Computing the Longest Increasing Subsequence of a Sequence Subject to Dynamic Insertion. - Alex Chen; Timothy Chu; Nathan Pinsker (CoRR, 2013)

Intrinsic Metrics: Nearest Neighbor and Edge Squared Distances. - Timothy Chu; Gary L. Miller; Donald R. Sheehy (CoRR, 2017)

Constant Arboricity Spectral Sparsifiers. - Timothy Chu; Michael B. Cohen; Jakub W. Pachocki; Richard Peng (CoRR, 2018)

Algorithms and Hardness for Linear Algebra on Geometric Graphs. - Josh Alman; Timothy Chu; Aaron Schild; Zhao Song 002 (FOCS, 2020)

Exact computation of a manifold metric, via Lipschitz Embeddings and Shortest Paths on a Graph. - Timothy Chu; Gary L. Miller; Donald R. Sheehy (SODA, 2020)

Weighted Cheeger and Buser Inequalities, with Applications to Clustering and Cutting Probability Densities. - Timothy Chu; Gary L. Miller; Noel J. Walkington; Alex L. Wang (CoRR, 2020)

Functions that Preserve Manhattan Distances. - Timothy Chu; Gary L. Miller; Shyam Narayanan; Mark Sellke (CoRR, 2020)

Machine Learning: Metrics and Embeddings. - Timothy Chu (2022)

Spectral Clustering on Large Datasets: When Does it Work? Theory from Continuous Clustering and Density Cheeger-Buser. - Timothy Chu; Gary L. Miller; Noel Walkington (CoRR, 2023)

Fine-tune Language Models to Approximate Unbiased In-context Learning. - Timothy Chu; Zhao Song 002; Chiwun Yang (CoRR, 2023)

Graph Sparsification, Spectral Sketches, and Faster Resistance Computation via Short Cycle Decompositions. - Timothy Chu; Yu Gao 001; Richard Peng; Sushant Sachdeva; Saurabh Sawlani; Junxing Wang (SIAM J. Comput., 2023)

How to Protect Copyright Data in Optimization of Large Language Models? - Timothy Chu; Zhao Song 002; Chiwun Yang (AAAI, 2024)

Metric Transforms and Low Rank Representations of Kernels for Fast Attention. - Timothy Chu; Josh Alman; Gary L. Miller; Shyam Narayanan; Mark Sellke; Zhao Song 002 (NeurIPS, 2024)