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Nevin Zhang

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5 papers

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AAAI Conference 2017 Conference Paper

Latent Tree Analysis

Nevin Zhang
Leonard Poon

Latent tree analysis seeks to model the correlations amonga set of random variables using a tree of latent variables. It was proposed as an improvement to latent class analysisÑa method widely used in social sciences and medicine to identify homogeneous subgroups in a population. It provides new and fruitful perspectives on a number of machine learningareas, including cluster analysis, topic detection, and deep probabilistic modeling. This paper gives an overview of the research on latent tree analysis and various ways it is used inpractice.

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AAAI Conference 2017 Conference Paper

Sparse Boltzmann Machines with Structure Learning as Applied to Text Analysis

Zhourong Chen
Nevin Zhang
Dit-Yan Yeung
Peixian Chen

We are interested in exploring the possibility and beneﬁts of structure learning for deep models. As the ﬁrst step, this paper investigates the matter for Restricted Boltzmann Machines (RBMs). We conduct the study with Replicated Softmax, a variant of RBMs for unsupervised text analysis. We present a method for learning what we call Sparse Boltzmann Machines, where each hidden unit is connected to a subset of the visible units instead of all of them. Empirical results show that the method yields models with signiﬁcantly improved model ﬁt and interpretability as compared with RBMs where each hidden unit is connected to all visible units.

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AAAI Conference 2016 Conference Paper

Progressive EM for Latent Tree Models and Hierarchical Topic Detection

Peixian Chen
Nevin Zhang
Leonard Poon
Zhourong Chen

Hierarchical latent tree analysis (HLTA) is recently proposed as a new method for topic detection. It differs fundamentally from the LDA-based methods in terms of topic deﬁnition, topic-document relationship, and learning method. It has been shown to discover signiﬁcantly more coherent topics and better topic hierarchies. However, HLTA relies on the Expectation-Maximization (EM) algorithm for parameter estimation and hence is not efﬁcient enough to deal with large datasets. In this paper, we propose a method to drastically speed up HLTA using a technique inspired by the advances in the method of moments. Empirical experiments show that our method greatly improves the efﬁciency of HLTA. It is as efﬁcient as the state-of-the-art LDA-based method for hierarchical topic detection and ﬁnds substantially better topics and topic hierarchies.

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AAAI Conference 2015 Conference Paper

Unidimensional Clustering of Discrete Data Using Latent Tree Models

April Liu
Leonard Poon
Nevin Zhang

This paper is concerned with model-based clustering of discrete data. Latent class models (LCMs) are usually used for the task. An LCM consists of a latent variable and a number of attributes. It makes the overly restrictive assumption that the attributes are mutually independent given the latent variable. We propose a novel method to relax the assumption. The key idea is to partition the attributes into groups such that correlations among the attributes in each group can be properly modeled by using one single latent variable. The latent variables for the attribute groups are then used to build a number of models and one of them is chosen to produce the clustering results. Extensive empirical studies have been conducted to compare the new method with LCM and several other methods (K-means, kernel Kmeans and spectral clustering) that are not model-based. The new method outperforms the alternative methods in most cases and the differences are often large.

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NeurIPS Conference 1999 Conference Paper

An Environment Model for Nonstationary Reinforcement Learning

Samuel Choi
Dit-Yan Yeung
Nevin Zhang

Reinforcement learning in nonstationary environments is generally regarded as an important and yet difficult problem. This paper partially addresses the problem by formalizing a subclass of nonsta(cid: 173) tionary environments. The environment model, called hidden-mode Markov decision process (HM-MDP), assumes that environmental changes are always confined to a small number of hidden modes. A mode basically indexes a Markov decision process (MDP) and evolves with time according to a Markov chain. While HM-MDP is a special case of partially observable Markov decision processes (POMDP), modeling an HM-MDP environment via the more gen(cid: 173) eral POMDP model unnecessarily increases the problem complex(cid: 173) ity. A variant of the Baum-Welch algorithm is developed for model learning requiring less data and time.

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