John C. Duchi - RankMe

170 papers A* 41A 8Journal 93Unranked 27

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Prior Diffusiveness and Regret in the Linear-Gaussian Bandit. CoRR Yifan Zhu, John C. Duchi, Benjamin Van Roy
2025	J	`jnl`	A fast and slightly robust covariance estimator. CoRR John C. Duchi, Saminul Haque, Rohith Kuditipudi
2025	J	`jnl`	Distribution free M-estimation. CoRR John C. Duchi
2025	J	`jnl`	On Privately Estimating a Single Parameter. CoRR Hilal Asi, John C. Duchi, Kunal Talwar
2025	A*	`conf`	Online Conformal Prediction via Online Optimization. ICML Felipe Areces, Christopher Mohri, Tatsunori Hashimoto, John C. Duchi
2025	J	`jnl`	Some Robustness Properties of Label Cleaning. CoRR Chen Cheng, John C. Duchi
2024	A*	`conf`	An information-theoretic lower bound in time-uniform estimation. COLT John C. Duchi, Saminul Haque
2024	J	`jnl`	An information-theoretic lower bound in time-uniform estimation. CoRR John C. Duchi, Saminul Haque
2024	—	`conf`	Differentially Private Heavy Hitter Detection using Federated Analytics. SaTML Karan N. Chadha, Junye Chen, John C. Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar
2024	J	`jnl`	Predictive Inference in Multi-environment Scenarios. CoRR John C. Duchi, Suyash Gupta, Kuanhao Jiang, Pragya Sur
2024	J	`jnl`	Predictive Inference with Weak Supervision. J. Mach. Learn. Res. Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi
2024	J	`jnl`	Resampling methods for Private Statistical Inference. CoRR Karan N. Chadha, John C. Duchi, Rohith Kuditipudi
2024	A*	`conf`	Two fundamental limits for uncertainty quantification in predictive inference. COLT Felipe Areces, Chen Cheng, John C. Duchi, Kuditipudi Rohith
2024	A*	`conf`	Universally Instance-Optimal Mechanisms for Private Statistical Estimation. COLT Hilal Asi, John C. Duchi, Saminul Haque, Zewei Li, Feng Ruan
2023	J	`jnl`	A Fast Algorithm for Adaptive Private Mean Estimation. CoRR John C. Duchi, Saminul Haque, Rohith Kuditipudi
2023	A*	`conf`	A Pretty Fast Algorithm for Adaptive Private Mean Estimation. COLT Rohith Kuditipudi, John C. Duchi, Saminul Haque
2023	A*	`conf`	Collaboratively Learning Linear Models with Structured Missing Data. NeurIPS Chen Cheng, Gary Cheng, John C. Duchi
2023	J	`jnl`	Collaboratively Learning Linear Models with Structured Missing Data. CoRR Chen Cheng, Gary Cheng, John C. Duchi
2023	J	`jnl`	Differentially Private Heavy Hitter Detection using Federated Analytics. CoRR Karan N. Chadha, Junye Chen, John C. Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar
2023	J	`jnl`	Distributionally Robust Losses for Latent Covariate Mixtures. Oper. Res. John C. Duchi, Tatsunori Hashimoto, Hongseok Namkoong
2023	A	`conf`	Federated Asymptotics: a model to compare federated learning algorithms. AISTATS Gary Cheng, Karan N. Chadha, John C. Duchi
2023	J	`jnl`	Lower bounds for non-convex stochastic optimization. Math. Program. Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth
2023	J	`jnl`	PPI++: Efficient Prediction-Powered Inference. CoRR Anastasios N. Angelopoulos, John C. Duchi, Tijana Zrnic
2022	A*	`conf`	Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization. ICML Karan N. Chadha, Gary Cheng, John C. Duchi
2022	J	`jnl`	How many labelers do you have? A closer look at gold-standard labels. CoRR Chen Cheng, Hilal Asi, John C. Duchi
2022	J	`jnl`	Mean Estimation From One-Bit Measurements. IEEE Trans. Inf. Theory Alon Kipnis, John C. Duchi
2022	J	`jnl`	Memorize to Generalize: on the Necessity of Interpolation in High Dimensional Linear Regression. CoRR Chen Cheng, John C. Duchi, Rohith Kuditipudi
2022	A*	`conf`	Memorize to generalize: on the necessity of interpolation in high dimensional linear regression. COLT Chen Cheng, John C. Duchi, Rohith Kuditipudi
2022	J	`jnl`	Predictive Inference with Weak Supervision. CoRR Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi
2022	J	`jnl`	Private Federated Statistics in an Interactive Setting. CoRR 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
2022	A*	`conf`	Private optimization in the interpolation regime: faster rates and hardness results. ICML Hilal Asi, Karan N. Chadha, Gary Cheng, John C. Duchi
2022	J	`jnl`	Private optimization in the interpolation regime: faster rates and hardness results. CoRR Hilal Asi, Karan N. Chadha, Gary Cheng, John C. Duchi
2022	J	`jnl`	Query-Adaptive Predictive Inference with Partial Labels. CoRR Maxime Cauchois, John C. Duchi
2022	A*	`conf`	Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. NeurIPS John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar
2022	J	`jnl`	Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. CoRR John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar
2021	A	`conf`	A constrained risk inequality for general losses. AISTATS John C. Duchi, Feng Ruan
2021	J	`jnl`	Accelerated, Optimal, and Parallel: Some Results on Model-Based Stochastic Optimization. CoRR Karan N. Chadha, Gary Cheng, John C. Duchi
2021	J	`jnl`	Adapting to Function Difficulty and Growth Conditions in Private Optimization. CoRR Hilal Asi, Daniel Levy, John C. Duchi
2021	A*	`conf`	Adapting to function difficulty and growth conditions in private optimization. NeurIPS Hilal Asi, Daniel Levy, John C. Duchi
2021	J	`jnl`	Fine-tuning is Fine in Federated Learning. CoRR Gary Cheng, Karan N. Chadha, John C. Duchi
2021	J	`jnl`	Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction. J. Mach. Learn. Res. Maxime Cauchois, Suyash Gupta, John C. Duchi
2021	J	`jnl`	Lower bounds for finding stationary points II: first-order methods. Math. Program. Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford
2021	A	`conf`	Misspecification in Prediction Problems and Robustness via Improper Learning. AISTATS Annie Marsden, John C. Duchi, Gregory Valiant
2021	J	`jnl`	On Misspecification in Prediction Problems and Robustness via Improper Learning. CoRR Annie Marsden, John C. Duchi, Gregory Valiant
2021	A*	`conf`	Private Adaptive Gradient Methods for Convex Optimization. ICML Hilal Asi, John C. Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar
2021	J	`jnl`	Private Adaptive Gradient Methods for Convex Optimization. CoRR Hilal Asi, John C. Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar
2021	J	`jnl`	Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach. Math. Oper. Res. John C. Duchi, Peter W. Glynn, Hongseok Namkoong
2020	A*	`conf`	Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices. NeurIPS John C. Duchi, Oliver Hinder, Andrew Naber, Yinyu Ye
2020	J	`jnl`	Distributionally Robust Losses for Latent Covariate Mixtures. CoRR John C. Duchi, Tatsunori Hashimoto, Hongseok Namkoong
2020	J	`jnl`	Editorial. IEEE J. Sel. Areas Inf. Theory Devavrat Shah, Guy Bresler, John C. Duchi, Po-Ling Loh, Yihong Wu, Christina Lee Yu
2020	J	`jnl`	First-Order Methods for Nonconvex Quadratic Minimization. SIAM Rev. Yair Carmon, John C. Duchi
2020	A*	`conf`	FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis. ICML Aman Sinha, Matthew O'Kelly, Hongrui Zheng, Rahul Mangharam, John C. Duchi, Russ Tedrake
2020	J	`jnl`	FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis. CoRR Aman Sinha, Matthew O'Kelly, Hongrui Zheng, Rahul Mangharam, John C. Duchi, Russ Tedrake
2020	A*	`conf`	Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms. NeurIPS Hilal Asi, John C. Duchi
2020	J	`jnl`	Knowing what you know: valid confidence sets in multiclass and multilabel prediction. CoRR Maxime Cauchois, Suyash Gupta, John C. Duchi
2020	A*	`conf`	Large-Scale Methods for Distributionally Robust Optimization. NeurIPS Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford
2020	J	`jnl`	Large-Scale Methods for Distributionally Robust Optimization. CoRR Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford
2020	J	`jnl`	Lower bounds for finding stationary points I. Math. Program. Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford
2020	A*	`conf`	Minibatch Stochastic Approximate Proximal Point Methods. NeurIPS Hilal Asi, Karan N. Chadha, Gary Cheng, John C. Duchi
2020	J	`jnl`	Near Instance-Optimality in Differential Privacy. CoRR Hilal Asi, John C. Duchi
2020	A*	`conf`	Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems. NeurIPS Aman Sinha, Matthew O'Kelly, Russ Tedrake, John C. Duchi
2020	J	`jnl`	Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems. CoRR Aman Sinha, Matthew O'Kelly, John C. Duchi, Russ Tedrake
2020	J	`jnl`	Robust Validation: Confident Predictions Even When Distributions Shift. CoRR Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi
2020	A*	`conf`	Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. COLT Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
2020	J	`jnl`	Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. CoRR Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
2020	J	`jnl`	Understanding and Mitigating the Tradeoff Between Robustness and Accuracy. CoRR Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang
2020	A*	`conf`	Understanding and Mitigating the Tradeoff between Robustness and Accuracy. ICML Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang
2019	A*	`conf`	A Rank-1 Sketch for Matrix Multiplicative Weights. COLT Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian
2019	J	`jnl`	A Rank-1 Sketch for Matrix Multiplicative Weights. CoRR Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian
2019	J	`jnl`	Adversarial Training Can Hurt Generalization. CoRR Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang
2019	J	`jnl`	Element Level Differential Privacy: The Right Granularity of Privacy. CoRR Hilal Asi, John C. Duchi, Omid Javidbakht
2019	J	`jnl`	Gradient Descent Finds the Cubic-Regularized Nonconvex Newton Step. SIAM J. Optim. Yair Carmon, John C. Duchi
2019	A*	`conf`	Lower Bounds for Locally Private Estimation via Communication Complexity. COLT John C. Duchi, Ryan Rogers
2019	J	`jnl`	Lower Bounds for Non-Convex Stochastic Optimization. CoRR Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth
2019	J	`jnl`	Mean Estimation from One-Bit Measurements. CoRR Alon Kipnis, John C. Duchi
2019	A	`conf`	Modeling simple structures and geometry for better stochastic optimization algorithms. AISTATS Hilal Asi, John C. Duchi
2019	J	`jnl`	Necessary and Sufficient Conditions for Adaptive, Mirror, and Standard Gradient Methods. CoRR Daniel Levy, John C. Duchi
2019	A*	`conf`	Necessary and Sufficient Geometries for Gradient Methods. NeurIPS Daniel Levy, John C. Duchi
2019	J	`jnl`	Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs. CoRR Yu Bai, John C. Duchi, Song Mei
2019	J	`jnl`	Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity. SIAM J. Optim. Hilal Asi, John C. Duchi
2019	A*	`conf`	Unlabeled Data Improves Adversarial Robustness. NeurIPS Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang
2019	J	`jnl`	Unlabeled Data Improves Adversarial Robustness. CoRR Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi
2019	J	`jnl`	Variance-based Regularization with Convex Objectives. J. Mach. Learn. Res. John C. Duchi, Hongseok Namkoong
2018	J	`jnl`	Accelerated Methods for NonConvex Optimization. SIAM J. Optim. Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford
2018	A*	`conf`	Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems. NeurIPS Yair Carmon, John C. Duchi
2018	—	`conf`	Certifying Some Distributional Robustness with Principled Adversarial Training. ICLR Aman Sinha, Hongseok Namkoong, John C. Duchi
2018	A	`conf`	Derivative Free Optimization Via Repeated Classification. AISTATS Tatsunori Hashimoto, Steve Yadlowsky, John C. Duchi
2018	J	`jnl`	Derivative free optimization via repeated classification. CoRR Tatsunori B. Hashimoto, Steve Yadlowsky, John C. Duchi
2018	A*	`conf`	Generalizing to Unseen Domains via Adversarial Data Augmentation. NeurIPS Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese
2018	J	`jnl`	Generalizing to Unseen Domains via Adversarial Data Augmentation. CoRR Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese
2018	J	`jnl`	Learning Models with Uniform Performance via Distributionally Robust Optimization. CoRR John C. Duchi, Hongseok Namkoong
2018	A*	`conf`	Minimax Bounds on Stochastic Batched Convex Optimization. COLT John C. Duchi, Feng Ruan, Chulhee Yun
2018	J	`jnl`	Protection Against Reconstruction and Its Applications in Private Federated Learning. CoRR Abhishek Bhowmick, John C. Duchi, Julien Freudiger, Gaurav Kapoor, Ryan Rogers
2018	A*	`conf`	Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation. NeurIPS Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, Russ Tedrake, John C. Duchi
2018	J	`jnl`	Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation. CoRR Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, John C. Duchi, Russ Tedrake
2018	J	`jnl`	Stochastic Methods for Composite and Weakly Convex Optimization Problems. SIAM J. Optim. John C. Duchi, Feng Ruan
2018	J	`jnl`	The Right Complexity Measure in Locally Private Estimation: It is not the Fisher Information. CoRR John C. Duchi, Feng Ruan
2017	A*	`conf`	"Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions. ICML Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford
2017	A*	`conf`	Adaptive Sampling Probabilities for Non-Smooth Optimization. ICML Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John C. Duchi
2017	J	`jnl`	Certifiable Distributional Robustness with Principled Adversarial Training. CoRR Aman Sinha, Hongseok Namkoong, John C. Duchi
2017	—	`conf`	Mean estimation from adaptive one-bit measurements. Allerton Alon Kipnis, John C. Duchi
2017	J	`jnl`	Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval. CoRR John C. Duchi, Feng Ruan
2017	—	`conf`	Unsupervised Transformation Learning via Convex Relaxations. NIPS Tatsunori B. Hashimoto, Percy Liang, John C. Duchi
2017	—	`conf`	Variance-based Regularization with Convex Objectives. NIPS Hongseok Namkoong, John C. Duchi
2016	J	`jnl`	Accelerated Methods for Non-Convex Optimization. CoRR Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford
2016	A*	`conf`	Estimation from Indirect Supervision with Linear Moments. ICML Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang
2016	J	`jnl`	Estimation from Indirect Supervision with Linear Moments. CoRR Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang
2016	J	`jnl`	Gradient Descent Efficiently Finds the Cubic-Regularized Non-Convex Newton Step. CoRR Yair Carmon, John C. Duchi
2016	J	`jnl`	Information Measures, Experiments, Multi-category Hypothesis Tests, and Surrogate Losses. CoRR John C. Duchi, Khashayar Khosravi, Feng Ruan
2016	—	`conf`	Learning Kernels with Random Features. NIPS Aman Sinha, John C. Duchi
2016	—	`conf`	Local Minimax Complexity of Stochastic Convex Optimization. NIPS Sabyasachi Chatterjee, John C. Duchi, John D. Lafferty, Yuancheng Zhu
2016	J	`jnl`	Minimax Optimal Procedures for Locally Private Estimation. CoRR John C. Duchi, Martin J. Wainwright, Michael I. Jordan
2016	—	`conf`	Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences. NIPS Hongseok Namkoong, John C. Duchi
2015	—	`conf`	Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care. NIPS Sorathan Chaturapruek, John C. Duchi, Christopher Ré
2015	J	`jnl`	Divide and conquer kernel ridge regression: a distributed algorithm with minimax optimal rates. J. Mach. Learn. Res. Yuchen Zhang, John C. Duchi, Martin J. Wainwright
2015	—	`conf`	Dynamic management of network risk from epidemic phenomena. CDC Aman Sinha, John C. Duchi, Nicholas Bambos
2015	A*	`conf`	Minimax rates for memory-bounded sparse linear regression. COLT Jacob Steinhardt, John C. Duchi
2015	J	`jnl`	Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations. IEEE Trans. Inf. Theory John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Andre Wibisono
2014	J	`jnl`	Information-theoretic lower bounds for distributed statistical estimation with communication constraints. CoRR John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Yuchen Zhang
2014	—	`—`	Multiple Optimality Guarantees in Statistical Learning. John C. Duchi
2014	J	`jnl`	Privacy Aware Learning. J. ACM John C. Duchi, Michael I. Jordan, Martin J. Wainwright
2014	J	`jnl`	Privacy and Statistical Risk: Formalisms and Minimax Bounds. CoRR Rina Foygel Barber, John C. Duchi
2014	—	`conf`	Privacy: A few definitional aspects and consequences for minimax mean-squared error. CDC Rina Foygel Barber, John C. Duchi
2013	J	`jnl`	Communication-efficient algorithms for statistical optimization. J. Mach. Learn. Res. Yuchen Zhang, John C. Duchi, Martin J. Wainwright
2013	J	`jnl`	Distance-based and continuum Fano inequalities with applications to statistical estimation. CoRR John C. Duchi, Martin J. Wainwright
2013	A*	`conf`	Divide and Conquer Kernel Ridge Regression. COLT Yuchen Zhang, John C. Duchi, Martin J. Wainwright
2013	J	`jnl`	Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates CoRR Yuchen Zhang, John C. Duchi, Martin J. Wainwright
2013	—	`conf`	Estimation, Optimization, and Parallelism when Data is Sparse. NIPS John C. Duchi, Michael I. Jordan, H. Brendan McMahan
2013	—	`conf`	Information-theoretic lower bounds for distributed statistical estimation with communication constraints. NIPS Yuchen Zhang, John C. Duchi, Michael I. Jordan, Martin J. Wainwright
2013	J	`jnl`	Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation CoRR John C. Duchi, Michael I. Jordan, Martin J. Wainwright
2013	—	`conf`	Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation. NIPS John C. Duchi, Martin J. Wainwright, Michael I. Jordan
2013	J	`jnl`	Local Privacy and Statistical Minimax Rates CoRR John C. Duchi, Michael I. Jordan, Martin J. Wainwright
2013	A*	`conf`	Local Privacy and Statistical Minimax Rates. FOCS John C. Duchi, Michael I. Jordan, Martin J. Wainwright
2013	—	`conf`	Local privacy and statistical minimax rates. Allerton John C. Duchi, Michael I. Jordan, Martin J. Wainwright
2013	A	`conf`	MLbase: A Distributed Machine-learning System. CIDR Tim Kraska, Ameet Talwalkar, John C. Duchi, Rean Griffith, Michael J. Franklin, Michael I. Jordan
2013	J	`jnl`	Optimal rates for zero-order optimization: the power of two function evaluations. CoRR John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Andre Wibisono
2013	J	`jnl`	The Generalization Ability of Online Algorithms for Dependent Data. IEEE Trans. Inf. Theory Alekh Agarwal, John C. Duchi
2012	A*	`conf`	Commentary on "Toward a Noncommutative Arithmetic-geometric Mean Inequality: Conjectures, Case-studies, and Consequences". COLT John C. Duchi
2012	—	`conf`	Communication-Efficient Algorithms for Statistical Optimization. NIPS Yuchen Zhang, John C. Duchi, Martin J. Wainwright
2012	—	`conf`	Communication-efficient algorithms for statistical optimization. CDC Yuchen Zhang, John C. Duchi, Martin J. Wainwright
2012	J	`jnl`	Comunication-Efficient Algorithms for Statistical Optimization CoRR Yuchen Zhang, John C. Duchi, Martin J. Wainwright
2012	J	`jnl`	Constrained Approximate Maximum Entropy Learning of Markov Random Fields CoRR Varun Ganapathi, David Vickrey, John C. Duchi, Daphne Koller
2012	—	`conf`	Distributed delayed stochastic optimization. CDC Alekh Agarwal, John C. Duchi
2012	J	`jnl`	Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling. IEEE Trans. Autom. Control. John C. Duchi, Alekh Agarwal, Martin J. Wainwright
2012	—	`conf`	Dual averaging for distributed optimization. Allerton Conference John C. Duchi, Alekh Agarwal, Martin J. Wainwright
2012	J	`jnl`	Ergodic Mirror Descent. SIAM J. Optim. John C. Duchi, Alekh Agarwal, Mikael Johansson, Michael I. Jordan
2012	—	`conf`	Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods. NIPS John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Andre Wibisono
2012	J	`jnl`	Oracle inequalities for computationally adaptive model selection CoRR Alekh Agarwal, Peter L. Bartlett, John C. Duchi
2012	J	`jnl`	Privacy Aware Learning CoRR John C. Duchi, Michael I. Jordan, Martin J. Wainwright
2012	—	`conf`	Privacy Aware Learning. NIPS John C. Duchi, Michael I. Jordan, Martin J. Wainwright
2012	J	`jnl`	Projected Subgradient Methods for Learning Sparse Gaussians CoRR John C. Duchi, Stephen Gould, Daphne Koller
2012	J	`jnl`	Randomized Smoothing for Stochastic Optimization. SIAM J. Optim. John C. Duchi, Peter L. Bartlett, Martin J. Wainwright
2012	—	`conf`	Randomized smoothing for (parallel) stochastic optimization. CDC John C. Duchi, Peter L. Bartlett, Martin J. Wainwright
2012	J	`jnl`	The Asymptotics of Ranking Algorithms CoRR John C. Duchi, Lester W. Mackey, Michael I. Jordan
2011	J	`jnl`	Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. J. Mach. Learn. Res. John C. Duchi, Elad Hazan, Yoram Singer
2011	—	`conf`	Distributed Delayed Stochastic Optimization. NIPS Alekh Agarwal, John C. Duchi
2011	—	`conf`	Ergodic mirror descent. Allerton John C. Duchi, Alekh Agarwal, Mikael Johansson, Michael I. Jordan
2011	A*	`conf`	Oracle inequalities for computationally budgeted model selection. COLT Alekh Agarwal, John C. Duchi, Peter L. Bartlett, Clément Levrard
2011	J	`jnl`	The Generalization Ability of Online Algorithms for Dependent Data CoRR Alekh Agarwal, John C. Duchi
2010	A*	`conf`	Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. COLT John C. Duchi, Elad Hazan, Yoram Singer
2010	A*	`conf`	Composite Objective Mirror Descent. COLT John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Ambuj Tewari
2010	—	`conf`	Distributed Dual Averaging In Networks. NIPS John C. Duchi, Alekh Agarwal, Martin J. Wainwright
2010	A*	`conf`	On the Consistency of Ranking Algorithms. ICML John C. Duchi, Lester W. Mackey, Michael I. Jordan
2009	A*	`conf`	Boosting with structural sparsity. ICML John C. Duchi, Yoram Singer
2009	—	`conf`	Efficient Learning using Forward-Backward Splitting. NIPS John C. Duchi, Yoram Singer
2009	J	`jnl`	Efficient Online and Batch Learning Using Forward Backward Splitting. J. Mach. Learn. Res. John C. Duchi, Yoram Singer
2008	A	`conf`	Constrained Approximate Maximum Entropy Learning of Markov Random Fields. UAI Varun Ganapathi, David Vickrey, John C. Duchi, Daphne Koller
2008	A*	`conf`	Efficient projections onto the ICML John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Tushar Chandra
2008	A	`conf`	Projected Subgradient Methods for Learning Sparse Gaussians. UAI John C. Duchi, Stephen Gould, Daphne Koller
2006	—	`conf`	Using Combinatorial Optimization within Max-Product Belief Propagation. NIPS John C. Duchi, Daniel Tarlow, Gal Elidan, Daphne Koller