406 research outputs found
Understanding structure of concurrent actions
Whereas most work in reinforcement learning (RL) ignores the structure or relationships between actions, in this paper we show that exploiting structure in the action space can improve sample efficiency during exploration. To show this we focus on concurrent action spaces where the RL agent selects multiple actions per timestep. Concurrent action spaces are challenging to learn in especially if the number of actions is large as this can lead to a combinatorial explosion of the action space.
This paper proposes two methods: a first approach uses implicit structure to perform high-level action elimination using task-invariant actions; a second approach looks for more explicit structure in the form of action clusters. Both methods are context-free, focusing only on an analysis of the action space and show a significant improvement in policy convergence times
Consistent Estimation of Mixed Memberships with Successive Projections
This paper considers the parameter estimation problem in Mixed Membership
Stochastic Block Model (MMSB), which is a quite general instance of random
graph model allowing for overlapping community structure. We present the new
algorithm successive projection overlapping clustering (SPOC) which combines
the ideas of spectral clustering and geometric approach for separable
non-negative matrix factorization. The proposed algorithm is provably
consistent under MMSB with general conditions on the parameters of the model.
SPOC is also shown to perform well experimentally in comparison to other
algorithms
SACOC: A spectral-based ACO clustering algorithm
The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, where ACO-based techniques have showed a great potential. At the same time, new clustering techniques that seek the continuity of data, specially focused on spectral-based approaches in opposition to classical centroid-based approaches, have attracted an increasing research interest–an area still under study by ACO clustering techniques. This work presents a hybrid spectral-based ACO clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach combines ACOC with the spectral Laplacian to generate a new search space for the algorithm in order to obtain more promising solutions. The new algorithm, called SACOC, has been compared against well-known algorithms (K-means and Spectral Clustering) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository
Alternative sampling for variational quantum Monte Carlo
Expectation values of physical quantities may accurately be obtained by the
evaluation of integrals within Many-Body Quantum mechanics, and these
multi-dimensional integrals may be estimated using Monte Carlo methods. In a
previous publication it has been shown that for the simplest, most commonly
applied strategy in continuum Quantum Monte Carlo, the random error in the
resulting estimates is not well controlled. At best the Central Limit theorem
is valid in its weakest form, and at worst it is invalid and replaced by an
alternative Generalised Central Limit theorem and non-Normal random error. In
both cases the random error is not controlled. Here we consider a new `residual
sampling strategy' that reintroduces the Central Limit Theorem in its strongest
form, and provides full control of the random error in estimates. Estimates of
the total energy and the variance of the local energy within Variational Monte
Carlo are considered in detail, and the approach presented may be generalised
to expectation values of other operators, and to other variants of the Quantum
Monte Carlo method.Comment: 14 pages, 9 figure
Large Scale Spectral Clustering Using Approximate Commute Time Embedding
Spectral clustering is a novel clustering method which can detect complex
shapes of data clusters. However, it requires the eigen decomposition of the
graph Laplacian matrix, which is proportion to and thus is not
suitable for large scale systems. Recently, many methods have been proposed to
accelerate the computational time of spectral clustering. These approximate
methods usually involve sampling techniques by which a lot information of the
original data may be lost. In this work, we propose a fast and accurate
spectral clustering approach using an approximate commute time embedding, which
is similar to the spectral embedding. The method does not require using any
sampling technique and computing any eigenvector at all. Instead it uses random
projection and a linear time solver to find the approximate embedding. The
experiments in several synthetic and real datasets show that the proposed
approach has better clustering quality and is faster than the state-of-the-art
approximate spectral clustering methods
Comparing spectra of graph shift operator matrices
Typically network structures are represented by one of three different graph shift operator matrices: the adjacency matrix and unnormalised and normalised Laplacian matrices. To enable a sensible comparison of their spectral (eigenvalue) properties, an affine transform is first applied to one of them, which preserves eigengaps. Bounds, which depend on the minimum and maximum degree of the network, are given on the resulting eigenvalue differences. The monotonicity of the bounds and the structure of networks are related. Bounds, which again depend on the minimum and maximum degree of the network, are also given for normalised eigengap differences, used in spectral clustering. Results are illustrated on the karate dataset and a stochastic block model. If the degree extreme difference is large, different choices of graph shift operator matrix may give rise to disparate inference drawn from network analysis; contrariwise, smaller degree extreme difference results in consistent inference
Incremental learning with social media data to predict near real-time events
International audienceIn this paper, we focus on the problem of predicting some particular user activities in social media. Our challenge is to consider real events such as message posting to friends or forwarding received ones, connecting to new friends, and provide near real-time prediction of new events. Our approach is based on latent factor models which can exploit simultaneously the timestamped interaction information among users and their posted content information. We propose a simple strategy to learn incrementally the latent factors at each time step. Our method takes only recent data to update latent factor models and thus can reduce computational cost. Experiments on a real dataset collected from Twitter show that our method can achieve performances that are comparable with other state-of-the-art non-incremental techniques
Fast Gaussian Pairwise Constrained Spectral Clustering
International audienceWe consider the problem of spectral clustering with partial supervision in the form of must-link and cannot-link constraints. Such pairwise constraints are common in problems like coreference resolution in natural language processing. The approach developed in this paper is to learn a new representation space for the data together with a dis-tance in this new space. The representation space is obtained through a constraint-driven linear transformation of a spectral embedding of the data. Constraints are expressed with a Gaussian function that locally reweights the similarities in the projected space. A global, non-convex optimization objective is then derived and the model is learned via gradi-ent descent techniques. Our algorithm is evaluated on standard datasets and compared with state of the art algorithms, like [14,18,31]. Results on these datasets, as well on the CoNLL-2012 coreference resolution shared task dataset, show that our algorithm significantly outperforms related approaches and is also much more scalable
A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks
This paper presents a novel spectral algorithm with additive clustering
designed to identify overlapping communities in networks. The algorithm is
based on geometric properties of the spectrum of the expected adjacency matrix
in a random graph model that we call stochastic blockmodel with overlap (SBMO).
An adaptive version of the algorithm, that does not require the knowledge of
the number of hidden communities, is proved to be consistent under the SBMO
when the degrees in the graph are (slightly more than) logarithmic. The
algorithm is shown to perform well on simulated data and on real-world graphs
with known overlapping communities.Comment: Journal of Theoretical Computer Science (TCS), Elsevier, A Para\^itr
Graph similarity through entropic manifold alignment
In this paper we decouple the problem of measuring graph similarity into two sequential steps. The first step is the linearization of the quadratic assignment problem (QAP) in a low-dimensional space, given by the embedding trick. The second step is the evaluation of an information-theoretic distributional measure, which relies on deformable manifold alignment. The proposed measure is a normalized conditional entropy, which induces a positive definite kernel when symmetrized. We use bypass entropy estimation methods to compute an approximation of the normalized conditional entropy. Our approach, which is purely topological (i.e., it does not rely on node or edge attributes although it can potentially accommodate them as additional sources of information) is competitive with state-of-the-art graph matching algorithms as sources of correspondence-based graph similarity, but its complexity is linear instead of cubic (although the complexity of the similarity measure is quadratic). We also determine that the best embedding strategy for graph similarity is provided by commute time embedding, and we conjecture that this is related to its inversibility property, since the inverse of the embeddings obtained using our method can be used as a generative sampler of graph structure.The work of the first and third authors was supported by the projects TIN2012-32839 and TIN2015-69077-P of the Spanish Government. The work of the second author was supported by a Royal Society Wolfson Research Merit Award
- …