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2020 – today
- 2023
[c61]Wesley A. Suttle, Alec Koppel, Ji Liu:
Information-Directed Policy Search in Sparse-Reward Settings via the Occupancy Information Ratio. CISS 2023: 1-6- [i42]Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Mengdi Wang, Furong Huang, Dinesh Manocha:
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning. CoRR abs/2301.12038 (2023) - [i41]Wesley A. Suttle, Amrit Singh Bedi, Bhrij Patel, Brian M. Sadler, Alec Koppel, Dinesh Manocha:
Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic. CoRR abs/2301.12083 (2023) - [i40]Donghao Ying, Yuhao Ding, Alec Koppel, Javad Lavaei:
Scalable Multi-Agent Reinforcement Learning with General Utilities. CoRR abs/2302.07938 (2023) - 2022
- [j27]Yagiz Savas, Erfaun Noorani, Alec Koppel, John S. Baras, Ufuk Topcu, Brian M. Sadler:
Collaborative one-shot beamforming under localization errors: A discrete optimization approach. Signal Process. 200: 108647 (2022) - [j26]Ehsan Zobeidi
, Alec Koppel, Nikolay Atanasov:
Dense Incremental Metric-Semantic Mapping for Multiagent Systems via Sparse Gaussian Process Regression. IEEE Trans. Robotics 38(5): 3133-3153 (2022) - [j25]Amrit Singh Bedi, Ketan Rajawat, Vaneet Aggarwal, Alec Koppel:
Escaping Saddle Points for Successive Convex Approximation. IEEE Trans. Signal Process. 70: 307-321 (2022) - [j24]Abhishek Chakraborty, Ketan Rajawat, Alec Koppel:
Sparse Representations of Positive Functions via First- and Second-Order Pseudo-Mirror Descent. IEEE Trans. Signal Process. 70: 3148-3164 (2022) - [j23]Zhan Gao, Alec Koppel, Alejandro Ribeiro:
Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems. IEEE Trans. Signal Process. 70: 3693-3708 (2022) - [c60]Qinbo Bai, Amrit Singh Bedi, Mridul Agarwal, Alec Koppel, Vaneet Aggarwal:
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach. AAAI 2022: 3682-3689 - [c59]Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel:
Multi-Agent Reinforcement Learning with General Utilities via Decentralized Shadow Reward Actor-Critic. AAAI 2022: 9031-9039 - [c58]James Di, Ehsan Zobeidi, Alec Koppel, Nikolay Atanasov:
Distributed Gaussian Process Mapping for Robot Teams with Time-varying Communication. ACC 2022: 4458-4464 - [c57]Alec Koppel, Amrit Singh Bedi, Bhargav Ganguly, Vaneet Aggarwal:
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming. CDC 2022: 4545-4552 - [c56]Wesley A. Suttle, Alec Koppel, Ji Liu:
Policy Gradient for Ratio Optimization: A Case Study. CISS 2022: 281-286 - [c55]Hrusikesha Pradhan, Alec Koppel, Ketan Rajawat:
On Submodular Set Cover Problems for Near-Optimal Online Kernel Basis Selection. ICASSP 2022: 4168-4172 - [c54]Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil, Brian M. Sadler, Pratap Tokekar, Alec Koppel:
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces. ICML 2022: 1716-1731 - [c53]Qiujiang Jin, Alec Koppel, Ketan Rajawat, Aryan Mokhtari:
Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood. ICML 2022: 10228-10250 - [c52]Yulun Tian, Amrit Singh Bedi, Alec Koppel, Miguel Calvo-Fullana, David M. Rosen, Jonathan P. How:
Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation. IROS 2022: 4391-4398 - [i39]Cole Hawkins, Alec Koppel, Zheng Zhang:
Online, Informative MCMC Thinning with Kernelized Stein Discrepancy. CoRR abs/2201.07130 (2022) - [i38]Wesley A. Suttle, Alec Koppel, Ji Liu:
Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search. CoRR abs/2201.08832 (2022) - [i37]Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil, Brian M. Sadler, Pratap Tokekar, Alec Koppel:
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces. CoRR abs/2201.12332 (2022) - [i36]Yulun Tian, Amrit Singh Bedi, Alec Koppel, Miguel Calvo-Fullana, David M. Rosen, Jonathan P. How:
Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation. CoRR abs/2203.00851 (2022) - [i35]Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Brian M. Sadler, Furong Huang, Pratap Tokekar, Dinesh Manocha:
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning. CoRR abs/2206.01162 (2022) - [i34]Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Pratap Tokekar, Dinesh Manocha:
Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies. CoRR abs/2206.05652 (2022) - [i33]Amrit Singh Bedi, Chen Fan, Alec Koppel, Anit Kumar Sahu, Brian M. Sadler, Furong Huang, Dinesh Manocha:
FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus. CoRR abs/2206.10815 (2022) - [i32]Muhammad Aneeq uz Zaman, Alec Koppel, Sujay Bhatt, Tamer Basar:
Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path. CoRR abs/2208.11639 (2022) - 2021
- [j22]Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel:
Cautious Reinforcement Learning via Distributional Risk in the Dual Domain. IEEE J. Sel. Areas Inf. Theory 2(2): 611-626 (2021) - [j21]Alec Koppel, Hrusikesha Pradhan, Ketan Rajawat:
Consistent online Gaussian process regression without the sample complexity bottleneck. Stat. Comput. 31(6): 76 (2021) - [j20]Alec Koppel, Garrett Warnell, Ethan Stump, Peter Stone, Alejandro Ribeiro:
Policy Evaluation in Continuous MDPs With Efficient Kernelized Gradient Temporal Difference. IEEE Trans. Autom. Control. 66(4): 1856-1863 (2021) - [j19]Hrusikesha Pradhan, Amrit Singh Bedi, Alec Koppel, Ketan Rajawat:
Adaptive Kernel Learning in Heterogeneous Networks. IEEE Trans. Signal Inf. Process. over Networks 7: 423-437 (2021) - [j18]Amrit Singh Bedi, Alec Koppel, Ketan Rajawat, Panchajanya Sanyal:
Nonparametric Compositional Stochastic Optimization for Risk-Sensitive Kernel Learning. IEEE Trans. Signal Process. 69: 428-442 (2021) - [j17]Deepak S. Kalhan, Amrit Singh Bedi, Alec Koppel, Ketan Rajawat, Hamed Hassani, Abhishek K. Gupta, Adrish Banerjee:
Dynamic Online Learning via Frank-Wolfe Algorithm. IEEE Trans. Signal Process. 69: 932-947 (2021) - [j16]Alec Koppel, Amrit Singh Bedi, Brian M. Sadler, Víctor Elvira:
Nearly Consistent Finite Particle Estimates in Streaming Importance Sampling. IEEE Trans. Signal Process. 69: 6401-6415 (2021) - [c51]Erfaun Noorani, Yagiz Savas, Alec Koppel, John S. Baras, Ufuk Topcu, Brian M. Sadler:
Collaborative Beamforming for Agents with Localization Errors. ACSCC 2021: 204-208 - [c50]Abhishek Chakraborty, Ketan Rajawat, Alec Koppel:
Projected Pseudo-Mirror Descent in Reproducing Kernel Hilbert Space. ACSCC 2021: 1008-1012 - [c49]Alec Koppel, Amrit Singh Bedi, Bhargav Ganguly, Vaneet Aggarwal:
Randomized Linear Programming for Tabular Average-Cost Multi-agent Reinforcement Learning. ACSCC 2021: 1023-1026 - [c48]Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel:
Beyond Cumulative Returns via Reinforcement Learning over State-Action Occupancy Measures. ACC 2021: 894-901 - [c47]Anjaly Parayil, Amrit Singh Bedi, Alec Koppel:
Joint Position and Beamforming Control via Alternating Nonlinear Least-Squares with a Hierarchical Gamma Prior. ACC 2021: 3513-3518 - [c46]Amrit Singh Bedi, Alec Koppel, Mengdi Wang, Junyu Zhang:
Intermittent Communications in Decentralized Shadow Reward Actor-Critic. CDC 2021: 2613-2620 - [c45]Sujay Bhatt, Weichao Mao, Alec Koppel, Tamer Basar:
Semiparametric Information State Embedding for Policy Search under Imperfect Information. CDC 2021: 4501-4506 - [c44]Alec Koppel, Amrit S. Bedi, Vikram Krishnamurthy:
A Dynamical Systems Perspective on Online Bayesian Nonparametric Estimators with Adaptive Hyperparameters. ICASSP 2021: 2975-2979 - [c43]Michael E. Kepler, Alec Koppel, Amrit Singh Bedi, Daniel J. Stilwell:
Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference. IROS 2021: 9833-9840 - [i31]Ekaterina I. Tolstaya, Ethan Stump, Alec Koppel, Alejandro Ribeiro:
Composable Learning with Sparse Kernel Representations. CoRR abs/2103.14474 (2021) - [i30]Ehsan Zobeidi, Alec Koppel, Nikolay Atanasov:
Dense Incremental Metric-Semantic Mapping for Multi-Agent Systems via Sparse Gaussian Process Regression. CoRR abs/2103.16170 (2021) - [i29]Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel:
MARL with General Utilities via Decentralized Shadow Reward Actor-Critic. CoRR abs/2106.00543 (2021) - [i28]Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang, Mengdi Wang, Alec Koppel:
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control. CoRR abs/2106.08414 (2021) - [i27]Michael E. Kepler, Alec Koppel, Amrit Singh Bedi, Daniel J. Stilwell:
Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference. CoRR abs/2107.12797 (2021) - [i26]Qinbo Bai, Amrit Singh Bedi, Mridul Agarwal, Alec Koppel, Vaneet Aggarwal:
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach. CoRR abs/2109.06332 (2021) - [i25]James Di, Ehsan Zobeidi, Alec Koppel, Nikolay Atanasov:
Distributed Gaussian Process Mapping for Robot Teams with Time-varying Communication. CoRR abs/2110.06401 (2021) - 2020
- [j15]Aryan Mokhtari, Alec Koppel, Martin Takác, Alejandro Ribeiro:
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning. J. Mach. Learn. Res. 21: 120:1-120:51 (2020) - [j14]Yulun Tian, Alec Koppel, Amrit Singh Bedi, Jonathan P. How:
Asynchronous and Parallel Distributed Pose Graph Optimization. IEEE Robotics Autom. Lett. 5(4): 5819-5826 (2020) - [j13]Kaiqing Zhang, Alec Koppel, Hao Zhu, Tamer Basar:
Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies. SIAM J. Control. Optim. 58(6): 3586-3612 (2020) - [j12]Alec Koppel, Amrit Singh Bedi, Ketan Rajawat, Brian M. Sadler:
Optimally Compressed Nonparametric Online Learning: Tradeoffs between memory and consistency. IEEE Signal Process. Mag. 37(3): 61-70 (2020) - [j11]Aryan Mokhtari, Alec Koppel:
High-Dimensional Nonconvex Stochastic Optimization by Doubly Stochastic Successive Convex Approximation. IEEE Trans. Signal Process. 68: 6287-6302 (2020) - [c42]Hrusikesha Pradhan, Amrit Singh Bedi, Alec Koppel, Ketan Rajawat:
Conservative Multi-agent Online Kernel Learning in Heterogeneous Networks. ACSSC 2020: 53-57 - [c41]Amrit Singh Bedi, Alec Koppel, Ketan Rajawat, Brian M. Sadler:
Trading Dynamic Regret for Model Complexity in Nonstationary Nonparametric Optimization. ACC 2020: 321-326 - [c40]Nagananda K. G, Rick S. Blum, Alec Koppel:
Reduced-rank Least Squares Parameter Estimation in the Presence of Byzantine Sensors. CISS 2020: 1-6 - [c39]Deepak S. Kalhan, Amrit S. Bedi, Alec Koppel, Ketan Rajawat, Abhishek K. Gupta, Adrish Banerjee:
Projection Free Dynamic Online Learning. ICASSP 2020: 3957-3961 - [c38]Zhan Gao, Alec Koppel, Alejandro Ribeiro:
Balancing Rates and Variance via Adaptive Batch-Sizes in First-Order Stochastic Optimization. ICASSP 2020: 5385-5389 - [c37]Ehsan Zobeidi, Alec Koppel, Nikolay Atanasov:
Dense Incremental Metric-Semantic Mapping via Sparse Gaussian Process Regression. IROS 2020: 6180-6187 - [c36]Amrit Singh Bedi, Dheeraj Peddireddy, Vaneet Aggarwal, Alec Koppel:
Efficient Large-Scale Gaussian Process Bandits by Believing only Informative Actions. L4DC 2020: 924-934 - [c35]Junyu Zhang, Alec Koppel, Amrit Singh Bedi, Csaba Szepesvári, Mengdi Wang:
Variational Policy Gradient Method for Reinforcement Learning with General Utilities. NeurIPS 2020 - [i24]Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel:
Cautious Reinforcement Learning via Distributional Risk in the Dual Domain. CoRR abs/2002.12475 (2020) - [i23]Yulun Tian, Alec Koppel, Amrit Singh Bedi, Jonathan P. How:
Asynchronous and Parallel Distributed Pose Graph Optimization. CoRR abs/2003.03281 (2020) - [i22]Amrit Singh Bedi, Dheeraj Peddireddy, Vaneet Aggarwal, Alec Koppel:
Efficient Gaussian Process Bandits by Believing only Informative Actions. CoRR abs/2003.10550 (2020) - [i21]Erfaun Noorani, Yagiz Savas, Alec Koppel, John S. Baras, Ufuk Topcu, Brian M. Sadler:
Distributed Beamforming for Agents with Localization Errors. CoRR abs/2003.12637 (2020) - [i20]Sujay Bhatt, Alec Koppel, Vikram Krishnamurthy:
Policy Gradient using Weak Derivatives for Reinforcement Learning. CoRR abs/2004.04843 (2020) - [i19]Alec Koppel, Hrusikesha Pradhan, Ketan Rajawat:
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck. CoRR abs/2004.11094 (2020) - [i18]Zhan Gao, Alec Koppel, Alejandro Ribeiro:
Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems. CoRR abs/2007.01219 (2020) - [i17]Junyu Zhang, Alec Koppel, Amrit Singh Bedi, Csaba Szepesvári, Mengdi Wang:
Variational Policy Gradient Method for Reinforcement Learning with General Utilities. CoRR abs/2007.02151 (2020) - [i16]Bingjia Wang, Alec Koppel, Vikram Krishnamurthy:
A Markov Decision Process Approach to Active Meta Learning. CoRR abs/2009.04950 (2020) - [i15]Abhishek Chakraborty, Ketan Rajawat, Alec Koppel:
Sparse Representations of Positive Functions via Projected Pseudo-Mirror Descent. CoRR abs/2011.07142 (2020)
2010 – 2019
- 2019
- [j10]Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro:
Parsimonious Online Learning with Kernels via Sparse Projections in Function Space. J. Mach. Learn. Res. 20: 3:1-3:44 (2019) - [j9]Amrit Singh Bedi, Alec Koppel, Ketan Rajawat:
Asynchronous Online Learning in Multi-Agent Systems With Proximity Constraints. IEEE Trans. Signal Inf. Process. over Networks 5(3): 479-494 (2019) - [j8]Amrit Singh Bedi, Alec Koppel, Ketan Rajawat:
Asynchronous Saddle Point Algorithm for Stochastic Optimization in Heterogeneous Networks. IEEE Trans. Signal Process. 67(7): 1742-1757 (2019) - [j7]Alec Koppel, Kaiqing Zhang, Hao Zhu, Tamer Basar:
Projected Stochastic Primal-Dual Method for Constrained Online Learning With Kernels. IEEE Trans. Signal Process. 67(10): 2528-2542 (2019) - [c34]Amrit Singh Bedi, Alec Koppel, Brian M. Sadler, Víctor Elvira:
Compressed Streaming Importance Sampling for Efficient Representations of Localization Distributions. ACSSC 2019: 477-481 - [c33]Alec Koppel:
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck. ACC 2019: 3512-3518 - [c32]Alec Koppel, Amrit S. Bedi, Ketan Rajawat:
Controlling the Bias-Variance Tradeoff via Coherent Risk for Robust Learning with Kernels. ACC 2019: 3519-3525 - [c31]Rishabh Dixit, Amrit Singh Bedi, Ketan Rajawat, Alec Koppel:
Distributed Online Learning over Time-varying Graphs via Proximal Gradient Descent. CDC 2019: 2745-2751 - [c30]Sujay Bhatt, Alec Koppel, Vikram Krishnamurthy:
Policy Gradient using Weak Derivatives for Reinforcement Learning. CDC 2019: 5531-5537 - [c29]Kaiqing Zhang, Alec Koppel, Hao Zhu, Tamer Basar:
Convergence and Iteration Complexity of Policy Gradient Method for Infinite-horizon Reinforcement Learning. CDC 2019: 7415-7422 - [c28]Sujay Bhatt, Alec Koppel, Vikram Krishnamurthy:
Policy Gradient using Weak Derivatives for Reinforcement Learning. CISS 2019: 1-3 - [c27]Kaiqing Zhang, Alec Koppel, Hao Zhu, Tamer Basar:
Policy Search in Infinite-Horizon Discounted Reinforcement Learning: Advances through Connections to Non-Convex Optimization : Invited Presentation. CISS 2019: 1-3 - [i14]Kaiqing Zhang, Alec Koppel, Hao Zhu, Tamer Basar:
Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies. CoRR abs/1906.08383 (2019) - [i13]Amrit Singh Bedi, Alec Koppel, Ketan Rajawat, Brian M. Sadler:
Nonstationary Nonparametric Online Learning: Balancing Dynamic Regret and Model Parsimony. CoRR abs/1909.05442 (2019) - [i12]Alec Koppel, Amrit Singh Bedi, Victor Elvira, Brian M. Sadler:
Approximate Shannon Sampling in Importance Sampling: Nearly Consistent Finite Particle Estimates. CoRR abs/1909.10279 (2019) - [i11]Alec Koppel, Amrit Singh Bedi, Ketan Rajawat, Brian M. Sadler:
Optimally Compressed Nonparametric Online Learning. CoRR abs/1909.11555 (2019) - [i10]Harshat Kumar, Alec Koppel, Alejandro Ribeiro:
On the Sample Complexity of Actor-Critic Method for Reinforcement Learning with Function Approximation. CoRR abs/1910.08412 (2019) - 2018
- [j6]Alec Koppel, Santiago Paternain, Cédric Richard, Alejandro Ribeiro:
Decentralized Online Learning With Kernels. IEEE Trans. Signal Process. 66(12): 3240-3255 (2018) - [c26]Brian Jalaian, Alec Koppel, Andre Harrison, James Michaelis, Stephen Russell:
On Stream-Centric Learning for Internet of Battlefield Things. AAAI Spring Symposia 2018 - [c25]Alec Koppel, Santiago Paternain, Cédric Richard, Alejandro Ribeiro:
Decentralized Online Nonparametric Learning. ACSSC 2018: 2139-2143 - [c24]Ekaterina I. Tolstaya, Alec Koppel, Ethan Stump, Alejandro Ribeiro:
Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision Problems. ACC 2018: 6608-6615 - [c23]Amrit Singh Bedi, Alec Koppel, Ketan Rajawat:
Asynchronous Saddle Point Method: Interference Management Through Pricing. CDC 2018: 3229-3235 - [c22]Kaiqing Zhang, Hao Zhu, Tamer Basar, Alec Koppel:
Projected Stochastic Primal-Dual Method for Constrained Online Learning with Kernels. CDC 2018: 4224-4231 - [c21]Hrusikesha Pradhan, Amrit Singh Bedi, Alec Koppel, Ketan Rajawat:
Exact Nonparametric Decentralized Online Optimization. GlobalSIP 2018: 643-647 - [c20]Alec Koppel, Aryan Mokhtari, Alejandro Ribeiro:
Parallel Stochastic Successive Convex Approximation Method for Large-Scale Dictionary Learning. ICASSP 2018: 2771-2775 - [c19]Ekaterina I. Tolstaya, Ethan Stump, Alec Koppel, Alejandro Ribeiro:
Composable Learning with Sparse Kernel Representations. IROS 2018: 4622-4628 - [i9]Alec Koppel, Ekaterina I. Tolstaya, Ethan Stump, Alejandro Ribeiro:
Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision Problems. CoRR abs/1804.07323 (2018) - 2017
- [j5]Andrea Simonetto, Alec Koppel, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro:
Decentralized Prediction-Correction Methods for Networked Time-Varying Convex Optimization. IEEE Trans. Autom. Control. 62(11): 5724-5738 (2017) - [j4]Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro:
D4L: Decentralized Dynamic Discriminative Dictionary Learning. IEEE Trans. Signal Inf. Process. over Networks 3(4): 728-743 (2017) - [j3]Alec Koppel, Brian M. Sadler, Alejandro Ribeiro:
Proximity Without Consensus in Online Multiagent Optimization. IEEE Trans. Signal Process. 65(12): 3062-3077 (2017) - [c18]Amrit Singh Bedi, Alec Koppel, Ketan Rajawat:
Beyond consensus and synchrony in decentralized online optimization using saddle point method. ACSSC 2017: 293-297 - [c17]Mahyar Fazlyab, Alec Koppel, Victor M. Preciado, Alejandro Ribeiro:
A variational approach to dual methods for constrained convex optimization. ACC 2017: 5269-5275 - [c16]Alec Koppel, Santiago Paternain, Cédric Richard, Alejandro Ribeiro:
Decentralized efficient nonparametric stochastic optimization. GlobalSIP 2017: 533-537 - [c15]Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro:
Parsimonious Online Learning with Kernels via sparse projections in function space. ICASSP 2017: 4671-4675 - [c14]Aryan Mokhtari, Alec Koppel, Gesualdo Scutari, Alejandro Ribeiro:
Large-scale nonconvex stochastic optimization by Doubly Stochastic Successive Convex approximation. ICASSP 2017: 4701-4705 - [i8]Alec Koppel, Santiago Paternain, Cédric Richard, Alejandro Ribeiro:
Decentralized Online Learning with Kernels. CoRR abs/1710.04062 (2017) - 2016
- [j2]Andrea Simonetto, Aryan Mokhtari, Alec Koppel, Geert Leus, Alejandro Ribeiro:
A Class of Prediction-Correction Methods for Time-Varying Convex Optimization. IEEE Trans. Signal Process. 64(17): 4576-4591 (2016) - [c13]Alec Koppel, Aryan Mokhtari, Alejandro Ribeiro:
Doubly stochastic algorithms for large-scale optimization. ACSSC 2016: 1705-1709 - [c12]Aryan Mokhtari, Alec Koppel, Alejandro Ribeiro:
Doubly random parallel stochastic methods for large scale learning. ACC 2016: 4847-4852 - [c11]Andrea Simonetto, Alec Koppel, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro:
A Quasi-newton prediction-correction method for decentralized dynamic convex optimization. ECC 2016: 1934-1939 - [c10]Alec Koppel, Brian M. Sadler, Alejandro Ribeiro:
Decentralized online optimization with heterogeneous data sources. GlobalSIP 2016: 515-519 - [c9]Alec Koppel, Brian M. Sadler, Alejandro Ribeiro:
Proximity without consensus in online multi-agent optimization. ICASSP 2016: 3726-3730 - [c8]Alec Koppel, Jonathan Fink, Garrett Warnell, Ethan Stump, Alejandro Ribeiro:
Online learning for characterizing unknown environments in ground robotic vehicle models. IROS 2016: 626-633 - [i7]Andrea Simonetto, Alec Koppel, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro:
Decentralized Prediction-Correction Methods for Networked Time-Varying Convex Optimization. CoRR abs/1602.01716 (2016) - [i6]