Search Results for author:
Found 6 papers, 1 papers with code
Co-training for Policy Learning
We study the problem of learning sequential decision-making policies in settings with multiple state-action representations.
Learning to Search via Retrospective Imitation
We study the problem of learning a good search policy for combinatorial search spaces.
PURE: Scalable Phase Unwrapping with Spatial Redundant Arcs
We also propose a decomposition technique that exploits the underlying graph structure for solving the sub-problems efficiently and guarantees asymptotic convergence to the globally optimal solution.
Single Sample Feature Importance: An Interpretable Algorithm for Low-Level Feature Analysis
Have you ever wondered how your feature space is impacting the prediction of a specific sample in your dataset?
A General Large Neighborhood Search Framework for Solving Integer Linear Programs
This paper studies a strategy for data-driven algorithm design for large-scale combinatorial optimization problems that can leverage existing state-of-the-art solvers in general purpose ways.
Machine Learning Based Path Planning for Improved Rover Navigation (Pre-Print Version)
Enhanced AutoNav (ENav), the baseline surface navigation software for NASA's Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe.
