Cristóbal Guzmán
20 papers · 2015–2025 · 5 conferences · across top CS/AI conferences
Achievements
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(22)
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(11)
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(5)
Conferences
NIPS (9)
COLT (7)
JMLR (2)
ALT (1)
UAI (1)
Top co-authors
Research topics
Keywords
differential privacy
(10)
stochastic optimization
(6)
convex optimization
(4)
stochastic gradient descent
(3)
oracle complexity
(3)
stochastic convex optimization
(3)
lower bound
(3)
dimension-independent rate
(2)
first-order method
(2)
excess population risk
(2)
generalized linear model
(2)
minimax optimization
(2)
non-convex optimization
(2)
gradient descent
(2)
online convex optimization
(2)
parallel algorithm
(2)
complexity bound
(2)
regret bound
(2)
randomized algorithm
(2)
sparse regression
(1)
Papers
PREM: Privately Answering Statistical Queries with Relative Error
COLT 2025
Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity
JMLR 2025
Non-Euclidean High-Order Smooth Convex Optimization Extended Abstract
COLT 2025
Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates
ALT 2024
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems extended abstract
COLT 2024
Public-data Assisted Private Stochastic Optimization: Power and Limitations
NIPS 2024
Differentially Private Optimization with Sparse Gradients
NIPS 2024
Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry
NIPS 2024
Differentially Private Algorithms for the Stochastic Saddle Point Problem with Optimal Rates for the Strong Gap
COLT 2023
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness
NIPS 2022
Differentially Private Generalized Linear Models Revisited
NIPS 2022
Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions
NIPS 2022
Non-Euclidean Differentially Private Stochastic Convex Optimization
COLT 2021
The complexity of nonconvex-strongly-concave minimax optimization
UAI 2021
Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings
NIPS 2021
Best-case lower bounds in online learning
NIPS 2021
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
NIPS 2020
Lower Bounds for Parallel and Randomized Convex Optimization
JMLR 2020
Lower Bounds for Parallel and Randomized Convex Optimization
COLT 2019
Open Problem: The Oracle Complexity of Smooth Convex Optimization in Nonstandard Settings
COLT 2015