21 citations · 58 across the 13 of their papers we have counts for
8 papers · 1 filter
Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms
Yilun Zhou, Adithya Renduchintala, Xian Li +3
Active learning (AL) algorithms may achieve better performance with fewer data because the model guides the data selection process. While many algorithms have been proposed, there…
Minimax bounds for structured prediction
Kevin Bello, Asish Ghoshal, Jean Honorio
Structured prediction can be considered as a generalization of many standard supervised learning tasks, and is usually thought as a simultaneous prediction of multiple labels. One…
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…
Learning linear structural equation models in polynomial time and sample complexity
Asish Ghoshal, Jean Honorio
The problem of learning structural equation models (SEMs) from data is a fundamental problem in causal inference. We develop a new algorithm --- which is computationally and statis…
Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity
Asish Ghoshal, Jean Honorio
We consider the problem of learning sparse polymatrix games from observations of strategic interactions. We show that a polynomial time method based on -group regulariz…
Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions
Asish Ghoshal, Jean Honorio
In this paper we obtain sufficient and necessary conditions on the number of samples required for exact recovery of the pure-strategy Nash equilibria (PSNE) set of a graphical game…
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity
Asish Ghoshal, Jean Honorio
Learning the directed acyclic graph (DAG) structure of a Bayesian network from observational data is a notoriously difficult problem for which many hardness results are known. In t…
Information-theoretic limits of Bayesian network structure learning
Asish Ghoshal, Jean Honorio
In this paper, we study the information-theoretic limits of learning the structure of Bayesian networks (BNs), on discrete as well as continuous random variables, from a finite num…