Logistic regression is a commonly used classification algorithm in machine learning. It allows categorizing data into discrete classes by learning the relationship from given set of labeled data. learns linear dataset and then introduces nonlinearity through an activation function to determine hyperplane that separates points two subclasses. In case logistic regression, most perform binary clas...

In this paper we propose a new algorithm for learning polyhedral classifiers. In contrast to existing methods for learning polyhedral classifier which solve a constrained optimization problem, our method solves an unconstrained optimization problem. Our method is based on a logistic function based model for the posterior probability function. We propose an alternating optimization algorithm, na...

We study model-based reinforcement learning (RL) for episodic Markov decision processes (MDP) whose transition probability is parametrized by an unknown core with features of state and action. Despite much recent progress in analyzing algorithms the linear MDP setting, understanding more general models very restrictive. In this paper, we propose a provably efficient RL algorithm given multinomi...