John C. Duchi

According to our database, John C. Duchi authored at least 121 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Predictive Inference in Multi-environment Scenarios.

John C. Duchi, Suyash Gupta, Kuanhao Jiang, Pragya Sur

CoRR, 2024

An information-theoretic lower bound in time-uniform estimation.

John C. Duchi, Saminul Haque

CoRR, 2024

Resampling methods for Private Statistical Inference.

Karan N. Chadha, John C. Duchi, Rohith Kuditipudi

CoRR, 2024

2023

Lower bounds for non-convex stochastic optimization.

Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth

Math. Program., May, 2023

Distributionally Robust Losses for Latent Covariate Mixtures.

John C. Duchi, Tatsunori Hashimoto, Hongseok Namkoong

Oper. Res., March, 2023

PPI++: Efficient Prediction-Powered Inference.

Anastasios N. Angelopoulos, John C. Duchi, Tijana Zrnic

CoRR, 2023

Differentially Private Heavy Hitter Detection using Federated Analytics.

Karan N. Chadha, Junye Chen, John C. Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar

CoRR, 2023

A Fast Algorithm for Adaptive Private Mean Estimation.

John C. Duchi, Saminul Haque, Rohith Kuditipudi

CoRR, 2023

Collaboratively Learning Linear Models with Structured Missing Data.

Chen Cheng, Gary Cheng, John C. Duchi

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Pretty Fast Algorithm for Adaptive Private Mean Estimation.

Rohith Kuditipudi, John C. Duchi, Saminul Haque

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Federated Asymptotics: a model to compare federated learning algorithms.

Gary Cheng, Karan N. Chadha, John C. Duchi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Mean Estimation From One-Bit Measurements.

Alon Kipnis, John C. Duchi

IEEE Trans. Inf. Theory, 2022

Private Federated Statistics in an Interactive Setting.

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

CoRR, 2022

How many labelers do you have? A closer look at gold-standard labels.

Chen Cheng, Hilal Asi, John C. Duchi

CoRR, 2022

Query-Adaptive Predictive Inference with Partial Labels.

Maxime Cauchois, John C. Duchi

CoRR, 2022

Predictive Inference with Weak Supervision.

Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi

CoRR, 2022

Subspace Recovery from Heterogeneous Data with Non-isotropic Noise.

John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization.

Karan N. Chadha, Gary Cheng, John C. Duchi

Proceedings of the International Conference on Machine Learning, 2022

Private optimization in the interpolation regime: faster rates and hardness results.

Hilal Asi, Karan N. Chadha, Gary Cheng, John C. Duchi

Proceedings of the International Conference on Machine Learning, 2022

Memorize to generalize: on the necessity of interpolation in high dimensional linear regression.

Chen Cheng, John C. Duchi, Rohith Kuditipudi

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Lower bounds for finding stationary points II: first-order methods.

Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford

Math. Program., 2021

Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach.

John C. Duchi, Peter W. Glynn, Hongseok Namkoong

Math. Oper. Res., 2021

Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction.

Maxime Cauchois, Suyash Gupta, John C. Duchi

J. Mach. Learn. Res., 2021

Fine-tuning is Fine in Federated Learning.

Gary Cheng, Karan N. Chadha, John C. Duchi

CoRR, 2021

Adapting to function difficulty and growth conditions in private optimization.

Hilal Asi, Daniel Levy, John C. Duchi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Private Adaptive Gradient Methods for Convex Optimization.

Hilal Asi, John C. Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar

Proceedings of the 38th International Conference on Machine Learning, 2021

Misspecification in Prediction Problems and Robustness via Improper Learning.

Annie Marsden, John C. Duchi, Gregory Valiant

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

A constrained risk inequality for general losses.

John C. Duchi, Feng Ruan

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

First-Order Methods for Nonconvex Quadratic Minimization.

Yair Carmon, John C. Duchi

SIAM Rev., 2020

Lower bounds for finding stationary points I.

Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford

Math. Program., 2020

Editorial.

Devavrat Shah, Guy Bresler, John C. Duchi, Po-Ling Loh, Yihong Wu, Christina Lee Yu

IEEE J. Sel. Areas Inf. Theory, 2020

Robust Validation: Confident Predictions Even When Distributions Shift.

Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi

CoRR, 2020

Near Instance-Optimality in Differential Privacy.

Hilal Asi, John C. Duchi

CoRR, 2020

Knowing what you know: valid confidence sets in multiclass and multilabel prediction.

Maxime Cauchois, Suyash Gupta, John C. Duchi

CoRR, 2020

Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems.

Aman Sinha, Matthew O'Kelly, Russ Tedrake, John C. Duchi

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Large-Scale Methods for Distributionally Robust Optimization.

Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices.

John C. Duchi, Oliver Hinder, Andrew Naber, Yinyu Ye

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms.

Hilal Asi, John C. Duchi

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Minibatch Stochastic Approximate Proximal Point Methods.

Hilal Asi, Karan N. Chadha, Gary Cheng, John C. Duchi

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis.

Aman Sinha, Matthew O'Kelly, Hongrui Zheng, Rahul Mangharam, John C. Duchi, Russ Tedrake

Proceedings of the 37th International Conference on Machine Learning, 2020

Understanding and Mitigating the Tradeoff between Robustness and Accuracy.

Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang

Proceedings of the 37th International Conference on Machine Learning, 2020

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

Proceedings of the Conference on Learning Theory, 2020

2019

Gradient Descent Finds the Cubic-Regularized Nonconvex Newton Step.

Yair Carmon, John C. Duchi

SIAM J. Optim., 2019

Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity.

Hilal Asi, John C. Duchi

SIAM J. Optim., 2019

Variance-based Regularization with Convex Objectives.

John C. Duchi, Hongseok Namkoong

J. Mach. Learn. Res., 2019

Element Level Differential Privacy: The Right Granularity of Privacy.

Hilal Asi, John C. Duchi, Omid Javidbakht

CoRR, 2019

Necessary and Sufficient Conditions for Adaptive, Mirror, and Standard Gradient Methods.

Daniel Levy, John C. Duchi

CoRR, 2019

Adversarial Training Can Hurt Generalization.

Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang

CoRR, 2019

Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs.

Yu Bai, John C. Duchi, Song Mei

CoRR, 2019

Necessary and Sufficient Geometries for Gradient Methods.

Daniel Levy, John C. Duchi

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Unlabeled Data Improves Adversarial Robustness.

Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Lower Bounds for Locally Private Estimation via Communication Complexity.

John C. Duchi, Ryan Rogers

Proceedings of the Conference on Learning Theory, 2019

A Rank-1 Sketch for Matrix Multiplicative Weights.

Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian

Proceedings of the Conference on Learning Theory, 2019

Modeling simple structures and geometry for better stochastic optimization algorithms.

Hilal Asi, John C. Duchi

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Stochastic Methods for Composite and Weakly Convex Optimization Problems.

John C. Duchi, Feng Ruan

SIAM J. Optim., 2018

Accelerated Methods for NonConvex Optimization.

Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford

SIAM J. Optim., 2018

Protection Against Reconstruction and Its Applications in Private Federated Learning.

Abhishek Bhowmick, John C. Duchi, Julien Freudiger, Gaurav Kapoor, Ryan Rogers

CoRR, 2018

Learning Models with Uniform Performance via Distributionally Robust Optimization.

John C. Duchi, Hongseok Namkoong

CoRR, 2018

The Right Complexity Measure in Locally Private Estimation: It is not the Fisher Information.

John C. Duchi, Feng Ruan

CoRR, 2018

Generalizing to Unseen Domains via Adversarial Data Augmentation.

Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation.

Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, Russ Tedrake, John C. Duchi

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems.

Yair Carmon, John C. Duchi

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Certifying Some Distributional Robustness with Principled Adversarial Training.

Aman Sinha, Hongseok Namkoong, John C. Duchi

Proceedings of the 6th International Conference on Learning Representations, 2018

Minimax Bounds on Stochastic Batched Convex Optimization.

John C. Duchi, Feng Ruan, Chulhee Yun

Proceedings of the Conference On Learning Theory, 2018

Derivative Free Optimization Via Repeated Classification.

Tatsunori Hashimoto, Steve Yadlowsky, John C. Duchi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Certifiable Distributional Robustness with Principled Adversarial Training.

Aman Sinha, Hongseok Namkoong, John C. Duchi

CoRR, 2017

Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval.

John C. Duchi, Feng Ruan

CoRR, 2017

Unsupervised Transformation Learning via Convex Relaxations.

Tatsunori B. Hashimoto, Percy Liang, John C. Duchi

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Adaptive Sampling Probabilities for Non-Smooth Optimization.

Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John C. Duchi

Proceedings of the 34th International Conference on Machine Learning, 2017

"Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions.

Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford

Proceedings of the 34th International Conference on Machine Learning, 2017

Mean estimation from adaptive one-bit measurements.

Alon Kipnis, John C. Duchi

Proceedings of the 55th Annual Allerton Conference on Communication, 2017

2016

Minimax Optimal Procedures for Locally Private Estimation.

John C. Duchi, Martin J. Wainwright, Michael I. Jordan

CoRR, 2016

Information Measures, Experiments, Multi-category Hypothesis Tests, and Surrogate Losses.

John C. Duchi, Khashayar Khosravi, Feng Ruan

CoRR, 2016

Accelerated Methods for Non-Convex Optimization.

Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford

CoRR, 2016

Gradient Descent Efficiently Finds the Cubic-Regularized Non-Convex Newton Step.

Yair Carmon, John C. Duchi

CoRR, 2016

Learning Kernels with Random Features.

Aman Sinha, John C. Duchi

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences.

Hongseok Namkoong, John C. Duchi

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Local Minimax Complexity of Stochastic Convex Optimization.

Sabyasachi Chatterjee, John C. Duchi, John D. Lafferty, Yuancheng Zhu

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Estimation from Indirect Supervision with Linear Moments.

Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations.

John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Andre Wibisono

IEEE Trans. Inf. Theory, 2015

Divide and conquer kernel ridge regression: a distributed algorithm with minimax optimal rates.

Yuchen Zhang, John C. Duchi, Martin J. Wainwright

J. Mach. Learn. Res., 2015

Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care.

Sorathan Chaturapruek, John C. Duchi, Christopher Ré

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Minimax rates for memory-bounded sparse linear regression.

Jacob Steinhardt, John C. Duchi

Proceedings of The 28th Conference on Learning Theory, 2015

Dynamic management of network risk from epidemic phenomena.

Aman Sinha, John C. Duchi, Nicholas Bambos

Proceedings of the 54th IEEE Conference on Decision and Control, 2015

2014

Multiple Optimality Guarantees in Statistical Learning.

John C. Duchi

PhD thesis, 2014

Privacy Aware Learning.

John C. Duchi, Michael I. Jordan, Martin J. Wainwright

J. ACM, 2014

Privacy and Statistical Risk: Formalisms and Minimax Bounds.

Rina Foygel Barber, John C. Duchi

CoRR, 2014

Privacy: A few definitional aspects and consequences for minimax mean-squared error.

Rina Foygel Barber, John C. Duchi

Proceedings of the 53rd IEEE Conference on Decision and Control, 2014

2013

The Generalization Ability of Online Algorithms for Dependent Data.

Alekh Agarwal, John C. Duchi

IEEE Trans. Inf. Theory, 2013

Communication-efficient algorithms for statistical optimization.

Yuchen Zhang, John C. Duchi, Martin J. Wainwright

J. Mach. Learn. Res., 2013

Distance-based and continuum Fano inequalities with applications to statistical estimation.

John C. Duchi, Martin J. Wainwright

CoRR, 2013

Optimal rates for zero-order optimization: the power of two function evaluations.

John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Andre Wibisono

CoRR, 2013

Information-theoretic lower bounds for distributed statistical estimation with communication constraints.

Yuchen Zhang, John C. Duchi, Michael I. Jordan, Martin J. Wainwright

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation.

John C. Duchi, Martin J. Wainwright, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Estimation, Optimization, and Parallelism when Data is Sparse.

John C. Duchi, Michael I. Jordan, H. Brendan McMahan

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Local Privacy and Statistical Minimax Rates.

John C. Duchi, Michael I. Jordan, Martin J. Wainwright

Proceedings of the 54th Annual IEEE Symposium on Foundations of Computer Science, 2013

Divide and Conquer Kernel Ridge Regression.

Yuchen Zhang, John C. Duchi, Martin J. Wainwright

Proceedings of the COLT 2013, 2013

MLbase: A Distributed Machine-learning System.

Tim Kraska, Ameet Talwalkar, John C. Duchi, Rean Griffith, Michael J. Franklin, Michael I. Jordan

Proceedings of the Sixth Biennial Conference on Innovative Data Systems Research, 2013

2012

Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling.

John C. Duchi, Alekh Agarwal, Martin J. Wainwright

IEEE Trans. Autom. Control., 2012

Randomized Smoothing for Stochastic Optimization.

John C. Duchi, Peter L. Bartlett, Martin J. Wainwright

SIAM J. Optim., 2012

Ergodic Mirror Descent.

John C. Duchi, Alekh Agarwal, Mikael Johansson, Michael I. Jordan

SIAM J. Optim., 2012

Commentary on "Toward a Noncommutative Arithmetic-geometric Mean Inequality: Conjectures, Case-studies, and Consequences".

John C. Duchi

Proceedings of the COLT 2012, 2012

Comunication-Efficient Algorithms for Statistical Optimization

Yuchen Zhang, John C. Duchi, Martin J. Wainwright

CoRR, 2012

Oracle inequalities for computationally adaptive model selection

Alekh Agarwal, Peter L. Bartlett, John C. Duchi

CoRR, 2012

The Asymptotics of Ranking Algorithms

John C. Duchi, Lester W. Mackey, Michael I. Jordan

CoRR, 2012

Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods.

John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Andre Wibisono

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Randomized smoothing for (parallel) stochastic optimization.

John C. Duchi, Peter L. Bartlett, Martin J. Wainwright

Proceedings of the 51th IEEE Conference on Decision and Control, 2012

Distributed delayed stochastic optimization.

Alekh Agarwal, John C. Duchi

Proceedings of the 51th IEEE Conference on Decision and Control, 2012

Dual averaging for distributed optimization.

John C. Duchi, Alekh Agarwal, Martin J. Wainwright

Proceedings of the 50th Annual Allerton Conference on Communication, 2012

2011

Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.

John C. Duchi, Elad Hazan, Yoram Singer

J. Mach. Learn. Res., 2011

Oracle inequalities for computationally budgeted model selection.

Alekh Agarwal, John C. Duchi, Peter L. Bartlett, Clément Levrard

Proceedings of the COLT 2011, 2011

2010

Distributed Dual Averaging In Networks.

John C. Duchi, Alekh Agarwal, Martin J. Wainwright

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

On the Consistency of Ranking Algorithms.

John C. Duchi, Lester W. Mackey, Michael I. Jordan

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Composite Objective Mirror Descent.

John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Ambuj Tewari

Proceedings of the COLT 2010, 2010

2009

Efficient Online and Batch Learning Using Forward Backward Splitting.

John C. Duchi, Yoram Singer

J. Mach. Learn. Res., 2009

Efficient Learning using Forward-Backward Splitting.

John C. Duchi, Yoram Singer

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Boosting with structural sparsity.

John C. Duchi, Yoram Singer

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

Constrained Approximate Maximum Entropy Learning of Markov Random Fields.

Varun Ganapathi, David Vickrey, John C. Duchi, Daphne Koller

Proceedings of the UAI 2008, 2008

Projected Subgradient Methods for Learning Sparse Gaussians.

John C. Duchi, Stephen Gould, Daphne Koller

Proceedings of the UAI 2008, 2008

Efficient projections onto the <i>l</i>₁-ball for learning in high dimensions.

John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Tushar Chandra

Proceedings of the Machine Learning, 2008

2006

Using Combinatorial Optimization within Max-Product Belief Propagation.

John C. Duchi, Daniel Tarlow, Gal Elidan, Daphne Koller

Proceedings of the Advances in Neural Information Processing Systems 19, 2006