Research

Preprints under Review or Workshop Papers

• Reverse Causal Inference on Panel Data via Generalized Synthetic Control.
  J. Zhang, Y. Park, D. Robinson, D. Roth, Y. Wang.
  Under review. Shorter version at NeurIPS Workshop on Causal Dynamic System 2022.

• Adaptive Sampling for Probabilistic Forecasting under Distribution Shift.
  L. Masserano, S. Rangapuram, S. Kapoor, R. Nirwan, Y. Park, M. Bohlke-Schneider.
  Under review. Shorter version at NeurIPS 2022 Workshop on Distribution Shifts.
  

• First Detrend then Attend: Rethinking Attention for Time Series Forecasting.
  X. Zhang, X. Jin, K. Gopalswamy, G. Gupta, Y. Park, D. Robinson, Y. Wang.
  Under review. Shorter version at NeurIPS 2022 Workshop on All Things Attention.

• Testing Causality of High-Dimensional Data.
  Under review.

• Dynamic Ensembling for Probabilistic Time Series Forecasting via Deep Reinforcement Learning.
  Under review.
  

• Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting.
  Under review.

• Temporal-consistent Optimal Transport for Time Series Alignment.
  Under review.

Published Papers

• But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI.
  C. Marx, Y. Park, H. Hasson, Y. Wang, S. Ermon, J. Huan.
  Accepted to Proceedings of International Conference on Learning Representation (ICLR), 2023.

• Towards Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms.
  L. Lui, Y. Park, N. Hoang, H. Hasson, J. Huan.
  Accepted to Proceedings of International Conference on Learning Representation (ICLR), 2023.

• Domain Adaptation for Time Series Forecasting via Attention Sharing.
  X. Jin, Y. Park, D. Robinson, Y. Wang.
  Proceedings of International Conference on Machine Learning (ICML), 2022.
  

• Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting.
  Y. Park, D. Robinson, J. Gasthaus, Y. Wang.
  Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.

• Multivariate Quantile Functions for Forecasting.
  K. Kan, F. Aubet, T. Januschowski, Y. Park, K. Benidis, J. Gasthaus.
  Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
  Selected as an oral, 2.6% of all submissions.

• Robust Probabilistic Time Series Forecasting.
  T. Yoon, Y. Park, E. Ryu, Y. Wang.
  Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.

• Variance Reduced Training with Stratified Sampling for Forecasting Models.
  Y. Lu, Y. Park, L. Cheng, Y. Wang, D. Foster.
  Proceedings of International Conference on Machine Learning (ICML), 2021.
  

• Structured Policy Iteration for Linear Quadratic Regulator.
  Y. Park, Z. Wen, R. Rossi, G. Wu, H. Zhao.
  Proceedings of International Conference on Machine Learning (ICML), 2020.
  [Slide], [Supplementary materials], [arXiv]

• Optimal Operation of a Plug-in Hybrid Vehicle with Battery Thermal and Degradation Model.
  J. Kim, Y. Park, J. Fox, S. Boyd, and W. Dally.
  Proceedings of the American Control Conference (ACC), 2020.
  

• Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize.
  Y. Park, S. Dhar, S. Boyd, and M. Shah.
  Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020.
  [arXiv (long version)], [ICASSP version]

• Linear Convergence of Cyclic SAGA.
  Y. Park and E. K. Ryu.
  Optimization Letter, 2020.
  [arXiv]

• Linear Quadratic Regulator for Resource-Efficient Cloud Services.
  Y. Park, K. Mahadik, R. Rossi, G. Wu, and H. Zhao.
  Proceedings of ACM Symposium on Cloud Computing (SoCC), 2019.

• Network Inference via the Time-Varying Graphical Lasso.
  D. Hallac, Y. Park, S. Boyd, and J. Leskovec.
  Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017
  [Code]

• Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields.
  Y. Park, D. Hallac, S. Boyd, and J. Leskovec.
  Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS), 2017
  [Supplementary Material], [Code]

Others

• Convergent Actor-Critic under Off-policy and Function Approximation. H. Maei and Y. Park, [Slide].

• Universal Loseless Compression: Context Tree Weighting. [Slides]

• Hypercontractivity, Maximal Correlation, and Non-cooperative Simulation. [Slides, Report]

• Successive Lossy Compression for Laplacian Source. [Slides, Report, Acknowledgement]