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2020 – today
- 2024
[j5]John Willes
, James Harrison, Ali Harakeh, Chelsea Finn, Marco Pavone, Steven L. Waslander:
Bayesian Embeddings for Few-Shot Open World Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 46(3): 1513-1529 (2024)- [j4]Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron T. Parisi, Abhishek Kumar, Alexander A. Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel:
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. Trans. Mach. Learn. Res. 2024 (2024) - [c22]James Harrison, John Willes, Jasper Snoek:
Variational Bayesian Last Layers. ICLR 2024 - [i30]Allan Zhou, Chelsea Finn, James Harrison:
Universal Neural Functionals. CoRR abs/2402.05232 (2024) - [i29]Tobias Enders, James Harrison, Maximilian Schiffer:
Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution Shifts. CoRR abs/2402.09992 (2024) - [i28]James Harrison, John Willes, Jasper Snoek:
Variational Bayesian Last Layers. CoRR abs/2404.11599 (2024) - [i27]Siddharth Nayak, Adelmo Morrison Orozco, Marina Ten Have, Vittal Thirumalai, Jackson Zhang, Darren Chen, Aditya Kapoor, Eric Robinson, Karthik Gopalakrishnan, James Harrison, Brian Ichter, Anuj Mahajan, Hamsa Balakrishnan:
Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments. CoRR abs/2407.10031 (2024) - 2023
- [j3]James Blundell, Charlotte Collins, Rod Sears, Tassos Plioutsias, John Huddlestone, Don Harris, James Harrison, Anthony Kershaw, Paul Harrison, Phil Lamb:
Multivariate Analysis of Gaze Behavior and Task Performance Within Interface Design Evaluation. IEEE Trans. Hum. Mach. Syst. 53(5): 875-884 (2023) - [c21]Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone, Filipe Rodrigues, Francisco C. Pereira:
Graph Reinforcement Learning for Network Control via Bi-Level Optimization. ICML 2023: 10587-10610 - [c20]Boris Ivanovic, James Harrison, Marco Pavone:
Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning. ICRA 2023: 7786-7793 - [c19]Tobias Enders, James Harrison, Marco Pavone, Maximilian Schiffer:
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems. L4DC 2023: 1284-1296 - [c18]Oscar Li, James Harrison, Jascha Sohl-Dickstein, Virginia Smith, Luke Metz:
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies. NeurIPS 2023 - [i26]Oscar Li, James Harrison, Jascha Sohl-Dickstein, Virginia Smith, Luke Metz:
Noise-Reuse in Online Evolution Strategies. CoRR abs/2304.12180 (2023) - [i25]Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone, Filipe Rodrigues, Francisco C. Pereira:
Graph Reinforcement Learning for Network Control via Bi-Level Optimization. CoRR abs/2305.09129 (2023) - [i24]Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin F. Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel:
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. CoRR abs/2312.06585 (2023) - 2022
- [j2]Thomas Lew, Apoorva Sharma, James Harrison, Andrew Bylard, Marco Pavone:
Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework. IEEE Trans. Robotics 38(5): 2888-2907 (2022) - [c17]Rohan Sinha, James Harrison, Spencer M. Richards, Marco Pavone:
Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty. ACC 2022: 906-913 - [c16]Luke Metz, C. Daniel Freeman, James Harrison, Niru Maheswaranathan, Jascha Sohl-Dickstein:
Practical Tradeoffs between Memory, Compute, and Performance in Learned Optimizers. CoLLAs 2022: 142-164 - [c15]Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues, Francisco C. Pereira, Marco Pavone:
Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand. KDD 2022: 2913-2923 - [c14]James Harrison, Luke Metz, Jascha Sohl-Dickstein:
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases. NeurIPS 2022 - [i23]Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues, Francisco C. Pereira, Marco Pavone:
Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand. CoRR abs/2202.07147 (2022) - [i22]Luke Metz, C. Daniel Freeman, James Harrison, Niru Maheswaranathan, Jascha Sohl-Dickstein:
Practical tradeoffs between memory, compute, and performance in learned optimizers. CoRR abs/2203.11860 (2022) - [i21]James Harrison, Luke Metz, Jascha Sohl-Dickstein:
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases. CoRR abs/2209.11208 (2022) - [i20]Boris Ivanovic, James Harrison, Marco Pavone:
Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning. CoRR abs/2209.11820 (2022) - [i19]Luke Metz, James Harrison, C. Daniel Freeman, Amil Merchant, Lucas Beyer, James Bradbury, Naman Agrawal, Ben Poole, Igor Mordatch, Adam Roberts, Jascha Sohl-Dickstein:
VeLO: Training Versatile Learned Optimizers by Scaling Up. CoRR abs/2211.09760 (2022) - [i18]Rohan Sinha, James Harrison, Spencer M. Richards, Marco Pavone:
Adaptive Robust Model Predictive Control via Uncertainty Cancellation. CoRR abs/2212.01371 (2022) - [i17]Louis Kirsch, James Harrison, Jascha Sohl-Dickstein, Luke Metz:
General-Purpose In-Context Learning by Meta-Learning Transformers. CoRR abs/2212.04458 (2022) - [i16]Tobias Enders, James Harrison, Marco Pavone, Maximilian Schiffer:
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems. CoRR abs/2212.07313 (2022) - 2021
- [j1]Sandeep Chinchali, Apoorva Sharma, James Harrison, Amine Elhafsi, Daniel Kang, Evgenya Pergament, Eyal Cidon, Sachin Katti, Marco Pavone:
Network offloading policies for cloud robotics: a learning-based approach. Auton. Robots 45(7): 997-1012 (2021) - [c13]Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues, Francisco C. Pereira, Marco Pavone:
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems. CDC 2021: 2996-3003 - [c12]Annie Xie, James Harrison, Chelsea Finn:
Deep Reinforcement Learning amidst Continual Structured Non-Stationarity. ICML 2021: 11393-11403 - [c11]Robert Dyro, James Harrison, Apoorva Sharma, Marco Pavone:
Particle MPC for Uncertain and Learning-Based Control. IROS 2021: 7127-7134 - [i15]Robert Dyro, James Harrison, Apoorva Sharma, Marco Pavone:
Particle MPC for Uncertain and Learning-Based Control. CoRR abs/2104.02213 (2021) - [i14]Rohan Sinha, James Harrison, Spencer M. Richards, Marco Pavone:
Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty. CoRR abs/2104.08261 (2021) - [i13]Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues, Francisco C. Pereira, Marco Pavone:
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems. CoRR abs/2104.11434 (2021) - [i12]John Willes, James Harrison, Ali Harakeh, Chelsea Finn, Marco Pavone, Steven Lake Waslander:
Bayesian Embeddings for Few-Shot Open World Recognition. CoRR abs/2107.13682 (2021) - [i11]Thomas Lew, Apoorva Sharma, James Harrison, Edward Schmerling, Marco Pavone:
On the Problem of Reformulating Systems with Uncertain Dynamics as a Stochastic Differential Equation. CoRR abs/2111.06084 (2021) - 2020
- [c10]James Harrison, Apoorva Sharma, Chelsea Finn, Marco Pavone:
Continuous Meta-Learning without Tasks. NeurIPS 2020 - [i10]Annie Xie, James Harrison, Chelsea Finn:
Deep Reinforcement Learning amidst Lifelong Non-Stationarity. CoRR abs/2006.10701 (2020) - [i9]Thomas Lew, Apoorva Sharma, James Harrison, Marco Pavone:
Safe Model-Based Meta-Reinforcement Learning: A Sequential Exploration-Exploitation Framework. CoRR abs/2008.11700 (2020) - [i8]Somrita Banerjee, James Harrison, P. Michael Furlong, Marco Pavone:
Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics. CoRR abs/2009.10191 (2020)
2010 – 2019
- 2019
- [c9]Apoorva Sharma, James Harrison, Matthew Tsao, Marco Pavone:
Robust and Adaptive Planning under Model Uncertainty. ICAPS 2019: 410-418 - [c8]Boris Ivanovic, James Harrison, Apoorva Sharma, Mo Chen, Marco Pavone:
BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning. ICRA 2019: 15-21 - [c7]Sandeep Chinchali, Apoorva Sharma, James Harrison, Amine Elhafsi, Daniel Kang, Evgenya Pergament, Eyal Cidon, Sachin Katti, Marco Pavone:
Network Offloading Policies for Cloud Robotics: A Learning-Based Approach. Robotics: Science and Systems 2019 - [i7]Apoorva Sharma, James Harrison, Matthew Tsao, Marco Pavone:
Robust and Adaptive Planning under Model Uncertainty. CoRR abs/1901.02577 (2019) - [i6]Sandeep Chinchali, Apoorva Sharma, James Harrison, Amine Elhafsi, Daniel Kang, Evgenya Pergament, Eyal Cidon, Sachin Katti, Marco Pavone:
Network Offloading Policies for Cloud Robotics: a Learning-based Approach. CoRR abs/1902.05703 (2019) - [i5]James Harrison, Apoorva Sharma, Chelsea Finn, Marco Pavone:
Continuous Meta-Learning without Tasks. CoRR abs/1912.08866 (2019) - 2018
- [c6]Brian Ichter, James Harrison, Marco Pavone:
Learning Sampling Distributions for Robot Motion Planning. ICRA 2018: 7087-7094 - [c5]James Harrison, Apoorva Sharma, Marco Pavone:
Meta-learning Priors for Efficient Online Bayesian Regression. WAFR 2018: 318-337 - [i4]Boris Ivanovic, James Harrison, Apoorva Sharma, Mo Chen, Marco Pavone:
BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning. CoRR abs/1806.06161 (2018) - [i3]James Harrison, Apoorva Sharma, Marco Pavone:
Meta-Learning Priors for Efficient Online Bayesian Regression. CoRR abs/1807.08912 (2018) - 2017
- [c4]James Harrison, Animesh Garg, Boris Ivanovic, Yuke Zhu, Silvio Savarese, Li Fei-Fei, Marco Pavone:
AdaPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems. ISRR 2017: 437-453 - [i2]James Harrison, Animesh Garg, Boris Ivanovic, Yuke Zhu, Silvio Savarese, Li Fei-Fei, Marco Pavone:
ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems. CoRR abs/1707.04674 (2017) - [i1]Brian Ichter, James Harrison, Marco Pavone:
Learning Sampling Distributions for Robot Motion Planning. CoRR abs/1709.05448 (2017) - 2015
- [c3]Colin R. Gallacher, James Harrison, József Kövecses:
Characterizing device dynamics for haptic manipulation and navigation. ICRA 2015: 3689-3695
2000 – 2009
- 2004
- [c2]Kevin J. Mitchell, Michael J. Becich, Jules J. Berman, Wendy W. Chapman, John R. Gilbertson, Dilip Gupta, James Harrison, Elizabeth Legowski, Rebecca S. Crowley:
Implementation and Evaluation of a Negation Tagger in a Pipeline-based System for Information Extraction from Pathology Reports. MedInfo 2004: 663-667
1990 – 1999
- 1999
- [c1]Mark Derthick, James Harrison, Andrew Moore, Steven F. Roth:
Efficient Multi-Object Dynamic Query Histograms. INFOVIS 1999: 84-91
Coauthor Index
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