Harrison, James

24 publications

ICLR 2025 Bayesian Optimization via Continual Variational Last Layer Training Paul Brunzema, Mikkel Jordahn, John Willes, Sebastian Trimpe, Jasper Snoek, James Harrison

ICLR 2025 Offline Hierarchical Reinforcement Learning via Inverse Optimization Carolin Schmidt, Daniele Gammelli, James Harrison, Marco Pavone, Filipe Rodrigues

NeurIPSW 2024 Applications of Fractional Calculus in Learned Optimization Teodor Alexandru Szente, James Harrison, Mihai Zanfir, Cristian Sminchisescu

TMLR 2024 Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models 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 A Culp, Lechao Xiao, Maxwell Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel

NeurIPS 2024 Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments 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

ICMLW 2024 Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments Siddharth Nayak, Adelmo Morrison Orozco, Marina Ten Have, Jackson Zhang, Vittal Thirumalai, Darren Chen, Aditya Kapoor, Eric Robinson, Karthik Gopalakrishnan, James Harrison, Anuj Mahajan, Brian Ichter, Hamsa Balakrishnan

ICMLW 2024 MAP-THOR: Benchmarking Long-Horizon Multi-Agent Planning Frameworks in Partially Observable Environments Siddharth Nayak, Adelmo Morrison Orozco, Marina Ten Have, Vittal Thirumalai, Jackson Zhang, Darren Chen, Aditya Kapoor, Eric Robinson, Karthik Gopalakrishnan, Brian Ichter, James Harrison, Anuj Mahajan, Hamsa Balakrishnan

NeurIPS 2024 Universal Neural Functionals Allan Zhou, Chelsea Finn, James Harrison

ICLR 2024 Variational Bayesian Last Layers James Harrison, John Willes, Jasper Snoek

NeurIPSW 2024 Variational Last Layers for Bayesian Optimization Paul Brunzema, Mikkel Jordahn, John Willes, Sebastian Trimpe, Jasper Snoek, James Harrison

ICML 2023 Graph Reinforcement Learning for Network Control via Bi-Level Optimization Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone, Filipe Rodrigues, Francisco C. Pereira

L4DC 2023 Hybrid Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems Tobias Enders, James Harrison, Marco Pavone, Maximilian Schiffer

NeurIPSW 2023 Towards General-Purpose In-Context Learning Agents Louis Kirsch, James Harrison, C. Daniel Freeman, Jascha Sohl-Dickstein, Jürgen Schmidhuber

NeurIPSW 2023 Towards General-Purpose In-Context Learning Agents Louis Kirsch, James Harrison, C. Daniel Freeman, Jascha Sohl-Dickstein, Jürgen Schmidhuber

NeurIPSW 2023 Towards General-Purpose In-Context Learning Agents Louis Kirsch, James Harrison, C. Freeman, Jascha Sohl-Dickstein, Jürgen Schmidhuber

NeurIPSW 2023 Towards General-Purpose In-Context Learning Agents Louis Kirsch, James Harrison, C. Freeman, Jascha Sohl-Dickstein, Jürgen Schmidhuber

NeurIPS 2023 Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies Oscar Li, James Harrison, Jascha Sohl-Dickstein, Virginia Smith, Luke Metz

NeurIPS 2022 A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases James Harrison, Luke Metz, Jascha Sohl-Dickstein

NeurIPSW 2022 Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning Boris Ivanovic, James Harrison, Marco Pavone

NeurIPSW 2022 General-Purpose In-Context Learning by Meta-Learning Transformers Louis Kirsch, James Harrison, Jascha Sohl-Dickstein, Luke Metz

CoLLAs 2022 Practical Tradeoffs Between Memory, Compute, and Performance in Learned Optimizers Luke Metz, C. Daniel Freeman, James Harrison, Niru Maheswaranathan, Jascha Sohl-dickstein

ICML 2021 Deep Reinforcement Learning Amidst Continual Structured Non-Stationarity Annie Xie, James Harrison, Chelsea Finn

NeurIPS 2020 Continuous Meta-Learning Without Tasks James Harrison, Apoorva Sharma, Chelsea Finn, Marco Pavone

ICMLW 2020 Deep Reinforcement Learning Amidst Lifelong Non-Stationarity Annie Xie, James Harrison, Chelsea Finn