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2020 – today
- 2024
[i17]Parham Mohammad Panahi, Andrew Patterson, Martha White, Adam White:
Investigating the Interplay of Prioritized Replay and Generalization. CoRR abs/2407.09702 (2024)- [i16]Gage MacLin, Venanzio Cichella, Andrew Patterson, Michael J. Acheson, Irene M. Gregory:
Optimal Control using Composite Bernstein Approximants. CoRR abs/2407.18081 (2024) - [i15]Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy, Martha White, Adam White:
The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning. CoRR abs/2407.18840 (2024) - 2023
- [j4]Andrew Patterson
, Victor Liao, Martha White:
Robust Losses for Learning Value Functions. IEEE Trans. Pattern Anal. Mach. Intell. 45(5): 6157-6167 (2023) - [i14]Andrew Patterson, Samuel Neumann, Martha White, Adam White:
Empirical Design in Reinforcement Learning. CoRR abs/2304.01315 (2023) - [i13]Vincent Liu, Prabhat Nagarajan, Andrew Patterson, Martha White:
When is Offline Policy Selection Sample Efficient for Reinforcement Learning? CoRR abs/2312.02355 (2023) - 2022
- [j3]Andrew Patterson, Adam White, Martha White:
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. J. Mach. Learn. Res. 23: 145:1-145:61 (2022) - [c13]Samuele Tosatto, Andrew Patterson, Martha White, Rupam Mahmood:
A Temporal-Difference Approach to Policy Gradient Estimation. ICML 2022: 21609-21632 - [i12]Samuele Tosatto, Andrew Patterson, Martha White, A. Rupam Mahmood:
A Temporal-Difference Approach to Policy Gradient Estimation. CoRR abs/2202.02396 (2022) - [i11]Andrew Patterson, Victor Liao, Martha White:
Robust Losses for Learning Value Functions. CoRR abs/2205.08464 (2022) - 2021
- [j2]Matthew Schlegel, Andrew Jacobsen, Zaheer Abbas, Andrew Patterson, Adam White, Martha White:
General Value Function Networks. J. Artif. Intell. Res. 70: 497-543 (2021) - [c12]Aditya Gahlawat, Arun Lakshmanan, Lin Song, Andrew Patterson, Zhuohuan Wu, Naira Hovakimyan, Evangelos A. Theodorou:
Contraction ℒ1-Adaptive Control using Gaussian Processes. L4DC 2021: 1027-1040 - [i10]Andrew Patterson, Adam White, Sina Ghiassian, Martha White:
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. CoRR abs/2104.13844 (2021) - 2020
- [c11]Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White:
Gradient Temporal-Difference Learning with Regularized Corrections. ICML 2020: 3524-3534 - [c10]Aditya Gahlawat, Pan Zhao, Andrew Patterson, Naira Hovakimyan, Evangelos A. Theodorou:
L1-GP: L1 Adaptive Control with Bayesian Learning. L4DC 2020: 826-837 - [c9]Yi-Qi Lin, Andrew Patterson:
Design Solutions for 5G Power Amplifiers using 0.15μm and 0.25μm GaN HEMTs. VLSI-DAT 2020: 1-3 - [i9]Andrew Patterson, Aditya Gahlawat, Naira Hovakimyan:
Learning Probabilistic Intersection Traffic Models for Trajectory Prediction. CoRR abs/2002.01965 (2020) - [i8]Aditya Gahlawat, Pan Zhao, Andrew Patterson, Naira Hovakimyan, Evangelos A. Theodorou:
$\mathcal{L}_1$-$\mathcal{GP}$: $\mathcal{L}_1$ Adaptive Control with Bayesian Learning. CoRR abs/2004.14594 (2020) - [i7]Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White:
Gradient Temporal-Difference Learning with Regularized Corrections. CoRR abs/2007.00611 (2020) - [i6]Aditya Gahlawat, Arun Lakshmanan, Lin Song, Andrew Patterson, Zhuohuan Wu, Naira Hovakimyan, Evangelos A. Theodorou:
RL1-GP: Safe Simultaneous Learning and Control. CoRR abs/2009.03864 (2020)
2010 – 2019
- 2019
- [c8]Andrew Patterson, Arun Lakshmanan, Naira Hovakimyan:
Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification. CDC 2019: 3827-3832 - [c7]Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell, Franco Pestilli, Cesar F. Caiafa, Russell Greiner, Martha White:
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization. NeurIPS 2019: 8847-8857 - [c6]Arun Lakshmanan, Andrew Patterson, Venanzio Cichella, Naira Hovakimyan:
Proximity Queries for Absolutely Continuous Parametric Curves. Robotics: Science and Systems 2019 - [i5]Arun Lakshmanan, Andrew Patterson, Venanzio Cichella, Naira Hovakimyan:
Proximity Queries for Absolutely Continuous Parametric Curves. CoRR abs/1902.05027 (2019) - [i4]Andrew Patterson, Arun Lakshmanan, Naira Hovakimyan:
Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification. CoRR abs/1904.02765 (2019) - 2018
- [c5]Yangchen Pan, Muhammad Zaheer, Adam White, Andrew Patterson, Martha White:
Organizing Experience: a Deeper Look at Replay Mechanisms for Sample-Based Planning in Continuous State Domains. IJCAI 2018: 4794-4800 - [c4]Lei Le, Andrew Patterson, Martha White:
Supervised autoencoders: Improving generalization performance with unsupervised regularizers. NeurIPS 2018: 107-117 - [i3]Yangchen Pan, Muhammad Zaheer, Adam White, Andrew Patterson, Martha White:
Organizing Experience: A Deeper Look at Replay Mechanisms for Sample-based Planning in Continuous State Domains. CoRR abs/1806.04624 (2018) - [i2]Matthew Schlegel, Adam White, Andrew Patterson, Martha White:
General Value Function Networks. CoRR abs/1807.06763 (2018) - [i1]Sina Ghiassian, Andrew Patterson, Martha White, Richard S. Sutton, Adam White:
Online Off-policy Prediction. CoRR abs/1811.02597 (2018) - 2014
- [c3]Andrew James McMinn, Daniel Tsvetkov, Tsvetan R. Yordanov, Andrew Patterson, Rrobi Szk, Jesus A. Rodriguez Perez, Joemon M. Jose:
An interactive interface for visualizing events on Twitter. SIGIR 2014: 1271-1272
2000 – 2009
- 2004
- [c2]Michael Kölling, Andrew Patterson:
Going Interactive: Combining Ad-Hoc and Regression Testing. XP 2004: 270-273 - 2003
- [j1]Michael Kölling, Bruce Quig, Andrew Patterson, John Rosenberg:
The BlueJ System and its Pedagogy. Comput. Sci. Educ. 13(4): 249-268 (2003) - [c1]Andrew Patterson, Michael Kölling, John Rosenberg:
Introducing unit testing with BlueJ. ITiCSE 2003: 11-15
Coauthor Index
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last updated on 2024-10-07 21:17 CEST by the dblp team
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