Entropic Causal Inference: Identifiability and Finite Sample Results. - Spencer Compton; Murat Kocaoglu; Kristjan H. Greenewald; Dmitriy Katz (NeurIPS, 2020)

Edge Matching with Inequalities, Triangles, Unknown Shape, and Two Players. - Jeffrey Bosboom; Charlotte Chen; Lily Chung; Spencer Compton; Michael J. Coulombe; Erik D. Demaine; Martin L. Demaine; Ivan Tadeu Ferreira Antunes Filho; Dylan H. Hendrickson; Adam Hesterberg; Calvin Hsu; William Hu; et al. (J. Inf. Process., 2020)

Entropic Causal Inference: Graph Identifiability. - Spencer Compton; Kristjan H. Greenewald; Dmitriy A. Katz; Murat Kocaoglu (ICML, 2022)

A Tighter Approximation Guarantee for Greedy Minimum Entropy Coupling. - Spencer Compton (ISIT, 2022)

Minimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier. - Spencer Compton; Dmitriy Katz; Benjamin Qi; Kristjan H. Greenewald; Murat Kocaoglu (AISTATS, 2023)

Average-Case Dimensionality Reduction in 𝓁1: Tree Ising Models. - Moses Charikar; Spencer Compton; Chirag Pabbaraju (CoRR, 2023)

Near-Optimal Mean Estimation with Unknown, Heteroskedastic Variances. - Spencer Compton; Gregory Valiant (STOC, 2024)

Computing Low-Entropy Couplings for Large-Support Distributions. - Samuel Sokota; Dylan Sam; Christian Schröder de Witt; Spencer Compton; Jakob N. Foerster; J. Zico Kolter (UAI, 2024)

New Partitioning Techniques and Faster Algorithms for Approximate Interval Scheduling. - Spencer Compton; Slobodan Mitrovic; Ronitt Rubinfeld (Algorithmica, 2024)

Lower Bounds for Greedy Teaching Set Constructions. - Spencer Compton; Chirag Pabbaraju; Nikita Zhivotovskiy (COLT, 2025)

Embedding Probability Distributions into Low Dimensional ℓ1: Tree Ising Models via Truncated Metrics. - Moses Charikar; Spencer Compton; Chirag Pabbaraju (SODA, 2025)

Attainability of Two-Point Testing Rates for Finite-Sample Location Estimation. - Spencer Compton; Gregory Valiant (CoRR, 2025)