Cristóbal Guzmán

According to our database, Cristóbal Guzmán authored at least 38 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Complementary composite minimization, small gradients in general norms, and applications.

Jelena Diakonikolas, Cristóbal Guzmán

Math. Program., November, 2024

Corrections to "Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization via Information Theory".

Gábor Braun, Cristóbal Guzmán, Sebastian Pokutta

IEEE Trans. Inf. Theory, July, 2024

Optimal Algorithms for Stochastic Complementary Composite Minimization.

Alexandre d'Aspremont, Cristóbal Guzmán, Clément Lezane

SIAM J. Optim., March, 2024

Optimal algorithms for differentially private stochastic monotone variational inequalities and saddle-point problems.

Digvijay Boob, Cristóbal Guzmán

Math. Program., March, 2024

Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity.

Quan Nguyen, Nishant A. Mehta, Cristóbal Guzmán

CoRR, 2024

Tracking solutions of time-varying variational inequalities.

Hédi Hadiji, Sarah Sachs, Cristóbal Guzmán

CoRR, 2024

Differentially Private Optimization with Sparse Gradients.

Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

CoRR, 2024

Optimization on a Finer Scale: Bounded Local Subgradient Variation Perspective.

Jelena Diakonikolas, Cristóbal Guzmán

CoRR, 2024

Public-data Assisted Private Stochastic Optimization: Power and Limitations.

Enayat Ullah, Michael Menart, Raef Bassily, Cristóbal Guzmán, Raman Arora

CoRR, 2024

Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems.

Tomás González, Cristóbal Guzmán, Courtney Paquette

CoRR, 2024

Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems extended abstract.

Tomás González, Cristóbal Guzmán, Courtney Paquette

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates.

Michael Menart, Enayat Ullah, Raman Arora, Raef Bassily, Cristóbal Guzmán

Proceedings of the International Conference on Algorithmic Learning Theory, 2024

2023

Accelerated Rates between Stochastic and Adversarial Online Convex Optimization.

Sarah Sachs, Hédi Hadiji, Tim van Erven, Cristóbal Guzmán

CoRR, 2023

Faster Rates of Convergence to Stationary Points in Differentially Private Optimization.

Raman Arora, Raef Bassily, Tomás González, Cristóbal Guzmán, Michael Menart, Enayat Ullah

Proceedings of the International Conference on Machine Learning, 2023

Differentially Private Algorithms for the Stochastic Saddle Point Problem with Optimal Rates for the Strong Gap.

Raef Bassily, Cristóbal Guzmán, Michael Menart

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Network Pricing: How to Induce Optimal Flows Under Strategic Link Operators.

José Correa, Cristóbal Guzmán, Thanasis Lianeas, Evdokia Nikolova, Marc Schröder

Oper. Res., 2022

A sequential Stackelberg game for dynamic inspection problems.

Cristóbal Guzmán, Javiera Riffo, Claudio Telha, Mathieu Van Vyve

Eur. J. Oper. Res., 2022

A Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusion Problems.

Xufeng Cai, Chaobing Song, Cristóbal Guzmán, Jelena Diakonikolas

CoRR, 2022

Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness.

Sarah Sachs, Hédi Hadiji, Tim van Erven, Cristóbal Guzmán

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions.

Xufeng Cai, Chaobing Song, Cristóbal Guzmán, Jelena Diakonikolas

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Differentially Private Generalized Linear Models Revisited.

Raman Arora, Raef Bassily, Cristóbal Guzmán, Michael Menart, Enayat Ullah

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

An Optimal Algorithm for Strict Circular Seriation.

Santiago Armstrong, Cristóbal Guzmán, Carlos A. Sing-Long

SIAM J. Math. Data Sci., 2021

Statistical Query Algorithms for Mean Vector Estimation and Stochastic Convex Optimization.

Vitaly Feldman, Cristóbal Guzmán, Santosh Srinivas Vempala

Math. Oper. Res., 2021

Complementary Composite Minimization, Small Gradients in General Norms, and Applications to Regression Problems.

Jelena Diakonikolas, Cristóbal Guzmán

CoRR, 2021

The complexity of nonconvex-strongly-concave minimax optimization.

Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, Niao He

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Best-case lower bounds in online learning.

Cristóbal Guzmán, Nishant A. Mehta, Ali Mortazavi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings.

Raef Bassily, Cristóbal Guzmán, Michael Menart

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Non-Euclidean Differentially Private Stochastic Convex Optimization.

Raef Bassily, Cristóbal Guzmán, Anupama Nandi

Proceedings of the Conference on Learning Theory, 2021

2020

Lower Bounds for Parallel and Randomized Convex Optimization.

Jelena Diakonikolas, Cristóbal Guzmán

J. Mach. Learn. Res., 2020

Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses.

Raef Bassily, Vitaly Feldman, Cristóbal Guzmán, Kunal Talwar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2018

Optimal Affine-Invariant Smooth Minimization Algorithms.

Alexandre d'Aspremont, Cristóbal Guzmán, Martin Jaggi

SIAM J. Optim., 2018

Fast, Deterministic and Sparse Dimensionality Reduction.

Daniel Dadush, Cristóbal Guzmán, Neil Olver

Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, 2018

2017

Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization via Information Theory.

Gábor Braun, Cristóbal Guzmán, Sebastian Pokutta

IEEE Trans. Inf. Theory, 2017

New Upper Bounds for the Density of Translative Packings of Three-Dimensional Convex Bodies with Tetrahedral Symmetry.

Maria Dostert, Cristóbal Guzmán, Fernando Mário de Oliveira Filho, Frank Vallentin

Discret. Comput. Geom., 2017

2015

On lower complexity bounds for large-scale smooth convex optimization.

Cristóbal Guzmán, Arkadi Nemirovski

J. Complex., 2015

Statistical Query Algorithms for Stochastic Convex Optimization.

Vitaly Feldman, Cristóbal Guzmán, Santosh S. Vempala

CoRR, 2015

Open Problem: The Oracle Complexity of Smooth Convex Optimization in Nonstandard Settings.

Cristóbal Guzmán

Proceedings of The 28th Conference on Learning Theory, 2015

2014

Network congestion control with Markovian multipath routing.

Roberto Cominetti, Cristóbal Guzmán

Math. Program., 2014