Search Results for author: Jialin Song

Found 11 papers, 2 papers with code

MLNav: Learning to Safely Navigate on Martian Terrains

no code implementations9 Mar 2022 Shreyansh Daftry, Neil Abcouwer, Tyler del Sesto, Siddarth Venkatraman, Jialin Song, Lucas Igel, Amos Byon, Ugo Rosolia, Yisong Yue, Masahiro Ono

We present MLNav, a learning-enhanced path planning framework for safety-critical and resource-limited systems operating in complex environments, such as rovers navigating on Mars.

Learning Pseudo-Backdoors for Mixed Integer Programs

no code implementations9 Jun 2021 Aaron Ferber, Jialin Song, Bistra Dilkina, Yisong Yue

In addition, we compare our learned approach against Gurobi, a state-of-the-art MIP solver, demonstrating that our method can be used to improve solver performance.

Combinatorial Optimization

Learning to Make Decisions via Submodular Regularization

no code implementations ICLR 2021 Ayya Alieva, Aiden Aceves, Jialin Song, Stephen Mayo, Yisong Yue, Yuxin Chen

In particular, we focus on a class of combinatorial problems that can be solved via submodular maximization (either directly on the objective function or via submodular surrogates).

Active Learning Combinatorial Optimization +2

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

Policy Optimization by Local Improvement through Search

no code implementations25 Sep 2019 Jialin Song, Joe Wenjie Jiang, Amir Yazdanbakhsh, Ebrahim Songhori, Anna Goldie, Navdeep Jaitly, Azalia Mirhoseini

On the other end of the spectrum, approaches rooted in Policy Iteration, such as Dual Policy Iteration do not choose next step actions based on an expert, but instead use planning or search over the policy to choose an action distribution to train towards.

Imitation Learning reinforcement-learning

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

Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes

no code implementations15 Nov 2018 Jialin Song, Yury S. Tokpanov, Yuxin Chen, Dagny Fleischman, Kate T. Fountaine, Harry A. Atwater, Yisong Yue

We apply numerical methods in combination with finite-difference-time-domain (FDTD) simulations to optimize transmission properties of plasmonic mirror color filters using a multi-objective figure of merit over a five-dimensional parameter space by utilizing novel multi-fidelity Gaussian processes approach.

Gaussian Processes

A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes

no code implementations2 Nov 2018 Jialin Song, Yuxin Chen, Yisong Yue

How can we efficiently gather information to optimize an unknown function, when presented with multiple, mutually dependent information sources with different costs?

Gaussian Processes

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

Onsets and Frames: Dual-Objective Piano Transcription

1 code implementation30 Oct 2017 Curtis Hawthorne, Erich Elsen, Jialin Song, Adam Roberts, Ian Simon, Colin Raffel, Jesse Engel, Sageev Oore, Douglas Eck

We advance the state of the art in polyphonic piano music transcription by using a deep convolutional and recurrent neural network which is trained to jointly predict onsets and frames.

Music Transcription

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