized Non-Convex Newton Step. CoRR (2016)
Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford
Accelerated Methods for Non-Convex Optimization. CoRR (2016)
Sabyasachi Chatterjee, John C. Duchi, John D. Lafferty, Yuancheng Zhu
Local Minimax Complexity of Stochastic Convex Optimization. NIPS (2016)
Aman Sinha, John C. Duchi
Learning Kernels with Random Features. NIPS (2016)
John C. Duchi, Martin J. Wainwright, Michael I. Jordan
Minimax Optimal Procedures for Locally Private Estimation. CoRR (2016)
Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang
Estimation from Indirect Supervision with Linear Moments. CoRR (2016)
John C. Duchi, Khashayar Khosravi, Feng Ruan
Information Measures, Experiments, Multi-category Hypothesis Tests, and Surrogate Losses. CoRR (2016)
Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang
Estimation from Indirect Supervision with Linear Moments. ICML (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

John C. Duchi