César A. Uribe
Titel
Zitiert von
Zitiert von
Jahr
Fast convergence rates for distributed non-Bayesian learning
A Nedić, A Olshevsky, CA Uribe
IEEE Transactions on Automatic Control, 2017
1212017
Geometrically convergent distributed optimization with uncoordinated step-sizes
A Nedić, A Olshevsky, W Shi, CA Uribe
American Control Conference (ACC), 2017, 3950-3955, 2017
982017
A dual approach for optimal algorithms in distributed optimization over networks
CA Uribe, S Lee, A Gasnikov, A Nedić
Optimization Methods and Software, 1-40, 2020
882020
Nonasymptotic convergence rates for cooperative learning over time-varying directed graphs
A Nedić, A Olshevsky, CA Uribe
2015 American Control Conference (ACC), 5884-5889, 2015
832015
Decentralize and randomize: Faster algorithm for Wasserstein barycenters
P Dvurechenskii, D Dvinskikh, A Gasnikov, C Uribe, A Nedich
Advances in Neural Information Processing Systems, 10783-10793, 2018
722018
On the complexity of approximating Wasserstein barycenters
A Kroshnin, N Tupitsa, D Dvinskikh, P Dvurechensky, A Gasnikov, C Uribe
International Conference on Machine Learning, 3530-3540, 2019
572019
Distributed computation of Wasserstein barycenters over networks
CA Uribe, D Dvinskikh, P Dvurechensky, A Gasnikov, A Nedić
2018 IEEE Conference on Decision and Control (CDC), 6544-6549, 2018
412018
Gradient methods for problems with inexact model of the objective
FS Stonyakin, D Dvinskikh, P Dvurechensky, A Kroshnin, O Kuznetsova, ...
International Conference on Mathematical Optimization Theory and Operations …, 2019
402019
Optimal Tensor Methods in Smooth Convex and Uniformly Convex Optimization
A Gasnikov, P Dvurechensky, E Gorbunov, E Vorontsova, ...
Conference on Learning Theory, 1374-1391, 2019
37*2019
Optimal algorithms for distributed optimization
CA Uribe, S Lee, A Gasnikov, A Nedić
arXiv preprint arXiv:1712.00232, 2017
372017
Near Optimal Methods for Minimizing Convex Functions with Lipschitz -th Derivatives
A Gasnikov, P Dvurechensky, E Gorbunov, E Vorontsova, ...
Conference on Learning Theory, 1392-1393, 2019
302019
A tutorial on distributed (non-Bayesian) learning: Problem, algorithms and results
A Nedić, A Olshevsky, CA Uribe
Decision and Control (CDC), 2016 IEEE 55th Conference on, 6795-6801, 2016
272016
On Primal and Dual Approaches for Distributed Stochastic Convex Optimization over Networks
D Dvinskikh, E Gorbunov, A Gasnikov, P Dvurechensky, CA Uribe
2019 IEEE 58th Conference on Decision and Control (CDC), 7435-7440, 2019
25*2019
Distributed Learning for Cooperative Inference
A Nedić, A Olshevsky, CA Uribe
arXiv preprint arXiv:1704.02718, 2017
232017
Network independent rates in distributed learning
A Nedić, A Olshevsky, C Uribe
2016 American Control Conference (ACC), 1072-1077, 2016
222016
Optimal distributed convex optimization on slowly time-varying graphs
A Rogozin, CA Uribe, AV Gasnikov, N Malkovsky, A Nedić
IEEE Transactions on Control of Network Systems 7 (2), 829-841, 2019
212019
Unsupervised feature selection based on fuzzy clustering for fault detection of the Tennessee Eastman process
C Bedoya, C Uribe, C Isaza
Advances in Artificial Intelligence–IBERAMIA 2012, 350-360, 2012
162012
Accelerating incremental gradient optimization with curvature information
HT Wai, W Shi, CA Uribe, A Nedić, A Scaglione
Computational Optimization and Applications 76 (2), 347-380, 2020
15*2020
Distributed learning with infinitely many hypotheses
A Nedić, A Olshevsky, CA Uribe
2016 IEEE 55th Conference on Decision and Control (CDC), 6321-6326, 2016
152016
On malicious agents in non-Bayesian social learning with uncertain models
JZ Hare, CA Uribe, LM Kaplan, A Jadbabaie
2019 22th International Conference on Information Fusion (FUSION), 1-8, 2019
102019
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