Fast Counting in Machine Learning Applications. - Subhadeep Karan; Matthew Eichhorn; Blake Hurlburt; Grant Iraci; Jaroslaw Zola (UAI, 2018)

Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations. - Tri Dao; Albert Gu; Matthew Eichhorn; Atri Rudra; Christopher Ré (ICML, 2019)

Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps. - Tri Dao; Nimit Sharad Sohoni; Albert Gu; Matthew Eichhorn; Amit Blonder; Megan Leszczynski; Atri Rudra; Christopher Ré (ICLR, 2020)

Automated survey of selected common plant species in Thai homegardens using Google Street View imagery and a deep neural network. - John Ringland; Martha Bohm; So-Ra Baek; Matthew Eichhorn (Earth Sci. Informatics, 2021)

Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge. - Mayleen Cortez; Matthew Eichhorn; Christina Lee Yu (NeurIPS, 2022)

Online Team Formation Under Different Synergies. - Matthew Eichhorn; Siddhartha Banerjee; David Kempe 001 (WINE, 2022)

Fair and Efficient Allocation with Quotas. - Siddhartha Banerjee; Matthew Eichhorn; David Kempe 001 (CoRR, 2022)

Graph Agnostic Estimators with Staggered Rollout Designs under Network Interference. - Mayleen Cortez; Matthew Eichhorn; Christina Lee Yu (CoRR, 2022)

Exploiting neighborhood interference with low order interactions under unit randomized design. - Mayleen Cortez; Matthew Eichhorn; Christina Lee Yu (CoRR, 2022)

Allocating with Priorities and Quotas: Algorithms, Complexity, and Dynamics. - Siddhartha Banerjee; Matthew Eichhorn; David Kempe 001 (EC, 2023)

Combining Rollout Designs and Clustering for Causal Inference under Low-order Interference. - Mayleen Cortez-Rodriguez; Matthew Eichhorn; Christina Lee Yu (CoRR, 2024)

Analysis of Two-Stage Rollout Designs with Clustering for Causal Inference under Network Interference. - Mayleen Cortez-Rodriguez; Matthew Eichhorn; Christina Lee Yu (AISTATS, 2025)