7 citations · 14 across the 23 of their papers we have counts for
7 papers · 1 filter
Information Theoretic Limits for Standard and One-Bit Compressed Sensing with Graph-Structured Sparsity
Adarsh Barik, Jean Honorio
In this paper, we analyze the information theoretic lower bound on the necessary number of samples needed for recovering a sparse signal under different compressed sensing settings…
Learning latent variable structured prediction models with Gaussian perturbations
Kevin Bello, Jean Honorio
The standard margin-based structured prediction commonly uses a maximum loss over all possible structured outputs. The large-margin formulation including latent variables not only…
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time
Asish Ghoshal, Jean Honorio
MAP perturbation models have emerged as a powerful framework for inference in structured prediction. Such models provide a way to efficiently sample from the Gibbs distribution and…
Regularized Loss Minimizers with Local Data Perturbation: Consistency and Data Irrecoverability
Zitao Li, Jean Honorio
We introduce a new concept, data irrecoverability, and show that the well-studied concept of data privacy is sufficient but not necessary for data irrecoverability. We show that th…
Learning discrete Bayesian networks in polynomial time and sample complexity
Adarsh Barik, Jean Honorio
In this paper, we study the problem of structure learning for Bayesian networks in which nodes take discrete values. The problem is NP-hard in general but we show that under certai…
Information-theoretic Limits for Community Detection in Network Models
Chuyang Ke, Jean Honorio
We analyze the information-theoretic limits for the recovery of node labels in several network models. This includes the Stochastic Block Model, the Exponential Random Graph Model,…