John C. Duchi
35 papers · 2006–2024 · 5 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π Interdisciplinary Bridge π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (19) π Conference Polyglot (5)
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Cross-Pollinator
(15)
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Renaissance Researcher
(7)
π§
Keyword Pioneer
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Keyword Trendsetter Combo
(3)
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Conference Loyalist
(26)
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Keyword Champion
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Deep Specialist
(19)
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Century Club
(35)
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Conference Pioneer
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Trend Setter
ποΈ
Keyword Collector
(62)
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Prolific Year
(6)
π₯
Unstoppable
(10)
Conferences
NIPS (26)
COLT (3)
JMLR (3)
ICML (2)
AISTATS (1)
Top co-authors
Research topics
Keywords
convex optimization
(8)
stochastic optimization
(6)
distributed optimization
(4)
stochastic convex optimization
(4)
differential privacy
(4)
stochastic gradient descent
(4)
gradient descent
(4)
convergence rate
(4)
stochastic gradient
(3)
distributionally robust optimization
(3)
empirical risk minimization
(3)
statistical optimization
(2)
asynchronous optimization
(2)
conformal prediction
(2)
communication-efficient algorithms
(2)
semi-supervised learning
(2)
statistical estimation
(2)
linear regression
(2)
principal component analysis
(2)
non-convex optimization
(2)
Papers
Universally Instance-Optimal Mechanisms for Private Statistical Estimation
COLT 2024
Predictive Inference with Weak Supervision
JMLR 2024
Collaboratively Learning Linear Models with Structured Missing Data
NIPS 2023
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise
NIPS 2022
Adapting to function difficulty and growth conditions in private optimization
NIPS 2021
Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction
JMLR 2021
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices
NIPS 2020
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations
COLT 2020
Minibatch Stochastic Approximate Proximal Point Methods
NIPS 2020
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms
NIPS 2020
Large-Scale Methods for Distributionally Robust Optimization
NIPS 2020
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems
NIPS 2020
Necessary and Sufficient Geometries for Gradient Methods
NIPS 2019
Unlabeled Data Improves Adversarial Robustness
NIPS 2019
Modeling simple structures and geometry for better stochastic optimization algorithms
AISTATS 2019
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation
NIPS 2018
Generalizing to Unseen Domains via Adversarial Data Augmentation
NIPS 2018
Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems
NIPS 2018
Adaptive Sampling Probabilities for Non-Smooth Optimization
ICML 2017
βConvex Until Proven Guiltyβ: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions
ICML 2017
Variance-based Regularization with Convex Objectives
NIPS 2017
Unsupervised Transformation Learning via Convex Relaxations
NIPS 2017
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences
NIPS 2016
Learning Kernels with Random Features
NIPS 2016
Local Minimax Complexity of Stochastic Convex Optimization
NIPS 2016
Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care
NIPS 2015
Communication-Efficient Algorithms for Statistical Optimization
JMLR 2013
Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods
NIPS 2012
Communication-Efficient Algorithms for Statistical Optimization
NIPS 2012
Privacy Aware Learning
NIPS 2012
Distributed Delayed Stochastic Optimization
NIPS 2011
Oracle inequalities for computationally budgeted model selection
COLT 2011
Distributed Dual Averaging In Networks
NIPS 2010
Efficient Learning using Forward-Backward Splitting
NIPS 2009
Using Combinatorial Optimization within Max-Product Belief Propagation
NIPS 2006