Various methods for solving the inverse reinforcement learning (IRL) problem have been developed independently in machine and economics. In particular, method of Maximum Causal Entropy IRL is based on perspective entropy maximization, while related advances field economics instead assume existence unobserved action shocks to explain expert behavior (Nested Fixed Point Algorithm, Conditional Cho...

We present a new algorithm, Iterative Estimation Maximization (IEM), for stochastic linear and convex programs with Conditional-Value-at-Risk (CVaR) constraints. IEM iteratively constructs a sequence of compact-sized linear, or convex, optimization problems, and solves them sequentially to find the optimal solution. The problem size IEM solves in each iteration is unaffected by the size of rand...

We propose in this paper a novel approach to the classification of discrete sequences. This approach builds a model fitting some dynamical features deduced from the learning sample. These features are discrete phase-type (PH) distributions. They model the first passage times (FPT) between occurrences of pairs of substrings. The PHit algorithm, an adapted version of the Expectation-Maximization ...

Multi-atlas techniques are commonplace in medical image segmentation due to their high performance and ease of implementation. Locally weighting the contributions from the different atlases in the label fusion process can improve the quality of the segmentation. However, how to define these weights in a principled way in inter-modality scenarios remains an open problem. Here we propose a label ...

Oscillator phase noise (PHN) and carrier frequency offset (CFO) can adversely impact the performance of orthogonal frequency division multiplexing (OFDM) systems since they can result in inter carrier interference and rotation of the signal constellation. In this paper, we propose an expectation conditional maximization (ECM) based algorithm for joint estimation of channel, PHN, and CFO in OFDM...

A challenge in microarray data analysis concerns discovering local structures composed by sets of genes that show homogeneous expression patterns across subsets of conditions. We present an extension of the mixture of factor analyzers model (MFA) allowing for simultaneous clustering of genes and conditions. The proposed model is rather flexible since it models the density of high-dimensional da...

Latent variable models are an elegant framework for capturing rich probabilistic dependencies in many applications. However, current approaches typically parametrize these models using conditional probability tables, and learning relies predominantly on local search heuristics such as Expectation Maximization. Using tensor algebra, we propose an alternative parameterization of latent variable m...

We propose a new catalog-based speech-music separation method for background music removal. Assuming that we know a catalog of the background music, we develop a generative model for the superposed speech and music spectrograms. We represent the speech spectrogram by a Non-negative Matrix Factorization (NMF) model and the music spectrogram by a conditional Poisson Mixture Model (PMM). By choosi...