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2020 – today
- 2024
[c12]Jun-Ting Hsieh, Pravesh K. Kothari, Lucas Pesenti, Luca Trevisan:
New SDP Roundings and Certifiable Approximation for Cubic Optimization. SODA 2024: 2337-2362- [c11]Jun-Ting Hsieh
, Theo McKenzie, Sidhanth Mohanty, Pedro Paredes:
Explicit Two-Sided Unique-Neighbor Expanders. STOC 2024: 788-799 - [i17]Jun-Ting Hsieh, Pravesh K. Kothari, Sidhanth Mohanty, David Munhá Correia, Benny Sudakov:
Small Even Covers, Locally Decodable Codes and Restricted Subgraphs of Edge-Colored Kikuchi Graphs. CoRR abs/2401.11590 (2024) - [i16]Venkatesan Guruswami, Jun-Ting Hsieh, Prasad Raghavendra:
Certifying Euclidean Sections and Finding Planted Sparse Vectors Beyond the √n Dimension Threshold. CoRR abs/2405.05373 (2024) - [i15]Mitali Bafna, Jun-Ting Hsieh, Pravesh K. Kothari:
Rounding Large Independent Sets on Expanders. CoRR abs/2405.10238 (2024) - [i14]Sitan Chen, Jaume de Dios Pont, Jun-Ting Hsieh, Hsin-Yuan Huang, Jane Lange, Jerry Li:
Predicting quantum channels over general product distributions. CoRR abs/2409.03684 (2024) - 2023
- [c10]Venkatesan Guruswami, Jun-Ting Hsieh, Pravesh K. Kothari, Peter Manohar:
Efficient Algorithms for Semirandom Planted CSPs at the Refutation Threshold. FOCS 2023: 307-327 - [c9]Jun-Ting Hsieh, Pravesh K. Kothari:
Approximating Max-Cut on Bounded Degree Graphs: Tighter Analysis of the FKL Algorithm. ICALP 2023: 77:1-77:7 - [c8]Jun-Ting Hsieh, Pravesh K. Kothari, Aaron Potechin, Jeff Xu:
Ellipsoid Fitting up to a Constant. ICALP 2023: 78:1-78:20 - [c7]Jun-Ting Hsieh, Pravesh K. Kothari, Sidhanth Mohanty:
A simple and sharper proof of the hypergraph Moore bound. SODA 2023: 2324-2344 - [i13]Jun-Ting Hsieh, Theo McKenzie, Sidhanth Mohanty, Pedro Paredes:
Explicit two-sided unique-neighbor expanders. CoRR abs/2302.01212 (2023) - [i12]Jun-Ting Hsieh, Pravesh K. Kothari, Aaron Potechin, Jeff Xu:
Ellipsoid Fitting Up to a Constant. CoRR abs/2307.05954 (2023) - [i11]Venkatesan Guruswami, Jun-Ting Hsieh, Pravesh K. Kothari, Peter Manohar:
Efficient Algorithms for Semirandom Planted CSPs at the Refutation Threshold. CoRR abs/2309.16897 (2023) - [i10]Jun-Ting Hsieh, Pravesh K. Kothari, Lucas Pesenti, Luca Trevisan:
New SDP Roundings and Certifiable Approximation for Cubic Optimization. CoRR abs/2310.00393 (2023) - 2022
- [c6]Jun-Ting Hsieh, Sidhanth Mohanty, Jeff Xu:
Certifying Solution Geometry in Random CSPs: Counts, Clusters and Balance. CCC 2022: 11:1-11:18 - [c5]Mitali Bafna, Jun-Ting Hsieh, Pravesh K. Kothari, Jeff Xu:
Polynomial-Time Power-Sum Decomposition of Polynomials. FOCS 2022: 956-967 - [c4]Jun-Ting Hsieh, Pravesh K. Kothari:
Algorithmic Thresholds for Refuting Random Polynomial Systems. SODA 2022: 1154-1203 - [i9]Jun-Ting Hsieh, Pravesh K. Kothari:
Approximating Max-Cut on Bounded Degree Graphs: Tighter Analysis of the FKL Algorithm. CoRR abs/2206.09204 (2022) - [i8]Jun-Ting Hsieh, Pravesh K. Kothari, Sidhanth Mohanty:
A simple and sharper proof of the hypergraph Moore bound. CoRR abs/2207.10850 (2022) - [i7]Mitali Bafna, Jun-Ting Hsieh, Pravesh K. Kothari, Jeff Xu:
Polynomial-Time Power-Sum Decomposition of Polynomials. CoRR abs/2208.00122 (2022) - 2021
- [i6]Jun-Ting Hsieh, Sidhanth Mohanty, Jeff Xu:
Certifying solution geometry in random CSPs: counts, clusters and balance. CoRR abs/2106.12710 (2021) - [i5]Jun-Ting Hsieh, Pravesh K. Kothari:
Algorithmic Thresholds for Refuting Random Polynomial Systems. CoRR abs/2110.08677 (2021)
2010 – 2019
- 2019
- [c3]Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon:
Learning Neural PDE Solvers with Convergence Guarantees. ICLR (Poster) 2019 - [i4]Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon:
Learning Neural PDE Solvers with Convergence Guarantees. CoRR abs/1906.01200 (2019) - 2018
- [c2]Zelun Luo, Jun-Ting Hsieh, Lu Jiang, Juan Carlos Niebles, Li Fei-Fei:
Graph Distillation for Action Detection with Privileged Modalities. ECCV (14) 2018: 174-192 - [c1]Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li Fei-Fei, Juan Carlos Niebles:
Learning to Decompose and Disentangle Representations for Video Prediction. NeurIPS 2018: 515-524 - [i3]Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li Fei-Fei, Juan Carlos Niebles:
Learning to Decompose and Disentangle Representations for Video Prediction. CoRR abs/1806.04166 (2018) - [i2]David Xue, Anin Sayana, Evan Darke, Kelly Shen, Jun-Ting Hsieh, Zelun Luo, Li-Jia Li, N. Lance Downing, Arnold Milstein, Li Fei-Fei:
Vision-Based Gait Analysis for Senior Care. CoRR abs/1812.00169 (2018) - 2017
- [i1]Zelun Luo, Lu Jiang, Jun-Ting Hsieh, Juan Carlos Niebles, Li Fei-Fei:
Graph Distillation for Action Detection with Privileged Information. CoRR abs/1712.00108 (2017)
Coauthor Index
aka: Pravesh K. Kothari
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