Computing and maximizing influence in linear threshold and triggering models. - Justin T. Khim; Varun S. Jog; Po-Ling Loh (NIPS, 2016)

Computationally Efficient Influence Maximization in Stochastic and Adversarial Models: Algorithms and Analysis. - Justin Khim; Varun S. Jog; Po-Ling Loh (CoRR, 2016)

Permutation Tests for Infection Graphs. - Justin Khim; Po-Ling Loh (CoRR, 2017)

Confidence Sets for the Source of a Diffusion in Regular Trees. - Justin Khim; Po-Ling Loh (IEEE Trans. Netw. Sci. Eng., 2017)

Adversarial Risk Bounds for Binary Classification via Function Transformation. - Justin Khim; Po-Ling Loh (CoRR, 2018)

Adversarial Influence Maximization. - Justin Khim; Varun S. Jog; Po-Ling Loh (ISIT, 2019)

Uniform Convergence of Rank-weighted Learning. - Justin Khim; Liu Leqi; Adarsh Prasad; Pradeep Ravikumar (ICML, 2020)

Class-Weighted Classification: Trade-offs and Robust Approaches. - Ziyu Xu 001; Chen Dan 001; Justin Khim; Pradeep Ravikumar (ICML, 2020)

Improved Clinical Abbreviation Expansion via Non-Sense-Based Approaches. - Juyong Kim 002; Linyuan Gong; Justin Khim; Jeremy C. Weiss; Pradeep Ravikumar (ML4H@NeurIPS, 2020)

Multiclass Classification via Class-Weighted Nearest Neighbors. - Justin Khim; Ziyu Xu 001; Shashank Singh 005 (CoRR, 2020)

Subseasonal climate prediction in the western US using Bayesian spatial models. - Vishwak Srinivasan; Justin Khim; Arindam Banerjee 001; Pradeep Ravikumar (UAI, 2021)

Statistical Theory for Imbalanced Binary Classification. - Shashank Singh 005; Justin Khim (CoRR, 2021)

Optimal Binary Classification Beyond Accuracy. - Shashank Singh 005; Justin T. Khim (NeurIPS, 2022)