arXiv research

Optimal transport for vector Gaussian mixtures improves efficiency and structure preservation.

We present a novel approach for learning an HMM whose outputs are distributed according to a parametric family. This is done by {\em decoupling} the learning task into two steps: first estimating the output parameters, and then estimating the hidden states transition probabilities. The first step is accomplished by fit…

We introduce the problem of learning mixtures of $k$ subcubes over $\{0,1\}^n$, which contains many classic learning theory problems as a special case (and is itself a special case of others). We give a surprising $n^{O(\log k)}$-time learning algorithm based on higher-order multilinear moments. It is not possible to l…

Study uses MTD model to optimize portfolios by capturing complex financial asset relationships.

This paper introduces constrained mixtures for continuous distributions, characterized by a mixture of distributions where each distribution has a shape similar to the base distribution and disjoint domains. This new concept is used to create generalized asymmetric versions of the Laplace and normal distributions, whic…

Bayesian mixture models are widely applied for unsupervised learning and exploratory data analysis. Markov chain Monte Carlo based on Gibbs sampling and split-merge moves are widely used for inference in these models. However, both methods are restricted to limited types of transitions and suffer from torpid mixing and…

Study uniform rates for estimating Gaussian mixtures without separation assumption.

New algorithm reduces dynamic regret for MDPs with unknown transition and adversarial rewards.

The paper proposes a structure learning model for efficient reinforcement learning.

Proposes a deep generative model for robust forecasting on sparse multivariate time series.

Proposes a new model for time series that considers smooth transitions between states.

Paper detects gradual changes in cluster structure using MC fusion.

New algorithm reduces reinforcement learning regret for linear MDPs with unknown transitions.

Model detects epileptic seizures in EEG with high sensitivity.

Proposes a differentiable LSE-ICNN for modeling multi-well potentials.

Efficient RL for linear MDPs with unknown transitions.

A new framework for robust policy learning in MDPs with linear mixture dynamics.

We study the mixtures of factorizing probability distributions represented as visible marginal distributions in stochastic layered networks. We take the perspective of kernel transitions of distributions, which gives a unified picture of distributed representations arising from Deep Belief Networks (DBN) and other netw…

Non-homogeneous hidden Markov models (NHHMM) are a subclass of dependent mixture models used for semi-supervised learning, where both transition probabilities between the latent states and mean parameter of the probability distribution of the responses (for a given state) depend on the set of $p$ covariates. A priori w…

This paper tackles online reinforcement learning for unseen tasks with unknown boundaries.

Study local geometry of mixture models via spectral theory, revealing transitions in training dynamics.

We propose a probabilistic modeling framework for learning the dynamic patterns in the collective behaviors of social agents and developing profiles for different behavioral groups, using data collected from multiple information sources. The proposed model is based on a hierarchical Bayesian process, in which each obse…

GDM models time series with smoother transitions and interpretable states.

A new method learns state and proposal dynamics in state-space models using neural networks.

New algorithm reduces regret in linear mixture SSPs without cost bounds.

Study calculates tail risk for various mixture distributions.

This work investigates a mixture of LMC and RMHMC with MMALA for geometric ergodicity.

Modeling the impact of the order flow on asset prices is of primary importance to understand the behavior of financial markets. Part I of this paper reported the remarkable improvements in the description of the price dynamics which can be obtained when one incorporates the impact of past returns on the future order fl…

DeRegiME forecasts with regime structure, improving probabilistic predictions across various time series.

Paper develops a robust classifier for Gaussian mixture models under sparse adversarial perturbations.

This paper analyzes MFVBI for GMM using statistical mechanics.

A key task in Bayesian machine learning is sampling from distributions that are only specified up to a partition function (i.e., constant of proportionality). One prevalent example of this is sampling posteriors in parametric distributions, such as latent-variable generative models. However sampling (even very approxim…

A key task in Bayesian statistics is sampling from distributions that are only specified up to a partition function (i.e., constant of proportionality). However, without any assumptions, sampling (even approximately) can be #P-hard, and few works have provided "beyond worst-case" guarantees for such settings. For log-c…

A new framework predicts hidden Markov model regimes online.

Phase segregation, the process by which the components of a binary mixture spontaneously separate, is a key process in the evolution and design of many chemical, mechanical, and biological systems. In this work, we present a data-driven approach for the learning, modeling, and prediction of phase segregation. A direct …

The existence of stationary Markov perfect equilibria in stochastic games is shown under a general condition called "(decomposable) coarser transition kernels". This result covers various earlier existence results on correlated equilibria, noisy stochastic games, stochastic games with finite actions and state-independe…

Framework for multi-scale clustering using phase transitions.

Understanding proper distance measures between distributions is at the core of several learning tasks such as generative models, domain adaptation, clustering, etc. In this work, we focus on mixture distributions that arise naturally in several application domains where the data contains different sub-populations. For …

New framework TDRL identifies latent causal variables from sequential data.

Study characterizes learning from heavy-tailed data in high dimensions using superstatistical methods.

We propose a framework, named Aggregated Wasserstein, for computing a dissimilarity measure or distance between two Hidden Markov Models with state conditional distributions being Gaussian. For such HMMs, the marginal distribution at any time spot follows a Gaussian mixture distribution, a fact exploited to softly matc…

In recent years, non-parametric methods utilizing random walks on graphs have been used to solve a wide range of machine learning problems, but in their simplest form they do not scale well due to the quadratic complexity. In this paper, a new dual-tree based variational approach for approximating the transition matrix…

Heavy-tailed distributions are widely used in robust mixture modelling due to possessing thick tails. As a computationally tractable subclass of the stable distributions, sub-Gaussian $α$-stable distribution received much interest in the literature. Here, we introduce a type of expectation maximization algorithm that e…

Identifies latent actions and dynamics from offline data with diverse demonstrators.

Consistent estimator for mixtures of nonparametric elliptical distributions helps cluster analysis.

Paper proposes a new Wasserstein distance for mixtures of radially contoured distributions.

NMDR estimates complex mixtures of distributions efficiently.

We study the problem of learning a mixture model of non-parametric product distributions. The problem of learning a mixture model is that of finding the component distributions along with the mixing weights using observed samples generated from the mixture. The problem is well-studied in the parametric setting, i.e., w…