| Fast convergence rates for distributed non-Bayesian learning A Nedić, A Olshevsky, CA Uribe IEEE Transactions on Automatic Control, 2017 | 193 | 2017 |
| Geometrically convergent distributed optimization with uncoordinated step-sizes A Nedić, A Olshevsky, W Shi, CA Uribe American Control Conference (ACC), 2017, 3950-3955, 2017 | 153 | 2017 |
| 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 | 150 | 2020 |
| 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 | 113 | 2018 |
| 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 | 105 | 2019 |
| 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 | 100 | 2015 |
| 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 | 77 | 2019 |
| 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 | 70* | 2019 |
| 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 | 59 | 2018 |
| Gradient methods for problems with inexact model of the objective FS Stonyakin, D Dvinskikh, P Dvurechensky, A Kroshnin, O Kuznetsova, ... Mathematical Optimization Theory and Operations Research: 18th International …, 2019 | 55 | 2019 |
| Optimal algorithms for distributed optimization CA Uribe, S Lee, A Gasnikov, A Nedić arXiv preprint arXiv:1712.00232, 2017 | 42 | 2017 |
| 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 | 40* | 2019 |
| 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 | 36 | 2016 |
| 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 | 34 | 2019 |
| Distributed Learning for Cooperative Inference A Nedić, A Olshevsky, CA Uribe arXiv preprint arXiv:1704.02718, 2017 | 28 | 2017 |
| Resilient primal–dual optimization algorithms for distributed resource allocation B Turan, CA Uribe, HT Wai, M Alizadeh IEEE Transactions on Control of Network Systems 8 (1), 282-294, 2020 | 26 | 2020 |
| Multimarginal optimal transport by accelerated alternating minimization N Tupitsa, P Dvurechensky, A Gasnikov, CA Uribe 2020 59th IEEE Conference on Decision and Control (CDC), 6132-6137, 2020 | 25 | 2020 |
| Network independent rates in distributed learning A Nedić, A Olshevsky, C Uribe 2016 American Control Conference (ACC), 1072-1077, 2016 | 25 | 2016 |
| 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 | 24* | 2020 |
| 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 | 19 | 2012 |
Angelia NedichProfessor, Arizona State UniversityVerified email at asu.edu
