Conference Publications

CircuitVAE: Efficient and Scalable Latent Circuit Optimization
Jialin Song*, Aidan Swope*, Robert Kirby, Rajarshi Roy, Saad Godil, Jonathan Raiman, Bryan Catanzaro
Design Automation Conference (DAC) 2024

Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
Fengxue Zhang, Jialin Song, James C Bowden, Alexander Ladd, Yisong Yue, Thomas Desautels, Yuxin Chen
[pdf]
ICML 2023

MLNav: Learning to Safely Navigate on Martian Terrains
Shreyansh Daftry, Neil Abcouwer, Tyler Del Sesto, Siddarth Venkatraman, Jialin Song, Lucas Igel, Amos Byon, Ugo Rosolia, Yisong Yue, Masahiro Ono
[arxiv][journal][video]
IEEE Robotics and Automation Letters (RA-L) 2022

Learning Pseudo-Backdoors for Mixed Integer Programs
Aaron Ferber, Jialin Song, Bistra Dilkina, Yisong Yue
[arxiv][Springer]
CPAIOR 2022

Learning to Make Decisions via Submodular Regularization
Ayya Alieva, Aiden Aceves, Jialin Song, Stephen Mayo, Yisong Yue, Yuxin Chen
[pdf]
ICLR 2021

Machine Learning Based Path Planning for Improved Rover Navigation
Neil Abcouwer, Shreyansh Daftry, Siddarth Venkatraman, Tyler del Sesto, Olivier Toupet, Ravi Lanka, Jialin Song, Yisong Yue, Masahiro Ono
[arxiv][video]
IEEE Aerospace Conference (AeroConf) 2021

A General Large Neighborhood Search Framework for Solving Integer Linear Programs
Jialin Song, Ravi Lanka, Yisong Yue, Bistra Dilkina
[arxiv][colab]
NeurIPS 2020

Co-training for Policy Learning
Jialin Song, Ravi Lanka, Yisong Yue, Masahiro Ono
(Oral Presentation)
[pdf][supplementary] [code]
UAI 2019

A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes
Jialin Song, Yuxin Chen, Yisong Yue
[pdf] [supplementary]
AISTATS 2019

Onsets and Frames: Dual-Objective Piano Transcription
Curtis Hawthorne*, Erich Elsen*, Jialin Song*, Adam Roberts, Ian Simon, Colin Raffel, Jesse Engel, Sageev Oore, Douglas Eck
[arxiv][blog][code]
ISMIR 2018

Workshop Publications

CircuitVAE: Efficient and Scalable Latent Circuit Optimization
Jialin Song*, Aidan Swope*, Robert Kirby, Rajarshi Roy, Saad Godil, Jonathan Raiman, Bryan Catanzaro
[pdf]
Workshop on Adaptive Experimental Design and Active Learning in the Real World, NeurIPS 2023

Multi-objective Reinforcement Learning with Adaptive Pareto Reset for Prefix Adder Design
Jialin Song, Rajarshi Roy, Jonathan Raiman, Robert Kirby, Neel Kant, Saad Godil, Bryan Catanzaro
[pdf]
Workshop on ML for Systems, NeurIPS 2022

Learning Region of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
Fengxue Zhang, Jialin Song, James Bowden, Alexander Ladd, Yisong Yue, Thomas Desautels, Yuxin Chen
[pdf]
Workshop on Adaptive Experimental Design and Active Learning in the Real World, ICML 2022

Multi-task Bayesian Optimization via Gaussian Process Upper Confidence Bound
Sihui Dai, Jialin Song, Yisong Yue
[pdf][slides]
Workshop on Real World Experiment Design and Active Learning, ICML 2020

Efficient Imitation Learning with Local Trajectory Optimization
Jialin Song, Joe Wenjie Jiang, Amir Yazdanbakhsh, Ebrahim Songhori, Anna Goldie, Navdeep Jaitly, Azalia Mirhoseini
[pdf][slides][code]
Workshop on Inductive Biases, Invariances and Generalization in RL, ICML 2020

Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes
Jialin Song, Yury S. Tokpanov, Yuxin Chen, Dagny Fleischman, Kate T. Fountaine, Harry A. Atwater, Yisong Yue
[arxiv]
Workshop on Machine Learning for Molecules and Materials, NeurIPS 2018

Learning to Search via Self-Imitation with Applicatin to Risk-Aware Planning
Jialin Song*, Ravi Lanka*, Albert Zhao, Yisong Yue, Masahiro Ono
[pdf]
Workshop on Learning with Limited Labeled Data, NeurIPS 2017

Pre-print

Learning to Search via Retrospective Imitation
Jialin Song*, Ravi Lanka*, Albert Zhao, Aadyot Bhatnagar, Yisong Yue, Masahiro Ono
[arxiv][colab]