Prathamesh Dharangutte

Prathamesh Dharangutte, Jie Gao, Ruobin Gong et al.

This work proposes a class of differentially private mechanisms for linear queries, in particular range queries, that leverages correlated input perturbation to simultaneously achieve unbiasedness, consistency, statistical transparency, and control over utility requirements in terms of accuracy targets expressed either in certain query margins or as implied by the hierarchical database structure. The proposed Cascade Sampling algorithm instantiates the mechanism exactly and efficiently. Our theoretical and empirical analysis demonstrates that we achieve near-optimal utility, effectively compete with other methods, and retain all the favorable statistical properties discussed earlier.

John Y. Shin, Prathamesh Dharangutte

Graph neural networks are a popular variant of neural networks that work with graph-structured data. In this work, we consider combining graph neural networks with the energy-based view of Grathwohl et al. (2019) with the aim of obtaining a more robust classifier. We successfully implement this framework by proposing a novel method to ensure generation over features as well as the adjacency matrix and evaluate our method against the standard graph convolutional network (GCN) architecture (Kipf & Welling (2016)). Our approach obtains comparable discriminative performance while improving robustness, opening promising new directions for future research for energy-based graph neural networks.

Prathamesh Dharangutte, Christopher Musco

The problem of inferring unknown graph edges from numerical data at a graph's nodes appears in many forms across machine learning. We study a version of this problem that arises in the field of \emph{landscape genetics}, where genetic similarity between organisms living in a heterogeneous landscape is explained by a weighted graph that encodes the ease of dispersal through that landscape. Our main contribution is an efficient algorithm for \emph{inverse landscape genetics}, which is the task of inferring this graph from measurements of genetic similarity at different locations (graph nodes). Inverse landscape genetics is important in discovering impediments to species dispersal that threaten biodiversity and long-term species survival. In particular, it is widely used to study the effects of climate change and human development. Drawing on influential work that models organism dispersal using graph \emph{effective resistances} (McRae 2006), we reduce the inverse landscape genetics problem to that of inferring graph edges from noisy measurements of these resistances, which can be obtained from genetic similarity data. Building on the NeurIPS 2018 work of Hoskins et al. 2018 on learning edges in social networks, we develop an efficient first-order optimization method for solving this problem. Despite its non-convex nature, experiments on synthetic and real genetic data establish that our method provides fast and reliable convergence, significantly outperforming existing heuristics used in the field. By providing researchers with a powerful, general purpose algorithmic tool, we hope our work will have a positive impact on accelerating work on landscape genetics.