9 papers
Partial Inference in Structured Prediction
Chuyang Ke, Jean Honorio
In this paper, we examine the problem of partial inference in the context of structured prediction. Using a generative model approach, we consider the task of maximizing a score fu…
A Theoretical Study of The Effects of Adversarial Attacks on Sparse Regression
Deepak Maurya, Jean Honorio
This paper analyzes regularized linear regression under the challenging scenario of having only adversarially corrupted data for training. We use the primal-dual witness p…
A Novel Plug-and-Play Approach for Adversarially Robust Generalization
Deepak Maurya, Adarsh Barik, Jean Honorio
In this work, we propose a robust framework that employs adversarially robust training to safeguard the ML models against perturbed testing data. Our contributions can be seen from…
Hyperedge Prediction using Tensor Eigenvalue Decomposition
Deepak Maurya, Balaraman Ravindran
Link prediction in graphs is studied by modeling the dyadic interactions among two nodes. The relationships can be more complex than simple dyadic interactions and could require th…
Identification of Errors-in-Variables ARX Models Using Modified Dynamic Iterative PCA
Deepak Maurya, Arun K. Tangirala, Shankar Narasimhan
Identification of autoregressive models with exogenous input (ARX) is a classical problem in system identification. This article considers the errors-in-variables (EIV) ARX model i…
Hypergraph Partitioning using Tensor Eigenvalue Decomposition
Deepak Maurya, Balaraman Ravindran
Hypergraphs have gained increasing attention in the machine learning community lately due to their superiority over graphs in capturing super-dyadic interactions among entities. In…