John C. Duchi

According to our database1, John C. Duchi authored at least 88 papers between 2006 and 2020.

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Bibliography

2020
First-Order Methods for Nonconvex Quadratic Minimization.
SIAM Review, 2020

Distributionally Robust Losses for Latent Covariate Mixtures.
CoRR, 2020

Near Instance-Optimality in Differential Privacy.
CoRR, 2020

FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis.
CoRR, 2020

Understanding and Mitigating the Tradeoff Between Robustness and Accuracy.
CoRR, 2020

Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations.
Proceedings of the Conference on Learning Theory, 2020

2019
Gradient Descent Finds the Cubic-Regularized Nonconvex Newton Step.
SIAM J. Optim., 2019

Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity.
SIAM J. Optim., 2019

Variance-based Regularization with Convex Objectives.
J. Mach. Learn. Res., 2019

Element Level Differential Privacy: The Right Granularity of Privacy.
CoRR, 2019

Lower Bounds for Non-Convex Stochastic Optimization.
CoRR, 2019

Necessary and Sufficient Conditions for Adaptive, Mirror, and Standard Gradient Methods.
CoRR, 2019

Adversarial Training Can Hurt Generalization.
CoRR, 2019

Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs.
CoRR, 2019

Mean Estimation from One-Bit Measurements.
CoRR, 2019

Necessary and Sufficient Geometries for Gradient Methods.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Unlabeled Data Improves Adversarial Robustness.
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.
Proceedings of the Conference on Learning Theory, 2019

A Rank-1 Sketch for Matrix Multiplicative Weights.
Proceedings of the Conference on Learning Theory, 2019

Modeling simple structures and geometry for better stochastic optimization algorithms.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Stochastic Methods for Composite and Weakly Convex Optimization Problems.
SIAM J. Optim., 2018

Accelerated Methods for NonConvex Optimization.
SIAM J. Optim., 2018

Protection Against Reconstruction and Its Applications in Private Federated Learning.
CoRR, 2018

Learning Models with Uniform Performance via Distributionally Robust Optimization.
CoRR, 2018

The Right Complexity Measure in Locally Private Estimation: It is not the Fisher Information.
CoRR, 2018

Derivative free optimization via repeated classification.
CoRR, 2018

Generalizing to Unseen Domains via Adversarial Data Augmentation.
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.
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.
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.
Proceedings of the 6th International Conference on Learning Representations, 2018

Minimax Bounds on Stochastic Batched Convex Optimization.
Proceedings of the Conference On Learning Theory, 2018

Derivative Free Optimization Via Repeated Classification.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Certifiable Distributional Robustness with Principled Adversarial Training.
CoRR, 2017

Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval.
CoRR, 2017

Unsupervised Transformation Learning via Convex Relaxations.
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.
Proceedings of the 34th International Conference on Machine Learning, 2017

"Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions.
Proceedings of the 34th International Conference on Machine Learning, 2017

Mean estimation from adaptive one-bit measurements.
Proceedings of the 55th Annual Allerton Conference on Communication, 2017

2016
Minimax Optimal Procedures for Locally Private Estimation.
CoRR, 2016

Information Measures, Experiments, Multi-category Hypothesis Tests, and Surrogate Losses.
CoRR, 2016

Accelerated Methods for Non-Convex Optimization.
CoRR, 2016

Gradient Descent Efficiently Finds the Cubic-Regularized Non-Convex Newton Step.
CoRR, 2016

Learning Kernels with Random Features.
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.
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.
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.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations.
IEEE Trans. Inf. Theory, 2015

Divide and conquer kernel ridge regression: a distributed algorithm with minimax optimal rates.
J. Mach. Learn. Res., 2015

Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care.
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.
Proceedings of The 28th Conference on Learning Theory, 2015

Dynamic management of network risk from epidemic phenomena.
Proceedings of the 54th IEEE Conference on Decision and Control, 2015

2014
Multiple Optimality Guarantees in Statistical Learning.
PhD thesis, 2014

Privacy Aware Learning.
J. ACM, 2014

Privacy and Statistical Risk: Formalisms and Minimax Bounds.
CoRR, 2014

Privacy: A few definitional aspects and consequences for minimax mean-squared error.
Proceedings of the 53rd IEEE Conference on Decision and Control, 2014

2013
The Generalization Ability of Online Algorithms for Dependent Data.
IEEE Trans. Inf. Theory, 2013

Communication-efficient algorithms for statistical optimization.
J. Mach. Learn. Res., 2013

Distance-based and continuum Fano inequalities with applications to statistical estimation.
CoRR, 2013

Optimal rates for zero-order optimization: the power of two function evaluations.
CoRR, 2013

Information-theoretic lower bounds for distributed statistical estimation with communication constraints.
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.
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.
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.
Proceedings of the 54th Annual IEEE Symposium on Foundations of Computer Science, 2013

Divide and Conquer Kernel Ridge Regression.
Proceedings of the COLT 2013, 2013

MLbase: A Distributed Machine-learning System.
Proceedings of the CIDR 2013, 2013

2012
Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling.
IEEE Trans. Autom. Control., 2012

Randomized Smoothing for Stochastic Optimization.
SIAM J. Optim., 2012

Ergodic Mirror Descent.
SIAM J. Optim., 2012

Commentary on "Toward a Noncommutative Arithmetic-geometric Mean Inequality: Conjectures, Case-studies, and Consequences".
Proceedings of the COLT 2012, 2012

Comunication-Efficient Algorithms for Statistical Optimization
CoRR, 2012

Oracle inequalities for computationally adaptive model selection
CoRR, 2012

The Asymptotics of Ranking Algorithms
CoRR, 2012

Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods.
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.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012

Distributed delayed stochastic optimization.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012

Dual averaging for distributed optimization.
Proceedings of the 50th Annual Allerton Conference on Communication, 2012

2011
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.
J. Mach. Learn. Res., 2011

Oracle inequalities for computationally budgeted model selection.
Proceedings of the COLT 2011, 2011

2010
Distributed Dual Averaging In Networks.
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.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Composite Objective Mirror Descent.
Proceedings of the COLT 2010, 2010

2009
Efficient Online and Batch Learning Using Forward Backward Splitting.
J. Mach. Learn. Res., 2009

Efficient Learning using Forward-Backward Splitting.
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.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008
Constrained Approximate Maximum Entropy Learning of Markov Random Fields.
Proceedings of the UAI 2008, 2008

Projected Subgradient Methods for Learning Sparse Gaussians.
Proceedings of the UAI 2008, 2008

Efficient projections onto the <i>l</i><sub>1</sub>-ball for learning in high dimensions.
Proceedings of the Machine Learning, 2008

2006
Using Combinatorial Optimization within Max-Product Belief Propagation.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006


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