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2020 – today
- 2024
[c10]Andrew Silva
, Pradyumna Tambwekar, Mariah Schrum, Matthew C. Gombolay:
Towards Balancing Preference and Performance through Adaptive Personalized Explainability. HRI 2024: 658-668- 2023
- [j6]Andrew Silva, Mariah Schrum, Erin Hedlund-Botti, Nakul Gopalan, Matthew C. Gombolay:
Explainable Artificial Intelligence: Evaluating the Objective and Subjective Impacts of xAI on Human-Agent Interaction. Int. J. Hum. Comput. Interact. 39(7): 1390-1404 (2023) - [j5]Pradyumna Tambwekar, Andrew Silva, Nakul Gopalan, Matthew C. Gombolay:
Natural Language Specification of Reinforcement Learning Policies Through Differentiable Decision Trees. IEEE Robotics Autom. Lett. 8(6): 3621-3628 (2023) - [c9]Andrew Silva, Pradyumna Tambwekar, Matthew C. Gombolay:
FedPerC: Federated Learning for Language Generation with Personal and Context Preference Embeddings. EACL (Findings) 2023: 839-852 - [i13]Rohan R. Paleja, Letian Chen, Yaru Niu, Andrew Silva, Zhaoxin Li, Songan Zhang, Chace Ritchie, Sugju Choi, Kimberlee Chestnut Chang, Hongtei Eric Tseng, Yan Wang, Subramanya Nageshrao, Matthew C. Gombolay:
Interpretable Reinforcement Learning for Robotics and Continuous Control. CoRR abs/2311.10041 (2023) - 2022
- [j4]Esmaeil Seraj, Andrew Silva, Matthew C. Gombolay:
Multi-UAV planning for cooperative wildfire coverage and tracking with quality-of-service guarantees. Auton. Agents Multi Agent Syst. 36(2): 39 (2022) - [j3]Andrew Silva, Nina Moorman, William Silva, Zulfiqar Zaidi, Nakul Gopalan, Matthew C. Gombolay:
LanCon-Learn: Learning With Language to Enable Generalization in Multi-Task Manipulation. IEEE Robotics Autom. Lett. 7(2): 1635-1642 (2022) - [c8]Andrew Silva, Rohit Chopra, Matthew C. Gombolay:
Cross-Loss Influence Functions to Explain Deep Network Representations. AISTATS 2022: 1-17 - [c7]Rohan R. Paleja, Yaru Niu, Andrew Silva, Chace Ritchie, Sugju Choi, Matthew C. Gombolay:
Learning Interpretable, High-Performing Policies for Autonomous Driving. Robotics: Science and Systems 2022 - [i12]Rohan R. Paleja, Yaru Niu, Andrew Silva, Chace Ritchie, Sugju Choi, Matthew C. Gombolay:
Learning Interpretable, High-Performing Policies for Continuous Control Problems. CoRR abs/2202.02352 (2022) - [i11]Andrew Silva, Katherine Metcalf, Nicholas Apostoloff, Barry-John Theobald:
FedEmbed: Personalized Private Federated Learning. CoRR abs/2202.09472 (2022) - [i10]Esmaeil Seraj, Andrew Silva, Matthew C. Gombolay:
Multi-UAV Planning for Cooperative Wildfire Coverage and Tracking with Quality-of-Service Guarantees. CoRR abs/2206.10544 (2022) - [i9]Andrew Silva, Pradyumna Tambwekar, Matthew C. Gombolay:
FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddings. CoRR abs/2210.03766 (2022) - 2021
- [c6]Andrew Silva, Matthew C. Gombolay:
Encoding Human Domain Knowledge to Warm Start Reinforcement Learning. AAAI 2021: 5042-5050 - [c5]Andrew Silva, Barry-John Theobald, Nicholas Apostoloff:
Multimodal Punctuation Prediction with Contextual Dropout. ICASSP 2021: 3980-3984 - [c4]Andrew Silva, Pradyumna Tambwekar, Matthew C. Gombolay:
Towards a Comprehensive Understanding and Accurate Evaluation of Societal Biases in Pre-Trained Transformers. NAACL-HLT 2021: 2383-2389 - [i8]Pradyumna Tambwekar, Andrew Silva, Nakul Gopalan, Matthew C. Gombolay:
Interpretable Policy Specification and Synthesis through Natural Language and RL. CoRR abs/2101.07140 (2021) - [i7]Andrew Silva, Barry-John Theobald, Nicholas Apostoloff:
Multimodal Punctuation Prediction with Contextual Dropout. CoRR abs/2102.11012 (2021) - 2020
- [c3]Andrew Silva, Matthew C. Gombolay, Taylor W. Killian, Ivan Dario Jimenez Jimenez, Sung-Hyun Son:
Optimization Methods for Interpretable Differentiable Decision Trees Applied to Reinforcement Learning. AISTATS 2020: 1855-1865 - [c2]Rohan R. Paleja, Andrew Silva, Letian Chen, Matthew C. Gombolay:
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations. NeurIPS 2020 - [i6]Andrew Silva, Rohit Chopra, Matthew C. Gombolay:
Using Cross-Loss Influence Functions to Explain Deep Network Representations. CoRR abs/2012.01685 (2020)
2010 – 2019
- 2019
- [c1]Andrew Silva, Sonia Chernova:
Unsupervised Role Discovery Using Temporal Observations of Agents. AAMAS 2019: 1627-1634 - [i5]Meera Hahn, Andrew Silva, James M. Rehg:
Action2Vec: A Crossmodal Embedding Approach to Action Learning. CoRR abs/1901.00484 (2019) - [i4]Andrew Silva, Matthew C. Gombolay:
ProLoNets: Neural-encoding Human Experts' Domain Knowledge to Warm Start Reinforcement Learning. CoRR abs/1902.06007 (2019) - [i3]Esmaeil Seraj, Andrew Silva, Matthew C. Gombolay:
Safe Coordination of Human-Robot Firefighting Teams. CoRR abs/1903.06847 (2019) - [i2]Rohan R. Paleja, Andrew Silva, Matthew C. Gombolay:
Personalized Apprenticeship Learning from Heterogeneous Decision-Makers. CoRR abs/1906.06397 (2019) - 2018
- [j2]Siddhartha Banerjee, Andrew Silva, Sonia Chernova:
Robot Classification of Human Interruptibility and a Study of Its Effects. ACM Trans. Hum. Robot Interact. 7(2): 14:1-14:35 (2018) - [i1]Siddhartha Banerjee, Andrew Silva, Karen M. Feigh, Sonia Chernova:
Effects of Interruptibility-Aware Robot Behavior. CoRR abs/1804.06383 (2018)
2000 – 2009
- 2005
- [j1]Jessi E. Johnson, Andrew Silva, George R. Branner:
Analysis of Nonlinear Input Impedance Matching in Active Frequency Multipliers. IEICE Trans. Inf. Syst. 88-D(7): 1409-1416 (2005)
Coauthor Index
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