9 citations · 13 across the 5 of their papers we have counts for
7 papers
Inferring Change Points in Regression via Sample Weighting
Gabriel Arpino, Ramji Venkataramanan
We study the problem of identifying change points in high-dimensional generalized linear models, and propose an approach based on sample-weighted empirical risk minimization. Our m…
Inferring Change Points in High-Dimensional Regression via Approximate Message Passing
Gabriel Arpino, Xiaoqi Liu, Julia Gontarek +1
We consider the problem of localizing change points in a generalized linear model (GLM), a model that covers many widely studied problems in statistical learning including linear,…
Greedy Heuristics and Linear Relaxations for the Random Hitting Set Problem
Gabriel Arpino, Daniil Dmitriev, Nicolo Grometto
Consider the Hitting Set problem where, for a given universe and a collection of subsets , one seeks…
Statistical-Computational Tradeoffs in Mixed Sparse Linear Regression
Gabriel Arpino, Ramji Venkataramanan
We consider the problem of mixed sparse linear regression with two components, where two real -sparse signals are to be recovered from unlabelled noisy linear mea…
(Dis)assortative Partitions on Random Regular Graphs
Freya Behrens, Gabriel Arpino, Yaroslav Kivva +1
We study the problem of assortative and disassortative partitions on random -regular graphs. Nodes in the graph are partitioned into two non-empty groups. In the assortative par…
Group Testing in the High Dilution Regime
Gabriel Arpino, Nicolò Grometto, Afonso S. Bandeira
Non-adaptive group testing refers to the problem of inferring a sparse set of defectives from a larger population using the minimum number of simultaneous pooled tests. Recent posi…
Using Information Invariants to Compare Swarm Algorithms and General Multi-Robot Algorithms: A Technical Report
Gabriel Arpino, Kyle Morris, Sasanka Nagavalli +1
Robotic swarms are decentralized multi-robot systems whose members use local information from proximal neighbors to execute simple reactive control laws that result in emergent col…