25 citations · 143 across the 71 of their papers we have counts for
9 papers · 1 filter
Provable Computational and Statistical Guarantees for Efficient Learning of Continuous-Action Graphical Games
Adarsh Barik, Jean Honorio
In this paper, we study the problem of learning the set of pure strategy Nash equilibria and the exact structure of a continuous-action graphical game with quadratic payoffs by obs…
Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation
Chuyang Ke, Jean Honorio
In this paper we propose an algorithm for exact partitioning of high-order models. We define a general class of -degree Homogeneous Polynomial Models, which subsumes several exa…
Exact inference in structured prediction
Kevin Bello, Jean Honorio
Structured prediction can be thought of as a simultaneous prediction of multiple labels. This is often done by maximizing a score function on the space of labels, which decomposes…
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…
On the Correctness and Sample Complexity of Inverse Reinforcement Learning
Abi Komanduru, Jean Honorio
Inverse reinforcement learning (IRL) is the problem of finding a reward function that generates a given optimal policy for a given Markov Decision Process. This paper looks at an a…
Direct Learning with Guarantees of the Difference DAG Between Structural Equation Models
Asish Ghoshal, Kevin Bello, Jean Honorio
Discovering cause-effect relationships between variables from observational data is a fundamental challenge in many scientific disciplines. However, in many situations it is desira…
Learning Bayesian Networks with Low Rank Conditional Probability Tables
Adarsh Barik, Jean Honorio
In this paper, we provide a method to learn the directed structure of a Bayesian network using data. The data is accessed by making conditional probability queries to a black-box m…
Exact Inference with Latent Variables in an Arbitrary Domain
Chuyang Ke, Jean Honorio
We analyze the necessary and sufficient conditions for exact inference of a latent model. In latent models, each entity is associated with a latent variable following some probabil…
Optimality Implies Kernel Sum Classifiers are Statistically Efficient
Raphael Arkady Meyer, Jean Honorio
We propose a novel combination of optimization tools with learning theory bounds in order to analyze the sample complexity of optimal kernel sum classifiers. This contrasts the typ…