Graph Distillation for Action Detection with Privileged Information. - Zelun Luo; Lu Jiang 004; Jun-Ting Hsieh; Juan Carlos Niebles; Li Fei-Fei 001 (CoRR, 2017)

Graph Distillation for Action Detection with Privileged Modalities. - Zelun Luo; Jun-Ting Hsieh; Lu Jiang 004; Juan Carlos Niebles; Li Fei-Fei 001 (ECCV (14), 2018)

Learning to Decompose and Disentangle Representations for Video Prediction. - Jun-Ting Hsieh; Bingbin Liu; De-An Huang; Li Fei-Fei 001; Juan Carlos Niebles (NeurIPS, 2018)

Vision-Based Gait Analysis for Senior Care. - David Xue; Anin Sayana; Evan Darke; Kelly Shen; Jun-Ting Hsieh; Zelun Luo; Li-Jia Li 001; N. Lance Downing; Arnold Milstein; Li Fei-Fei 001 (CoRR, 2018)

Learning Neural PDE Solvers with Convergence Guarantees. - Jun-Ting Hsieh; Shengjia Zhao; Stephan Eismann; Lucia Mirabella; Stefano Ermon (ICLR (Poster), 2019)

Certifying Solution Geometry in Random CSPs: Counts, Clusters and Balance. - Jun-Ting Hsieh; Sidhanth Mohanty; Jeff Xu (CCC, 2022)

Polynomial-Time Power-Sum Decomposition of Polynomials. - Mitali Bafna; Jun-Ting Hsieh; Pravesh K. Kothari; Jeff Xu (FOCS, 2022)

Algorithmic Thresholds for Refuting Random Polynomial Systems. - Jun-Ting Hsieh; Pravesh K. Kothari (SODA, 2022)

Efficient Algorithms for Semirandom Planted CSPs at the Refutation Threshold. - Venkatesan Guruswami; Jun-Ting Hsieh; Pravesh K. Kothari; Peter Manohar (FOCS, 2023)

Approximating Max-Cut on Bounded Degree Graphs: Tighter Analysis of the FKL Algorithm. - Jun-Ting Hsieh; Pravesh K. Kothari (ICALP, 2023)

Ellipsoid Fitting up to a Constant. - Jun-Ting Hsieh; Pravesh K. Kothari; Aaron Potechin; Jeff Xu (ICALP, 2023)

A simple and sharper proof of the hypergraph Moore bound. - Jun-Ting Hsieh; Pravesh K. Kothari; Sidhanth Mohanty (SODA, 2023)

Certifying Euclidean Sections and Finding Planted Sparse Vectors Beyond the √n Dimension Threshold. - Venkatesan Guruswami; Jun-Ting Hsieh; Prasad Raghavendra (FOCS, 2024)

New SDP Roundings and Certifiable Approximation for Cubic Optimization. - Jun-Ting Hsieh; Pravesh K. Kothari; Lucas Pesenti; Luca Trevisan 001 (SODA, 2024)

Explicit Two-Sided Unique-Neighbor Expanders. - Jun-Ting Hsieh; Theo McKenzie; Sidhanth Mohanty; Pedro Paredes 002 (STOC, 2024)

Small Even Covers, Locally Decodable Codes and Restricted Subgraphs of Edge-Colored Kikuchi Graphs. - Jun-Ting Hsieh; Pravesh K. Kothari; Sidhanth Mohanty; David Munhá Correia; Benny Sudakov (CoRR, 2024)

Improved Lower Bounds for all Odd-Query Locally Decodable Codes. - Arpon Basu; Jun-Ting Hsieh; Pravesh Kothari; Andrew D. Lin (Electron. Colloquium Comput. Complex., 2024)

Predicting quantum channels over general product distributions. - Sitan Chen; Jaume de Dios Pont; Jun-Ting Hsieh; Hsin-Yuan Huang; Jane Lange; Jerry Li 001 (COLT, 2025)

Rounding Large Independent Sets on Expanders. - Mitali Bafna; Jun-Ting Hsieh; Pravesh K. Kothari (STOC, 2025)

Explicit Two-Sided Vertex Expanders beyond the Spectral Barrier. - Jun-Ting Hsieh; Ting-Chun Lin; Sidhanth Mohanty; Ryan O'Donnell; Rachel Yun Zhang (STOC, 2025)

Explicit Lossless Vertex Expanders. - Jun-Ting Hsieh; Alexander Lubotzky; Sidhanth Mohanty; Assaf Reiner; Rachel Yun Zhang (CoRR, 2025)

The Quasi-Polynomial Low-Degree Conjecture is False. - Rares-Darius Buhai; Jun-Ting Hsieh; Aayush Jain; Pravesh K. Kothari (CoRR, 2025)

Solving Random Planted CSPs below the nk/2\

Coloring 3-Colorable Graphs with Low Threshold Rank. - Jun-Ting Hsieh (CoRR, 2025)

Sparsifying Cayley Graphs on Every Group. - Jun-Ting Hsieh; Daniel Z. Lee; Sidhanth Mohanty; Aaron Putterman; Rachel Yun Zhang (CoRR, 2025)