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12
Diffusion Wavelets
, 2004
"... We present a multiresolution construction for efficiently computing, compressing and applying large powers of operators that have high powers with low numerical rank. This allows the fast computation of functions of the operator, notably the associated Green’s function, in compressed form, and their ..."
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Cited by 73 (12 self)
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We present a multiresolution construction for efficiently computing, compressing and applying large powers of operators that have high powers with low numerical rank. This allows the fast computation of functions of the operator, notably the associated Green’s function, in compressed form, and their fast application. Classes of operators satisfying these conditions include diffusionlike operators, in any dimension, on manifolds, graphs, and in nonhomogeneous media. In this case our construction can be viewed as a farreaching generalization of Fast Multipole Methods, achieved through a different point of view, and of the nonstandard wavelet representation of CalderónZygmund and pseudodifferential operators, achieved through a different multiresolution analysis adapted to the operator. We show how the dyadic powers of an operator can be used to induce a multiresolution analysis, as in classical LittlewoodPaley and wavelet theory, and we show how to construct, with fast and stable algorithms, scaling function and wavelet bases associated to this multiresolution analysis, and the corresponding downsampling operators, and use them to compress the corresponding powers of the operator. This allows to extend multiscale signal processing to general spaces (such as manifolds and graphs) in a very natural way, with corresponding fast algorithms.
Protovalue functions: A laplacian framework for learning representation and control in markov decision processes
 Journal of Machine Learning Research
, 2006
"... This paper introduces a novel spectral framework for solving Markov decision processes (MDPs) by jointly learning representations and optimal policies. The major components of the framework described in this paper include: (i) A general scheme for constructing representations or basis functions by d ..."
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Cited by 67 (9 self)
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This paper introduces a novel spectral framework for solving Markov decision processes (MDPs) by jointly learning representations and optimal policies. The major components of the framework described in this paper include: (i) A general scheme for constructing representations or basis functions by diagonalizing symmetric diffusion operators (ii) A specific instantiation of this approach where global basis functions called protovalue functions (PVFs) are formed using the eigenvectors of the graph Laplacian on an undirected graph formed from state transitions induced by the MDP (iii) A threephased procedure called representation policy iteration comprising of a sample collection phase, a representation learning phase that constructs basis functions from samples, and a final parameter estimation phase that determines an (approximately) optimal policy within the (linear) subspace spanned by the (current) basis functions. (iv) A specific instantiation of the RPI framework using leastsquares policy iteration (LSPI) as the parameter estimation method (v) Several strategies for scaling the proposed approach to large discrete and continuous state spaces, including the Nyström extension for outofsample interpolation of eigenfunctions, and the use of Kronecker sum factorization to construct compact eigenfunctions in product spaces such as factored MDPs (vi) Finally, a series of illustrative discrete and continuous control tasks, which both illustrate the concepts and provide a benchmark for evaluating the proposed approach. Many challenges remain to be addressed in scaling the proposed framework to large MDPs, and several elaboration of the proposed framework are briefly summarized at the end.
Regularization on graphs with functionadapted diffusion process
, 2006
"... Harmonic analysis and diffusion on discrete data has been shown to lead to stateoftheart algorithms for machine learning tasks, especially in the context of semisupervised and transductive learning. The success of these algorithms rests on the assumption that the function(s) to be studied (learn ..."
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Cited by 23 (5 self)
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Harmonic analysis and diffusion on discrete data has been shown to lead to stateoftheart algorithms for machine learning tasks, especially in the context of semisupervised and transductive learning. The success of these algorithms rests on the assumption that the function(s) to be studied (learned, interpolated, etc.) are smooth with respect to the geometry of the data. In this paper we present a method for modifying the given geometry so the function(s) to be studied are smoother with respect to the modified geometry, and thus more amenable to treatment using harmonic analysis methods. Among the many possible applications, we consider the problems of image denoising and transductive classification. In both settings, our approach improves on standard diffusion based methods.
Representation Policy Iteration
, 2005
"... This paper addresses a fundamental issue central to approximation methods for solving large Markov decision processes (MDPs): how to automatically learn the underlying representation for value function approximation? A novel theoretically rigorous framework is proposed that automatically generates g ..."
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Cited by 21 (8 self)
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This paper addresses a fundamental issue central to approximation methods for solving large Markov decision processes (MDPs): how to automatically learn the underlying representation for value function approximation? A novel theoretically rigorous framework is proposed that automatically generates geometrically customized orthonormal sets of basis functions, which can be used with any approximate MDP solver like leastsquares policy iteration (LSPI). The key innovation is a coordinatefree representation of value functions, using the theory of smooth functions on a Riemannian manifold. Hodge theory yields a constructive method for generating basis functions for approximating value functions based on the eigenfunctions of the selfadjoint (LaplaceBeltrami) operator on manifolds. In effect, this approach performs a global Fourier analysis on the state space graph to approximate value functions, where the basis functions reflect the largescale topology of the underlying state space. A new class of algorithms called Representation Policy Iteration (RPI) are presented that automatically learn both basis functions and approximately optimal policies. Illustrative experiments compare the performance of RPI with that of LSPI using two handcoded basis functions (RBF and polynomial state encodings).
Diffusion polynomial frames on metric measure spaces. submitted
, 2006
"... We construct a multiscale tight frame based on an arbitrary orthonormal basis for the L 2 space of an arbitrary sigma finite measure space. The approximation properties of the resulting multiscale are studied in the context of Besov approximation spaces, which are characterized both in terms of suit ..."
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Cited by 13 (6 self)
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We construct a multiscale tight frame based on an arbitrary orthonormal basis for the L 2 space of an arbitrary sigma finite measure space. The approximation properties of the resulting multiscale are studied in the context of Besov approximation spaces, which are characterized both in terms of suitable K–functionals and the frame transforms. The only major condition required is the uniform boundedness of a summabilility operator. We give sufficient conditions for this to hold in the context of a very general class of metric measure spaces. The theory is illustrated using the approximation of characteristic functions of caps on a dumbell manifold, and applied to the problem of recognition of hand–written digits. Our methods outperforms comparable methods for semi–supervised learning.
Approximate Dynamic Programming with Applications in MultiAgent Systems
, 2007
"... This thesis presents the development and implementation of approximate dynamic programming methods used to manage multiagent systems. The purpose of this thesis is to develop an architectural framework and theoretical methods that enable an autonomous mission system to manage realtime multiagent ..."
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Cited by 9 (1 self)
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This thesis presents the development and implementation of approximate dynamic programming methods used to manage multiagent systems. The purpose of this thesis is to develop an architectural framework and theoretical methods that enable an autonomous mission system to manage realtime multiagent operations. To meet this goal, we begin by discussing aspects of the realtime multiagent mission problem. Next, we formulate this problem as a Markov Decision Process (MDP) and present a system architecture designed to improve missionlevel functional reliability through system selfawareness and adaptive mission planning. Since most multiagent mission problems are computationally difficult to solve in realtime, approximation techniques are needed to find policies for these largescale problems. Thus, we have developed
Multiscale analysis of document corpora based on diffusion models
 In Proceedings of the 21st International Joint Conference on Artificial Intelligence
"... We introduce a nonparametric approach to multiscale analysis of document corpora using a hierarchical matrix analysis framework called diffusion wavelets. In contrast to eigenvector methods, diffusion wavelets construct multiscale basis functions. In this framework, a hierarchy is automatically cons ..."
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Cited by 7 (4 self)
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We introduce a nonparametric approach to multiscale analysis of document corpora using a hierarchical matrix analysis framework called diffusion wavelets. In contrast to eigenvector methods, diffusion wavelets construct multiscale basis functions. In this framework, a hierarchy is automatically constructed by an iterative series of dilation and orthogonalization steps beginning with an initial set of orthogonal basis functions, such as the unitvector bases. Each set of basis functions at a given level is constructed from the bases at the lower level by dilation using the dyadic powers of a diffusion operator. A novel aspect of our work is that the diffusion analysis is conducted on the space of variables (words), instead of instances (documents). This approach can automatically and efficiently determine the number of levels of the topical hierarchy, as well as the topics at each level. Multiscale analysis of document corpora is achieved by using the projections of the documents onto the spaces spanned by basis functions at different levels. Further, when the input termterm matrix is a “local” diffusion operator, the algorithm runs in time approximately linear in the number of nonzero elements of the matrix. The approach is illustrated on various data sets including NIPS conference papers, 20 Newsgroups and TDT2 data. 1
Fast Approximate Hierarchical Solution of MDPs
"... In this thesis, we present an efficient algorithm for creating and solving hierarchical models of large Markov decision processes (MDPs). As the size of the MDP increases, finding an exact solution becomes intractable, so we expect only to find an approximate solution. We also assume that the hierar ..."
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Cited by 2 (1 self)
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In this thesis, we present an efficient algorithm for creating and solving hierarchical models of large Markov decision processes (MDPs). As the size of the MDP increases, finding an exact solution becomes intractable, so we expect only to find an approximate solution. We also assume that the hierarchies we create are not necessarily applicable to more than one problem so that we must be able to construct and solve the hierarchical model in less time than it would have taken to simply solve the original, flat model. Our approach works in two stages. We first create the hierarchical MDP by forming clusters of states that can transition easily among themselves. We then solve the hierarchical MDP. We use a quick bottomup pass based on a deterministic approximation of expected costs to move from one state to another to derive a policy from the top down, which avoids solving lowlevel MDPs for multiple objectives. The
Representation Discovery in Planning using Harmonic Analysis ∗
"... This paper summarizes ongoing research on a framework for representation learning using harmonic analysis, a subfield of mathematics. Harmonic analysis includes Fourier analysis, where new eigenvector representations are constructed by diagonalization of operators, and wavelet analysis, where new re ..."
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Cited by 1 (1 self)
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This paper summarizes ongoing research on a framework for representation learning using harmonic analysis, a subfield of mathematics. Harmonic analysis includes Fourier analysis, where new eigenvector representations are constructed by diagonalization of operators, and wavelet analysis, where new representations are constructed by dilation. The approach is presented specifically in the context of Markov decision processes (MDPs), a widely studied model of planning under uncertainty, although the approach is applicable more broadly to other areas of AI as well. This paper describes a novel harmonic analysis framework for planning based on estimating a diffusion model that models flow of information on a graph (discrete state space) or a manifold (continuous state space) using a discrete form of the Laplace heat equation. Two methods for constructing novel plan representations from diffusion models are described: Fourier methods diagonalize a symmetric diffusion operator called the Laplacian; wavelet methods dilate unit basis functions progressively using powers of the diffusion operator. A new planning framework called Representation Policy Iteration (RPI) is described consisting of an outer loop that estimates new basis functions by diagonalization or dilation, and an inner loop that learns the best policy representable within the linear span of the current basis functions. We demonstrate the flexibility of the approach, which allows basis functions to be adapted to a particular task or reward function, and the hierarchical temporally extended nature of actions.
A GEOMETRIC FRAMEWORK FOR TRANSFER LEARNING USING MANIFOLD ALIGNMENT
, 2010
"... I would like to thank my thesis advisor, Sridhar Mahadevan. Sridhar has been such a wonderful advisor, and every aspect of this thesis has benefitted from his guidance and support throughout my graduate studies. I also like to thank Sridhar for giving me the flexibility to explore many different ide ..."
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Cited by 1 (0 self)
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I would like to thank my thesis advisor, Sridhar Mahadevan. Sridhar has been such a wonderful advisor, and every aspect of this thesis has benefitted from his guidance and support throughout my graduate studies. I also like to thank Sridhar for giving me the flexibility to explore many different ideas and research topics. I am appreciative of the support offered by my other thesis committee members, Andrew McCallum, Erik LearnedMiller, and Weibo Gong. Andrew helped me on CRF, MALLET and topic modeling. Erik helped me on computer vision. Weibo has many brilliant ideas on how brains work. I got a lot of inspirations from him. I am grateful for many other professors and staff members, who helped me along. Andy Barto offered me many insightful comments and advice on my research. David Kulp and Oliver Brock helped me on bioinformatics. Stephen Scott brought me to this country, taught me machine learning/bioinformatics and offers me constant support. Vadim Gladyshev helped me on biochemistry. Mauro Maggioni helped me on diffusion wavelets. I also thank Gwyn Mitchell and Leanne Leclerc for their help with my questions over the years. I am deeply thankful to my Master thesis advisor, Zhuzhi Yuan and other teachers in Nankai University for guiding my development as