Articles with public access mandates - Alec KoppelLearn more
Not available anywhere: 9
A variational approach to dual methods for constrained convex optimization
M Fazlyab, A Koppel, VM Preciado, A Ribeiro
2017 American Control Conference (ACC), 5269-5275, 2017
Mandates: US National Science Foundation, US Department of Defense
Optimally compressed nonparametric online learning: Tradeoffs between memory and consistency
A Koppel, AS Bedi, K Rajawat, BM Sadler
IEEE Signal Processing Magazine 37 (3), 61-70, 2020
Mandates: US Department of Defense
On submodular set cover problems for near-optimal online kernel basis selection
H Pradhan, A Koppel, K Rajawat
ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022
Mandates: US Department of Defense
Escaping saddle points for successive convex approximation
AS Bedi, K Rajawat, V Aggarwal, A Koppel
IEEE Transactions on Signal Processing 70, 307-321, 2021
Mandates: US Department of Defense
Semiparametric information state embedding for policy search under imperfect information
S Bhatt, W Mao, A Koppel, T Başar
2021 60th IEEE Conference on Decision and Control (CDC), 4501-4506, 2021
Mandates: US Department of Defense
Decentralized Multi-agent Exploration with Limited Inter-agent Communications
HJ He, A Koppel, AS Bedi, DJ Stilwell, M Farhood, B Biggs
2023 IEEE International Conference on Robotics and Automation (ICRA), 5530-5536, 2023
Mandates: US Department of Defense
Balancing rates and variance via adaptive batch-sizes in first-order stochastic optimization
Z Gao, A Koppel, A Ribeiro
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020
Mandates: US National Science Foundation, US Department of Defense
Bi-Level Nonstationary Kernels for Online Gaussian Process Regression
HJ He, A Koppel, AS Bedi, M Farhood, DJ Stilwell
2023 IEEE 19th International Conference on Automation Science and …, 2023
Mandates: US Department of Defense
Policy Gradient for Ratio Optimization: A Case Study
WA Suttle, A Koppel, J Liu
2022 56th Annual Conference on Information Sciences and Systems (CISS), 281-286, 2022
Mandates: US Department of Defense
Available somewhere: 41
Global convergence of policy gradient methods to (almost) locally optimal policies
K Zhang, A Koppel, H Zhu, T Basar
SIAM Journal on Control and Optimization 58 (6), 3586-3612, 2020
Mandates: US National Science Foundation, US Department of Defense
Variational policy gradient method for reinforcement learning with general utilities
J Zhang, A Koppel, AS Bedi, C Szepesvari, M Wang
Advances in Neural Information Processing Systems 33, 4572-4583, 2020
Mandates: US National Science Foundation, US Department of Defense, Natural Sciences …
On the sample complexity of actor-critic method for reinforcement learning with function approximation
H Kumar, A Koppel, A Ribeiro
Machine Learning 112 (7), 2433-2467, 2023
Mandates: US Department of Defense
Proximity without consensus in online multi-agent optimization
A Koppel, BM Sadler, A Ribeiro
Proc. Int. Conf. Accoustics Speech Signal Proces (submitted),, 2016
Mandates: US National Science Foundation
Decentralized online learning with kernels
A Koppel, S Paternain, C Richard, A Ribeiro
IEEE Transactions on Signal Processing 66 (12), 3240-3255, 2018
Mandates: US National Science Foundation, US Department of Defense
Parsimonious online learning with kernels via sparse projections in function space
A Koppel, G Warnell, E Stump, A Ribeiro
The Journal of Machine Learning Research 20 (1), 83-126, 2019
Mandates: US National Science Foundation, US Department of Defense
Parsimonious online learning with kernels via sparse projections in function space
A Koppel, G Warnell, E Stump, A Ribeiro
2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017
Mandates: US National Science Foundation, US Department of Defense
Consistent online gaussian process regression without the sample complexity bottleneck
A Koppel, H Pradhan, K Rajawat
Statistics and Computing 31, 1-18, 2021
Mandates: US Department of Defense
Asynchronous and parallel distributed pose graph optimization
Y Tian, A Koppel, AS Bedi, JP How
IEEE Robotics and Automation Letters 5 (4), 5819-5826, 2020
Mandates: US National Aeronautics and Space Administration
Policy Evaluation in Continuous MDPs with Efficient Kernelized Gradient Temporal Difference
A Koppel, G Warnell, E Stump, P Stone, A Ribeiro.
IEEE Transactions on Automatic Control 66 (4), 2020
Mandates: US National Science Foundation
Cautious reinforcement learning via distributional risk in the dual domain
J Zhang, AS Bedi, M Wang, A Koppel
IEEE Journal on Selected Areas in Information Theory 2 (2), 611-626, 2021
Mandates: US Department of Defense
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