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John C. Duchi
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- affiliation: Stanford University, USA
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2020 – today
- 2024
- [j25]Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi:
Predictive Inference with Weak Supervision. J. Mach. Learn. Res. 25: 118:1-118:45 (2024) - [c75]Felipe Areces, Chen Cheng, John C. Duchi, Kuditipudi Rohith:
Two fundamental limits for uncertainty quantification in predictive inference. COLT 2024: 186-218 - [c74]Hilal Asi, John C. Duchi, Saminul Haque, Zewei Li, Feng Ruan:
Universally Instance-Optimal Mechanisms for Private Statistical Estimation. COLT 2024: 221-259 - [c73]John C. Duchi, Saminul Haque:
An information-theoretic lower bound in time-uniform estimation. COLT 2024: 1486-1500 - [c72]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: 512-533 - [i63]Karan N. Chadha, John C. Duchi, Rohith Kuditipudi:
Resampling methods for Private Statistical Inference. CoRR abs/2402.07131 (2024) - [i62]John C. Duchi, Saminul Haque:
An information-theoretic lower bound in time-uniform estimation. CoRR abs/2402.08794 (2024) - [i61]John C. Duchi, Suyash Gupta, Kuanhao Jiang, Pragya Sur:
Predictive Inference in Multi-environment Scenarios. CoRR abs/2403.16336 (2024) - 2023
- [j24]John C. Duchi, Tatsunori Hashimoto, Hongseok Namkoong
:
Distributionally Robust Losses for Latent Covariate Mixtures. Oper. Res. 71(2): 649-664 (2023) - [j23]Yossi Arjevani, Yair Carmon
Lower bounds for non-convex stochastic optimization. Math. Program. 199(1): 165-214 (2023) - [c71]Gary Cheng, Karan N. Chadha, John C. Duchi:
Federated Asymptotics: a model to compare federated learning algorithms. AISTATS 2023: 10650-10689 - [c70]Rohith Kuditipudi, John C. Duchi, Saminul Haque:
A Pretty Fast Algorithm for Adaptive Private Mean Estimation. COLT 2023: 2511-2551 - [c69]Chen Cheng, Gary Cheng, John C. Duchi:
Collaboratively Learning Linear Models with Structured Missing Data. NeurIPS 2023 - [i60]John C. Duchi, Saminul Haque, Rohith Kuditipudi:
A Fast Algorithm for Adaptive Private Mean Estimation. CoRR abs/2301.07078 (2023) - [i59]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 abs/2307.11749 (2023) - [i58]Chen Cheng, Gary Cheng, John C. Duchi:
Collaboratively Learning Linear Models with Structured Missing Data. CoRR abs/2307.11947 (2023) - [i57]Anastasios N. Angelopoulos, John C. Duchi, Tijana Zrnic:
PPI++: Efficient Prediction-Powered Inference. CoRR abs/2311.01453 (2023) - 2022
- [j22]Alon Kipnis
Mean Estimation From One-Bit Measurements. IEEE Trans. Inf. Theory 68(9): 6276-6296 (2022) - [c68]Chen Cheng, John C. Duchi, Rohith Kuditipudi:
Memorize to generalize: on the necessity of interpolation in high dimensional linear regression. COLT 2022: 5528-5560 - [c67]Hilal Asi, Karan N. Chadha, Gary Cheng, John C. Duchi:
Private optimization in the interpolation regime: faster rates and hardness results. ICML 2022: 1025-1045 - [c66]Karan N. Chadha, Gary Cheng, John C. Duchi:
Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization. ICML 2022: 2811-2827 - [c65]John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar:
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. NeurIPS 2022 - [i56]Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi:
Predictive Inference with Weak Supervision. CoRR abs/2201.08315 (2022) - [i55]Chen Cheng, John C. Duchi, Rohith Kuditipudi:
Memorize to Generalize: on the Necessity of Interpolation in High Dimensional Linear Regression. CoRR abs/2202.09889 (2022) - [i54]Maxime Cauchois, John C. Duchi:
Query-Adaptive Predictive Inference with Partial Labels. CoRR abs/2206.07236 (2022) - [i53]Chen Cheng, Hilal Asi, John C. Duchi:
How many labelers do you have? A closer look at gold-standard labels. CoRR abs/2206.12041 (2022) - [i52]John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar:
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. CoRR abs/2210.13497 (2022) - [i51]Hilal Asi, Karan N. Chadha, Gary Cheng, John C. Duchi:
Private optimization in the interpolation regime: faster rates and hardness results. CoRR abs/2210.17070 (2022) - [i50]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 abs/2211.10082 (2022) - 2021
- [j21]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: 81:1-81:42 (2021) - [j20]John C. Duchi, Peter W. Glynn, Hongseok Namkoong
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach. Math. Oper. Res. 46(3): 946-969 (2021) - [j19]Yair Carmon
Lower bounds for finding stationary points II: first-order methods. Math. Program. 185(1-2): 315-355 (2021) - [c64]John C. Duchi, Feng Ruan:
A constrained risk inequality for general losses. AISTATS 2021: 802-810 - [c63]Annie Marsden, John C. Duchi, Gregory Valiant:
Misspecification in Prediction Problems and Robustness via Improper Learning. AISTATS 2021: 2161-2169 - [c62]Hilal Asi, John C. Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar:
Private Adaptive Gradient Methods for Convex Optimization. ICML 2021: 383-392 - [c61]Hilal Asi, Daniel Levy, John C. Duchi:
Adapting to function difficulty and growth conditions in private optimization. NeurIPS 2021: 19069-19081 - [i49]Karan N. Chadha, Gary Cheng, John C. Duchi:
Accelerated, Optimal, and Parallel: Some Results on Model-Based Stochastic Optimization. CoRR abs/2101.02696 (2021) - [i48]Annie Marsden, John C. Duchi, Gregory Valiant:
On Misspecification in Prediction Problems and Robustness via Improper Learning. CoRR abs/2101.05234 (2021) - [i47]Hilal Asi, John C. Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar:
Private Adaptive Gradient Methods for Convex Optimization. CoRR abs/2106.13756 (2021) - [i46]Hilal Asi, Daniel Levy, John C. Duchi:
Adapting to Function Difficulty and Growth Conditions in Private Optimization. CoRR abs/2108.02391 (2021) - [i45]Gary Cheng, Karan N. Chadha, John C. Duchi:
Fine-tuning is Fine in Federated Learning. CoRR abs/2108.07313 (2021) - 2020
- [j18]Devavrat Shah
Editorial. IEEE J. Sel. Areas Inf. Theory 1(3): 612 (2020) - [j17]Yair Carmon
Lower bounds for finding stationary points I. Math. Program. 184(1): 71-120 (2020) - [j16]Yair Carmon, John C. Duchi:
First-Order Methods for Nonconvex Quadratic Minimization. SIAM Rev. 62(2): 395-436 (2020) - [c60]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: 242-299 - [c59]Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang:
Understanding and Mitigating the Tradeoff between Robustness and Accuracy. ICML 2020: 7909-7919 - [c58]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: 8992-9004 - [c57]Hilal Asi, Karan N. Chadha, Gary Cheng, John C. Duchi:
Minibatch Stochastic Approximate Proximal Point Methods. NeurIPS 2020 - [c56]Hilal Asi, John C. Duchi:
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms. NeurIPS 2020 - [c55]John C. Duchi, Oliver Hinder, Andrew Naber, Yinyu Ye:
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices. NeurIPS 2020 - [c54]Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford:
Large-Scale Methods for Distributionally Robust Optimization. NeurIPS 2020 - [c53]Aman Sinha, Matthew O'Kelly, Russ Tedrake, John C. Duchi:
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems. NeurIPS 2020 - [i44]Aditi Raghunathan, Sang Michael Xie
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy. CoRR abs/2002.10716 (2020) - [i43]Aman Sinha, Matthew O'Kelly, Hongrui Zheng, Rahul Mangharam, John C. Duchi, Russ Tedrake:
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis. CoRR abs/2003.03900 (2020) - [i42]Maxime Cauchois, Suyash Gupta, John C. Duchi:
Knowing what you know: valid confidence sets in multiclass and multilabel prediction. CoRR abs/2004.10181 (2020) - [i41]Hilal Asi, John C. Duchi:
Near Instance-Optimality in Differential Privacy. CoRR abs/2005.10630 (2020) - [i40]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 abs/2006.13476 (2020) - [i39]John C. Duchi, Tatsunori Hashimoto, Hongseok Namkoong:
Distributionally Robust Losses for Latent Covariate Mixtures. CoRR abs/2007.13982 (2020) - [i38]Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi:
Robust Validation: Confident Predictions Even When Distributions Shift. CoRR abs/2008.04267 (2020) - [i37]Aman Sinha, Matthew O'Kelly, John C. Duchi, Russ Tedrake:
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems. CoRR abs/2008.10581 (2020) - [i36]Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford:
Large-Scale Methods for Distributionally Robust Optimization. CoRR abs/2010.05893 (2020)
2010 – 2019
- 2019
- [j15]John C. Duchi, Hongseok Namkoong:
Variance-based Regularization with Convex Objectives. J. Mach. Learn. Res. 20: 68:1-68:55 (2019) - [j14]Yair Carmon
Gradient Descent Finds the Cubic-Regularized Nonconvex Newton Step. SIAM J. Optim. 29(3): 2146-2178 (2019) - [j13]Hilal Asi, John C. Duchi:
Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity. SIAM J. Optim. 29(3): 2257-2290 (2019) - [c52]Hilal Asi, John C. Duchi:
Modeling simple structures and geometry for better stochastic optimization algorithms. AISTATS 2019: 2425-2434 - [c51]Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian:
A Rank-1 Sketch for Matrix Multiplicative Weights. COLT 2019: 589-623 - [c50]John C. Duchi, Ryan Rogers:
Lower Bounds for Locally Private Estimation via Communication Complexity. COLT 2019: 1161-1191 - [c49]Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang:
Unlabeled Data Improves Adversarial Robustness. NeurIPS 2019: 11190-11201 - [c48]Daniel Levy, John C. Duchi:
Necessary and Sufficient Geometries for Gradient Methods. NeurIPS 2019: 11491-11501 - [i35]Alon Kipnis, John C. Duchi:
Mean Estimation from One-Bit Measurements. CoRR abs/1901.03403 (2019) - [i34]Yu Bai, John C. Duchi, Song Mei:
Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs. CoRR abs/1903.00184 (2019) - [i33]Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian:
A Rank-1 Sketch for Matrix Multiplicative Weights. CoRR abs/1903.02675 (2019) - [i32]Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi:
Unlabeled Data Improves Adversarial Robustness. CoRR abs/1905.13736 (2019) - [i31]Aditi Raghunathan, Sang Michael Xie
Adversarial Training Can Hurt Generalization. CoRR abs/1906.06032 (2019) - [i30]Daniel Levy, John C. Duchi:
Necessary and Sufficient Conditions for Adaptive, Mirror, and Standard Gradient Methods. CoRR abs/1909.10455 (2019) - [i29]Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth:
Lower Bounds for Non-Convex Stochastic Optimization. CoRR abs/1912.02365 (2019) - [i28]Hilal Asi, John C. Duchi, Omid Javidbakht:
Element Level Differential Privacy: The Right Granularity of Privacy. CoRR abs/1912.04042 (2019) - 2018
- [j12]Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford:
Accelerated Methods for NonConvex Optimization. SIAM J. Optim. 28(2): 1751-1772 (2018) - [j11]John C. Duchi, Feng Ruan:
Stochastic Methods for Composite and Weakly Convex Optimization Problems. SIAM J. Optim. 28(4): 3229-3259 (2018) - [c47]Tatsunori Hashimoto, Steve Yadlowsky, John C. Duchi:
Derivative Free Optimization Via Repeated Classification. AISTATS 2018: 2027-2036 - [c46]John C. Duchi, Feng Ruan, Chulhee Yun:
Minimax Bounds on Stochastic Batched Convex Optimization. COLT 2018: 3065-3162 - [c45]Aman Sinha, Hongseok Namkoong, John C. Duchi:
Certifying Some Distributional Robustness with Principled Adversarial Training. ICLR 2018 - [c44]Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese:
Generalizing to Unseen Domains via Adversarial Data Augmentation. NeurIPS 2018: 5339-5349 - [c43]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: 9849-9860 - [c42]Yair Carmon, John C. Duchi:
Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems. NeurIPS 2018: 10728-10738 - [i27]Tatsunori B. Hashimoto, Steve Yadlowsky, John C. Duchi:
Derivative free optimization via repeated classification. CoRR abs/1804.03761 (2018) - [i26]Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese:
Generalizing to Unseen Domains via Adversarial Data Augmentation. CoRR abs/1805.12018 (2018) - [i25]John C. Duchi, Feng Ruan:
The Right Complexity Measure in Locally Private Estimation: It is not the Fisher Information. CoRR abs/1806.05756 (2018) - [i24]John C. Duchi, Hongseok Namkoong:
Learning Models with Uniform Performance via Distributionally Robust Optimization. CoRR abs/1810.08750 (2018) - [i23]Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, John C. Duchi, Russ Tedrake:
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation. CoRR abs/1811.00145 (2018) - [i22]Abhishek Bhowmick, John C. Duchi, Julien Freudiger, Gaurav Kapoor, Ryan Rogers:
Protection Against Reconstruction and Its Applications in Private Federated Learning. CoRR abs/1812.00984 (2018) - 2017
- [c41]Alon Kipnis
Mean estimation from adaptive one-bit measurements. Allerton 2017: 1000-1007 - [c40]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: 654-663 - [c39]Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John C. Duchi:
Adaptive Sampling Probabilities for Non-Smooth Optimization. ICML 2017: 2574-2583 - [c38]Hongseok Namkoong, John C. Duchi:
Variance-based Regularization with Convex Objectives. NIPS 2017: 2971-2980 - [c37]Tatsunori B. Hashimoto, Percy Liang, John C. Duchi:
Unsupervised Transformation Learning via Convex Relaxations. NIPS 2017: 6875-6883 - [i21]John C. Duchi, Feng Ruan:
Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval. CoRR abs/1705.02356 (2017) - [i20]Aman Sinha, Hongseok Namkoong, John C. Duchi:
Certifiable Distributional Robustness with Principled Adversarial Training. CoRR abs/1710.10571 (2017) - 2016
- [c36]Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang:
Estimation from Indirect Supervision with Linear Moments. ICML 2016: 2568-2577 - [c35]Aman Sinha, John C. Duchi:
Learning Kernels with Random Features. NIPS 2016: 1298-1306 - [c34]Hongseok Namkoong, John C. Duchi:
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences. NIPS 2016: 2208-2216 - [c33]Sabyasachi Chatterjee, John C. Duchi, John D. Lafferty, Yuancheng Zhu:
Local Minimax Complexity of Stochastic Convex Optimization. NIPS 2016: 3423-3431 - [i19]John C. Duchi, Khashayar Khosravi, Feng Ruan:
Information Measures, Experiments, Multi-category Hypothesis Tests, and Surrogate Losses. CoRR abs/1603.00126 (2016) - [i18]John C. Duchi, Martin J. Wainwright, Michael I. Jordan:
Minimax Optimal Procedures for Locally Private Estimation. CoRR abs/1604.02390 (2016) - [i17]Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang:
Estimation from Indirect Supervision with Linear Moments. CoRR abs/1608.03100 (2016) - [i16]Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford:
Accelerated Methods for Non-Convex Optimization. CoRR abs/1611.00756 (2016) - [i15]Yair Carmon, John C. Duchi:
Gradient Descent Efficiently Finds the Cubic-Regularized Non-Convex Newton Step. CoRR abs/1612.00547 (2016) - 2015
- [j10]Yuchen Zhang, John C. Duchi, Martin J. Wainwright:
Divide and conquer kernel ridge regression: a distributed algorithm with minimax optimal rates. J. Mach. Learn. Res. 16: 3299-3340 (2015) - [j9]John C. Duchi, Michael I. Jordan
Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations. IEEE Trans. Inf. Theory 61(5): 2788-2806 (2015) - [c32]Aman Sinha, John C. Duchi, Nicholas Bambos:
Dynamic management of network risk from epidemic phenomena. CDC 2015: 1583-1588 - [c31]Jacob Steinhardt, John C. Duchi:
Minimax rates for memory-bounded sparse linear regression. COLT 2015: 1564-1587 - [c30]Sorathan Chaturapruek, John C. Duchi, Christopher Ré:
Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care. NIPS 2015: 1531-1539 - 2014
- [b1]John C. Duchi:
Multiple Optimality Guarantees in Statistical Learning. University of California, Berkeley, USA, 2014 - [j8]John C. Duchi, Michael I. Jordan
Privacy Aware Learning. J. ACM 61(6): 38:1-38:57 (2014) - [c29]Rina Foygel Barber, John C. Duchi:
Privacy: A few definitional aspects and consequences for minimax mean-squared error. CDC 2014: 1365-1369 - [i14]John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Yuchen Zhang:
Information-theoretic lower bounds for distributed statistical estimation with communication constraints. CoRR abs/1405.0782 (2014) - [i13]Rina Foygel Barber, John C. Duchi:
Privacy and Statistical Risk: Formalisms and Minimax Bounds. CoRR abs/1412.4451 (2014) - 2013
- [j7]Yuchen Zhang, John C. Duchi, Martin J. Wainwright:
Communication-efficient algorithms for statistical optimization. J. Mach. Learn. Res. 14(1): 3321-3363 (2013) - [j6]Alekh Agarwal, John C. Duchi:
The Generalization Ability of Online Algorithms for Dependent Data. IEEE Trans. Inf. Theory 59(1): 573-587 (2013) - [c28]John C. Duchi, Michael I. Jordan
Local privacy and statistical minimax rates. Allerton 2013: 1592 - [c27]Tim Kraska, Ameet Talwalkar, John C. Duchi, Rean Griffith, Michael J. Franklin, Michael I. Jordan:
MLbase: A Distributed Machine-learning System. CIDR 2013 - [c26]Yuchen Zhang, John C. Duchi, Martin J. Wainwright:
Divide and Conquer Kernel Ridge Regression. COLT 2013: 592-617 - [c25]John C. Duchi, Michael I. Jordan
Local Privacy and Statistical Minimax Rates. FOCS 2013: 429-438 - [c24]John C. Duchi, Martin J. Wainwright, Michael I. Jordan:
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation. NIPS 2013: 1529-1537 - [c23]Yuchen Zhang, John C. Duchi, Michael I. Jordan, Martin J. Wainwright:
Information-theoretic lower bounds for distributed statistical estimation with communication constraints. NIPS 2013: 2328-2336 - [c22]John C. Duchi, Michael I. Jordan, H. Brendan McMahan:
Estimation, Optimization, and Parallelism when Data is Sparse. NIPS 2013: 2832-2840 - [i12]John C. Duchi, Michael I. Jordan, Martin J. Wainwright:
Local Privacy and Statistical Minimax Rates. CoRR abs/1302.3203 (2013) - [i11]Yuchen Zhang, John C. Duchi, Martin J. Wainwright:
Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates. CoRR abs/1305.5029 (2013) - [i10]John C. Duchi, Michael I. Jordan, Martin J. Wainwright:
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation. CoRR abs/1305.6000 (2013) - [i9]John C. Duchi, Martin J. Wainwright:
Distance-based and continuum Fano inequalities with applications to statistical estimation. CoRR abs/1311.2669 (2013) - [i8]John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Andre Wibisono:
Optimal rates for zero-order optimization: the power of two function evaluations. CoRR abs/1312.2139 (2013) - 2012
- [j5]John C. Duchi, Peter L. Bartlett
Randomized Smoothing for Stochastic Optimization. SIAM J. Optim. 22(2): 674-701 (2012) - [j4]John C. Duchi, Alekh Agarwal, Mikael Johansson, Michael I. Jordan
Ergodic Mirror Descent. SIAM J. Optim. 22(4): 1549-1578 (2012) - [j3]John C. Duchi, Alekh Agarwal, Martin J. Wainwright
Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling. IEEE Trans. Autom. Control. 57(3): 592-606 (2012) - [c21]John C. Duchi, Alekh Agarwal, Martin J. Wainwright
Dual averaging for distributed optimization. Allerton Conference 2012: 1564-1565 - [c20]John C. Duchi, Peter L. Bartlett
Randomized smoothing for (parallel) stochastic optimization. CDC 2012: 5442-5444 - [c19]Alekh Agarwal, John C. Duchi:
Distributed delayed stochastic optimization. CDC 2012: 5451-5452 - [c18]Yuchen Zhang, John C. Duchi, Martin J. Wainwright
Communication-efficient algorithms for statistical optimization. CDC 2012: 6792 - [c17]John C. Duchi, Michael I. Jordan, Martin J. Wainwright:
Privacy Aware Learning. NIPS 2012: 1439-1447 - [c16]John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Andre Wibisono:
Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods. NIPS 2012: 1448-1456 - [c15]Yuchen Zhang, John C. Duchi, Martin J. Wainwright:
Communication-Efficient Algorithms for Statistical Optimization. NIPS 2012: 1511-1519 - [c14]John C. Duchi:
Commentary on "Toward a Noncommutative Arithmetic-geometric Mean Inequality: Conjectures, Case-studies, and Consequences". COLT 2012: 11.25-11.27 - [i7]John C. Duchi, Lester W. Mackey, Michael I. Jordan:
The Asymptotics of Ranking Algorithms. CoRR abs/1204.1688 (2012) - [i6]John C. Duchi, Stephen Gould, Daphne Koller:
Projected Subgradient Methods for Learning Sparse Gaussians. CoRR abs/1206.3249 (2012) - [i5]Varun Ganapathi, David Vickrey, John C. Duchi, Daphne Koller:
Constrained Approximate Maximum Entropy Learning of Markov Random Fields. CoRR abs/1206.3257 (2012) - [i4]Alekh Agarwal, Peter L. Bartlett, John C. Duchi:
Oracle inequalities for computationally adaptive model selection. CoRR abs/1208.0129 (2012) - [i3]Yuchen Zhang, John C. Duchi, Martin J. Wainwright:
Comunication-Efficient Algorithms for Statistical Optimization. CoRR abs/1209.4129 (2012) - [i2]John C. Duchi, Michael I. Jordan, Martin J. Wainwright:
Privacy Aware Learning. CoRR abs/1210.2085 (2012) - 2011
- [j2]John C. Duchi, Elad Hazan, Yoram Singer:
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. J. Mach. Learn. Res. 12: 2121-2159 (2011) - [c13]John C. Duchi, Alekh Agarwal, Mikael Johansson, Michael I. Jordan
Ergodic mirror descent. Allerton 2011: 701-706 - [c12]Alekh Agarwal, John C. Duchi:
Distributed Delayed Stochastic Optimization. NIPS 2011: 873-881 - [c11]Alekh Agarwal, John C. Duchi, Peter L. Bartlett, Clément Levrard:
Oracle inequalities for computationally budgeted model selection. COLT 2011: 69-86 - [i1]Alekh Agarwal, John C. Duchi:
The Generalization Ability of Online Algorithms for Dependent Data. CoRR abs/1110.2529 (2011) - 2010
- [c10]John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Ambuj Tewari:
Composite Objective Mirror Descent. COLT 2010: 14-26 - [c9]John C. Duchi, Elad Hazan, Yoram Singer:
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. COLT 2010: 257-269 - [c8]John C. Duchi, Lester W. Mackey, Michael I. Jordan:
On the Consistency of Ranking Algorithms. ICML 2010: 327-334 - [c7]John C. Duchi, Alekh Agarwal, Martin J. Wainwright:
Distributed Dual Averaging In Networks. NIPS 2010: 550-558
2000 – 2009
- 2009
- [j1]John C. Duchi, Yoram Singer:
Efficient Online and Batch Learning Using Forward Backward Splitting. J. Mach. Learn. Res. 10: 2899-2934 (2009) - [c6]John C. Duchi, Yoram Singer:
Boosting with structural sparsity. ICML 2009: 297-304 - [c5]John C. Duchi, Yoram Singer:
Efficient Learning using Forward-Backward Splitting. NIPS 2009: 495-503 - 2008
- [c4]John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Tushar Chandra:
Efficient projections onto the l1-ball for learning in high dimensions. ICML 2008: 272-279 - [c3]John C. Duchi, Stephen Gould, Daphne Koller:
Projected Subgradient Methods for Learning Sparse Gaussians. UAI 2008: 145-152 - [c2]Varun Ganapathi, David Vickrey, John C. Duchi, Daphne Koller:
Constrained Approximate Maximum Entropy Learning of Markov Random Fields. UAI 2008: 196-203 - 2006
- [c1]John C. Duchi, Daniel Tarlow, Gal Elidan, Daphne Koller:
Using Combinatorial Optimization within Max-Product Belief Propagation. NIPS 2006: 369-376
Coauthor Index
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