Search Results for author: Ravi Lanka

Found 6 papers, 1 papers with code

Machine Learning Based Path Planning for Improved Rover Navigation (Pre-Print Version)

no code implementations11 Nov 2020 Neil Abcouwer, Shreyansh Daftry, Siddarth Venkatraman, Tyler del Sesto, Olivier Toupet, Ravi Lanka, Jialin Song, Yisong Yue, Masahiro Ono

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.

A General Large Neighborhood Search Framework for Solving Integer Linear Programs

no code implementations NeurIPS 2020 Jialin Song, Ravi Lanka, Yisong Yue, Bistra Dilkina

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.

Combinatorial Optimization

Single Sample Feature Importance: An Interpretable Algorithm for Low-Level Feature Analysis

no code implementations27 Nov 2019 Joseph Gatto, Ravi Lanka, Yumi Iwashita, Adrian Stoica

Have you ever wondered how your feature space is impacting the prediction of a specific sample in your dataset?

Feature Importance

Co-training for Policy Learning

1 code implementation3 Jul 2019 Jialin Song, Ravi Lanka, Yisong Yue, Masahiro Ono

We study the problem of learning sequential decision-making policies in settings with multiple state-action representations.

Combinatorial Optimization Continuous Control +3

PURE: Scalable Phase Unwrapping with Spatial Redundant Arcs

no code implementations19 Apr 2018 Ravi Lanka

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.

Learning to Search via Retrospective Imitation

no code implementations3 Apr 2018 Jialin Song, Ravi Lanka, Albert Zhao, Aadyot Bhatnagar, Yisong Yue, Masahiro Ono

We study the problem of learning a good search policy for combinatorial search spaces.

Imitation Learning

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