Articles with public access mandates - Cristobal GuzmanLearn more
Available somewhere: 16
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
R Bassily, V Feldman, C Guzmán, K Talwar
NeurIPS, 2020
Mandates: US National Science Foundation
The complexity of nonconvex-strongly-concave minimax optimization
S Zhang, J Yang, C Guzmán, N Kiyavash, N He
UAI 2021, 2021
Mandates: US National Science Foundation
Non-euclidean differentially private stochastic convex optimization
R Bassily, C Guzmán, A Nandi
COLT 2021 134, 474-499, 2021
Mandates: US National Science Foundation
Lower Bounds for Parallel and Randomized Convex Optimization
J Diakonikolas, C Guzmán
Journal of Machine Learning Research 21 (5), 1-31, 2020
Mandates: US National Science Foundation
Differentially private stochastic optimization: New results in convex and non-convex settings
R Bassily, C Guzmán, M Menart
Advances in Neural Information Processing Systems 34, 9317-9329, 2021
Mandates: US National Science Foundation
Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization via Information Theory
G Braun, C Guzmán, S Pokutta
IEEE Transactions on Information Theory (DOI:10.1109/TIT.2017.2701343), 2017
Mandates: US National Science Foundation
New upper bounds for the density of translative packings of three-dimensional convex bodies with tetrahedral symmetry
M Dostert, C Guzmán, F Vallentin
Discrete & Computational Geometry (DOI: 10.1007/s00454-017-9882-y), 2017
Mandates: Netherlands Organisation for Scientific Research
Optimal Affine-Invariant Smooth Minimization Algorithms
A d'Aspremont, C Guzmán, M Jaggi
SIAM Journal on Optimization 28 (3), 2384-2405, 2018
Mandates: Swiss National Science Foundation, European Commission, AXA Research Fund …
Faster rates of convergence to stationary points in differentially private optimization
R Arora, R Bassily, T González, CA Guzmán, M Menart, E Ullah
International Conference on Machine Learning, 1060-1092, 2023
Mandates: US National Science Foundation
Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions
X Cai, C Song, CA Guzmán, J Diakonikolas
Advances in Neural Information Processing Systems, 2022
Mandates: US National Science Foundation, US Department of Defense
Differentially private generalized linear models revisited
R Arora, R Bassily, C Guzmán, M Menart, E Ullah
Advances in Neural Information Processing Systems 35, 22505-22517, 2022
Mandates: US National Science Foundation
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness
S Sachs, H Hadiji, T van Erven, C Guzmán
Advances in Neural Information Processing Systems (NeurIPS), 2022
Mandates: Netherlands Organisation for Scientific Research
Fast, Deterministic and Sparse Dimensionality Reduction
D Dadush, C Guzmán, N Olver
Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete …, 2018
Mandates: Netherlands Organisation for Scientific Research
Differentially Private Algorithms for the Stochastic Saddle Point Problem with Optimal Rates for the Strong Gap
R Bassily, C Guzmán, M Menart
COLT 2023 195, 2482-2508, 2023
Mandates: US National Science Foundation
Best-case lower bounds in online learning
C Guzmán, N Mehta, A Mortazavi
Advances in Neural Information Processing Systems 34, 21923-21934, 2021
Mandates: Natural Sciences and Engineering Research Council of Canada
Optimal Algorithms for Stochastic Complementary Composite Minimization
A d’Aspremont, C Guzmán, C Lezane
SIAM Journal on Optimization 34 (1), 163-189, 2024
Mandates: Agence Nationale de la Recherche
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