Minimum Feedback for Collision-Free Scheduling in Massive Random Access. - Justin Singh Kang; Wei Yu (ISIT, 2020)

Efficiently Computing Sparse Fourier Transforms of q-ary Functions. - Yigit Efe Erginbas; Justin Singh Kang; Amirali Aghazadeh; Kannan Ramchandran (ISIT, 2023)

Learning a 1-layer conditional generative model in total variation. - Ajil Jalal; Justin Singh Kang; Ananya Uppal; Kannan Ramchandran; Eric Price 001 (NeurIPS, 2023)

The Fair Value of Data Under Heterogeneous Privacy Constraints. - Justin Singh Kang; Ramtin Pedarsani; Kannan Ramchandran (CoRR, 2023)

Learning to Understand: Identifying Interactions via the Möbius Transform. - Justin Singh Kang; Yigit Efe Erginbas; Landon Butler; Ramtin Pedarsani; Kannan Ramchandran (NeurIPS, 2024)

The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning. - Justin Singh Kang; Ramtin Pedarsani; Kannan Ramchandran (Trans. Mach. Learn. Res., 2024)

SPEX: Scaling Feature Interaction Explanations for LLMs. - Justin Singh Kang; Landon Butler; Abhineet Agarwal; Yigit Efe Erginbas; Ramtin Pedarsani; Kannan Ramchandran; Bin Yu 001 (CoRR, 2025)

ProxySPEX: Inference-Efficient Interpretability via Sparse Feature Interactions in LLMs. - Landon Butler; Abhineet Agarwal; Justin Singh Kang; Yigit Efe Erginbas; Bin Yu 001; Kannan Ramchandran (CoRR, 2025)