1 code implementation • 5 Mar 2024 • Dharmesh Tailor, Aditya Patra, Rajeev Verma, Putra Manggala, Eric Nalisnick
The learning to defer (L2D) framework allows autonomous systems to be safe and robust by allocating difficult decisions to a human expert.
1 code implementation • 30 Oct 2023 • Peter Nickl, Lu Xu, Dharmesh Tailor, Thomas Möllenhoff, Mohammad Emtiyaz Khan
Understanding model's sensitivity to its training data is crucial but can also be challenging and costly, especially during training.
1 code implementation • 27 Jun 2023 • Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric Nalisnick
Neural Processes (NPs) are appealing due to their ability to perform fast adaptation based on a context set.
no code implementations • 7 Jan 2019 • Dharmesh Tailor, Dario Izzo
By substituting expert demonstrations for optimal behaviours, the same paradigm leads to the design of control policies closely approximating the optimal state-feedback.
1 code implementation • 6 Dec 2018 • Dario Izzo, Dharmesh Tailor, Thomas Vasileiou
Recent work have shown how the optimal state-feedback, obtained as the solution to the Hamilton-Jacobi-Bellman equations, can be approximated for several nonlinear, deterministic systems by deep neural networks.
no code implementations • 1 Feb 2018 • Dario Izzo, Christopher Sprague, Dharmesh Tailor
After providing a brief historical overview on the synergies between artificial intelligence research, in the areas of evolutionary computations and machine learning, and the optimal design of interplanetary trajectories, we propose and study the use of deep artificial neural networks to represent, on-board, the optimal guidance profile of an interplanetary mission.