John Duchi

Q: What Schools Are Affiliated With John Duchi

John Duchi is affiliated with the following schools: University of California, Berkeley, Stanford University

John Duchi's AcademicInfluence.com Rankings

John Duchi

Mathematics

#6509

World Rank

#8994

Historical Rank

Statistics

#617

World Rank

#698

Historical Rank

mathematics Degrees

Download Badge

Mathematics

John Duchi's Degrees

PhD Statistics Stanford University

Why Is John Duchi Influential?

(Suggest an Edit or Addition)

(See a Problem?)

John Duchi's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Adaptive Subgradient Methods for Online Learning and Stochastic Optimization (2011) (9035)
Efficient projections onto the l1-ball for learning in high dimensions (2008) (1340)
Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling (2010) (1107)
Local privacy and statistical minimax rates (2013) (782)
Certifying Some Distributional Robustness with Principled Adversarial Training (2017) (660)
Efficient Online and Batch Learning Using Forward Backward Splitting (2009) (619)
Distributed delayed stochastic optimization (2011) (563)
Unlabeled Data Improves Adversarial Robustness (2019) (522)
Generalizing to Unseen Domains via Adversarial Data Augmentation (2018) (468)
Communication-efficient algorithms for statistical optimization (2012) (451)
MLbase: A Distributed Machine-learning System (2013) (350)
Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations (2013) (343)
Composite Objective Mirror Descent (2010) (323)
Certifiable Distributional Robustness with Principled Adversarial Training (2017) (315)
Divide and conquer kernel ridge regression: a distributed algorithm with minimax optimal rates (2013) (305)
Minimax Optimal Procedures for Locally Private Estimation (2016) (285)
Privacy Aware Learning (2012) (270)
Variance-based Regularization with Convex Objectives (2016) (251)
Protection Against Reconstruction and Its Applications in Private Federated Learning (2018) (247)
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences (2016) (244)
Information-theoretic lower bounds for distributed statistical estimation with communication constraints (2013) (240)
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach (2016) (237)
Learning Models with Uniform Performance via Distributionally Robust Optimization (2018) (213)
Randomized Smoothing for Stochastic Optimization (2011) (202)
Accelerated Methods for NonConvex Optimization (2018) (191)
Lower bounds for finding stationary points I (2017) (182)
Divide and Conquer Kernel Ridge Regression (2013) (182)
Adversarial Training Can Hurt Generalization (2019) (172)
Lower bounds for non-convex stochastic optimization (2019) (170)
Projected Subgradient Methods for Learning Sparse Gaussians (2008) (163)
Derivations for Linear Algebra and Optimization (2016) (150)
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation (2018) (143)
Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval (2017) (133)
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy (2020) (132)
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation (2013) (126)
Local Privacy and Statistical Minimax Rates (2013) (125)
Accelerated Methods for Non-Convex Optimization (2016) (122)
"Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions (2017) (120)
Efficient Learning using Forward-Backward Splitting (2009) (118)
Creating Human-like Synthetic Characters with Multiple Skill Levels: A Case Study using the Soar Quakebot (2001) (114)
Estimation, Optimization, and Parallelism when Data is Sparse (2013) (100)
Stochastic Methods for Composite and Weakly Convex Optimization Problems (2017) (100)
Large-Scale Methods for Distributionally Robust Optimization (2020) (97)
On the Consistency of Ranking Algorithms (2010) (96)
Gradient Descent Efficiently Finds the Cubic-Regularized Non-Convex Newton Step (2016) (93)
Ergodic mirror descent (2011) (90)
Learning Kernels with Random Features (2016) (82)
Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity (2018) (78)
The Generalization Ability of Online Algorithms for Dependent Data (2011) (77)
Lower bounds for finding stationary points II: first-order methods (2017) (76)
Local Privacy, Data Processing Inequalities, and Statistical Minimax Rates (2013) (72)
Boosting with structural sparsity (2009) (72)
Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care (2015) (72)
Lower Bounds for Locally Private Estimation via Communication Complexity (2019) (70)
Distributed Dual Averaging In Networks (2010) (69)
Randomized smoothing for (parallel) stochastic optimization (2012) (69)
Using Combinatorial Optimization within Max-Product Belief Propagation (2006) (68)
Privacy and Statistical Risk: Formalisms and Minimax Bounds (2014) (61)
Asynchronous stochastic convex optimization (2015) (59)
Optimality guarantees for distributed statistical estimation (2014) (58)
Distributionally Robust Losses Against Mixture Covariate Shifts (2019) (57)
The importance of better models in stochastic optimization (2019) (56)
Distance-based and continuum Fano inequalities with applications to statistical estimation (2013) (54)
Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction (2020) (49)
Distributionally Robust Losses for Latent Covariate Mixtures (2020) (49)
Bounds on the conditional and average treatment effect with unobserved confounding factors (2018) (43)
The Right Complexity Measure in Locally Private Estimation: It is not the Fisher Information (2018) (42)
Asymptotic optimality in stochastic optimization (2016) (42)
Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems (2018) (41)
Minimax rates for memory-bounded sparse linear regression (2015) (40)
Introductory lectures on stochastic optimization (2018) (39)
Robust Validation: Confident Predictions Even When Distributions Shift (2020) (37)
Simultaneous dimension reduction and adjustment for confounding variation (2016) (37)
Gradient Descent Finds the Cubic-Regularized Nonconvex Newton Step (2016) (35)
Stochastic Methods for Composite Optimization Problems (2017) (34)
Constrained Approximate Maximum Entropy Learning of Markov Random Fields (2008) (34)
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations (2020) (34)
Oracle inequalities for computationally budgeted model selection (2011) (32)
Adaptive Sampling Probabilities for Non-Smooth Optimization (2017) (32)
Multiclass classification, information, divergence and surrogate risk (2016) (30)
Private Adaptive Gradient Methods for Convex Optimization (2021) (29)
Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods (2012) (27)
Fisher Information (25)
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms (2020) (25)
Minimax Bounds on Stochastic Batched Convex Optimization (2018) (25)
Local Minimax Complexity of Stochastic Convex Optimization (2016) (25)
Near Instance-Optimality in Differential Privacy (2020) (23)
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems (2020) (22)
A Rank-1 Sketch for Matrix Multiplicative Weights (2019) (21)
The Asymptotics of Ranking Algorithms (2012) (20)
Multi-Armed Bandit Problem (2010) (19)
Fine-tuning is Fine in Federated Learning (2021) (18)
A ug 2 01 4 Local Privacy , Data Processing Inequalities , and Minimax Rates (2018) (18)
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis (2020) (17)
Oracle inequalities for computationally adaptive model selection (2012) (17)
Efficient projections onto the {\it l}$_{\mbox{1}}$-ball for learning in high dimensions (2008) (16)
Derivative Free Optimization Via Repeated Classification (2018) (16)
Dual averaging for distributed optimization (2012) (16)
First-Order Methods for Nonconvex Quadratic Minimization (2020) (15)
Knowing what you know: valid confidence sets in multiclass and multilabel prediction (2020) (14)
Element Level Differential Privacy: The Right Granularity of Privacy (2019) (14)
Bounds on the conditional and average treatment effect in the presence of unobserved confounders (2018) (13)
MLbase : A Distributed Machine Learning Wrapper (2012) (12)
Estimation from Indirect Supervision with Linear Moments (2016) (11)
Unsupervised Transformation Learning via Convex Relaxations (2017) (10)
Minibatch Stochastic Approximate Proximal Point Methods (2020) (10)
Accelerated, Optimal, and Parallel: Some Results on Model-Based Stochastic Optimization (2021) (9)
Optimal rates for zero-order optimization: the power of two function evaluations (2013) (9)
Local Asymptotics for some Stochastic Optimization Problems: Optimality, Constraint Identification, and Dual Averaging (2016) (9)
Necessary and Sufficient Geometries for Gradient Methods (2019) (9)
Adapting to Function Difficulty and Growth Conditions in Private Optimization (2021) (8)
Ergodic Subgradient Descent (2011) (8)
Optimization with uncertain data (2015) (6)
The Mathematics of Data (2018) (6)
A Fast Algorithm for Adaptive Private Mean Estimation (2023) (6)
Predictive Inference with Weak Supervision (2022) (6)
Commentary on \Towards a Noncommutative Arithmetic-Geometric Mean Inequality" by B. Recht and C. R e (2012) (6)
Modeling simple structures and geometry for better stochastic optimization algorithms (2019) (5)
Privacy: A few definitional aspects and consequences for minimax mean-squared error (2014) (5)
Lower bounds for finding stationary points I (2019) (5)
Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs (2019) (5)
Mean Estimation From One-Bit Measurements (2019) (5)
Mean estimation from adaptive one-bit measurements (2017) (4)
Stochastic optimization with noni . i . d . noise (2011) (4)
Memorize to Generalize: on the Necessity of Interpolation in High Dimensional Linear Regression (2022) (4)
Comments on Michael Jordan’s Essay “The AI Revolution Hasn’t Happened Yet" (2019) (4)
Lower Bounds for Finding Stationary Points of Non-Convex , Smooth High-Dimensional Functions ∗ (2017) (4)
Fine-tuning in Federated Learning: a simple but tough-to-beat baseline (2021) (3)
Proximal and First-Order Methods for Convex Optimization (2013) (3)
Temporal Constraint Satisfaction Problems An Evaluation of Search Strategies (3)
CS 229 Supplemental Lecture notes Hoeffding ’ s inequality (2016) (3)
A constrained risk inequality for general losses (2018) (3)
Federated Asymptotics: a model to compare federated learning algorithms (2021) (3)
Commentary on "Toward a Noncommutative Arithmetic-geometric Mean Inequality: Conjectures, Case-studies, and Consequences" (2012) (2)
Stanford Statistics 311/electrical Engineering 377 (2014) (2)
Private optimization in the interpolation regime: faster rates and hardness results (2022) (2)
Information Measures, Experiments, Multi-category Hypothesis Tests, and Surrogate Losses (2016) (2)
An Evaluation of Blackbox Graph Planning (2005) (1)
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices (2020) (1)
Multiple Optimality Guarantees in Statistical Learning (2014) (1)
Asynchronous stochastic convex optimization: the noise is in the noise and SGD don\textquotesingle t care (2015) (1)
Chapter 2 Minimax lower bounds : the Fano and Le Cam methods (2014) (1)
How many labelers do you have? A closer look at gold-standard labels (2022) (1)
On Misspecification in Prediction Problems and Robustness via Improper Learning (2021) (1)
A comment and erratum on"Excess Optimism: How Biased is the Apparent Error of an Estimator Tuned by SURE?" (2021) (1)
CS 229 Supplemental Lecture notes (2016) (1)
Exercises for Theory of Statistics (Stats300b) (2018) (1)
The Lifecycle of a Statistical Model: Model Failure Detection, Identification, and Refitting (2022) (1)
A few notes on contiguity, asymptotics, and local asymptotic normality (2018) (1)
When Covariate-shifted Data Augmentation Increases Test Error And How to Fix It (2019) (1)
Query By Humming : Finding Songs in a Polyphonic Database (2005) (1)
A Scalable Risk-based Framework for Rigorous Autonomous Vehicle Evaluation (2019) (1)
Nonparametric regression: minimax upper and lower bounds (2014) (1)
Variational Methods: a Short Overview (2009) (0)
Lecture 2 : Subgradient Methods (2016) (0)
Surrogate Risk Consistency : the Classification Case (2014) (0)
Crossword Puzzles and Constraint Satisfaction (2005) (0)
Submitted to the Annals of Statistics MULTICLASS CLASSIFICATION , INFORMATION , DIVERGENCE , AND SURROGATE RISK By (2017) (0)
Private Federated Statistics in an Interactive Setting (2022) (0)
Exercises for EE 364 b (2015) (0)
Exercises for EE364b (2022) (0)
Statistical Estimation of Large Deviation Rates for i.i.d. Sub-exponential Random Walks (2017) (0)
RINCIPLED A DVERSARIAL T RAINING (2018) (0)
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise (2022) (0)
AC-PCA adjusts for confounding variation in transcriptome data and recovers the anatomical structure of neocortex (2016) (0)
Universal Prediction and Coding 9.1 Universal and Sequential Prediction (0)
Assouad's Method 3.1 the Method 3.1.1 Well-separated Problems (2014) (0)
Rates of Convergence by Moduli of Continuity (2017) (0)
Näıve comparison of tests (2017) (0)
Stats 300 b : Theory of Statistics Winter 2018 Lecture 15 – February 27 (2018) (0)
Query-Adaptive Predictive Inference with Partial Labels (2022) (0)
Solutions for Stats311/EE377 (2019) (0)
Exponential Families and Maximum Entropy (0)
Robust Stochastic Optimization : Learning the Tails (2016) (0)
Local Asymptotics for Stochastic Optimization: Optimality, Constraint Identification, and Dual Averaging (2016) (0)
Dynamic management of network risk from epidemic phenomena (2015) (0)
Proofs for empirical likelihood with general f-divergences (2016) (0)
2 Convex Sets and Local / Global Minima (2007) (0)
AC-PCA: simultaneous dimension reduction and adjustment for confounding variation (2016) (0)
Anomaly Detection for Asynchronous and Incomplete Data (2008) (0)
Necessary and Sufficient Conditions for Adaptive, Mirror, and Standard Gradient Methods (2019) (0)
Editorial (1902) (0)
Gaussian Sequence Models – Hard Thresholding – Soft Thresholding • Basis Pursuit / Noiseless recovery – l 1-relaxations – Isometry properties of matrices 1 Gaussian Sequence Model Recap (2019) (0)
VC-Dimension , Covering , and Packing (2017) (0)
More Variational Methods , Ising Models , and Fixed Point Iteration (0)
A ug 2 01 2 Oracle inequalities for computationally adaptive model selection (2012) (0)
Convexity , Detection , and Generalized f-divergences (2015) (0)

This paper list is powered by the following services:

Other Resources About John Duchi

profiles.stanford.edu

What Schools Are Affiliated With John Duchi?

John Duchi is affiliated with the following schools:

John Duchi's Academic­Influence.com Rankings