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Title: Bayesian approach for near-duplicate image detection |
Abstract: In this paper we propose a bayesian approach for near-duplicate image detection, and investigate how different probabilistic models affect the performance obtained. The task of identifying an image whose metadata are missing is often demanded for a myriad of applications: metadata retrieval in cultural instit... |
Title: Clustering Partially Observed Graphs via Convex Optimization |
Abstract: This paper considers the problem of clustering a partially observed unweighted graph---i.e., one where for some node pairs we know there is an edge between them, for some others we know there is no edge, and for the remaining we do not know whether or not there is an edge. We want to organize the nodes into d... |
Title: Fast global convergence of gradient methods for high-dimensional statistical recovery |
Abstract: Many statistical $M$-estimators are based on convex optimization problems formed by the combination of a data-dependent loss function with a norm-based regularizer. We analyze the convergence rates of projected gradient and composite gradient methods for solving such problems, working within a high-dimensiona... |
Title: Hybrid Tractable Classes of Binary Quantified Constraint Satisfaction Problems |
Abstract: In this paper, we investigate the hybrid tractability of binary Quantified Constraint Satisfaction Problems (QCSPs). First, a basic tractable class of binary QCSPs is identified by using the broken-triangle property. In this class, the variable ordering for the broken-triangle property must be same as that in... |
Title: On the half-Cauchy prior for a global scale parameter |
Abstract: This paper argues that the half-Cauchy distribution should replace the inverse-Gamma distribution as a default prior for a top-level scale parameter in Bayesian hierarchical models, at least for cases where a proper prior is necessary. Our arguments involve a blend of Bayesian and frequentist reasoning, and a... |
Title: A Machine Learning Based Analytical Framework for Semantic Annotation Requirements |
Abstract: The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the c... |
Title: Combining Ontology Development Methodologies and Semantic Web Platforms for E-government Domain Ontology Development |
Abstract: One of the key challenges in electronic government (e-government) is the development of systems that can be easily integrated and interoperated to provide seamless services delivery to citizens. In recent years, Semantic Web technologies based on ontology have emerged as promising solutions to the above engin... |
Title: Preprocessing: A Step in Automating Early Detection of Cervical Cancer |
Abstract: This paper has been withdrawn |
Title: Arc Consistency and Friends |
Abstract: A natural and established way to restrict the constraint satisfaction problem is to fix the relations that can be used to pose constraints; such a family of relations is called a constraint language. In this article, we study arc consistency, a heavily investigated inference method, and three extensions there... |
Title: Reducing Commitment to Tasks with Off-Policy Hierarchical Reinforcement Learning |
Abstract: In experimenting with off-policy temporal difference (TD) methods in hierarchical reinforcement learning (HRL) systems, we have observed unwanted on-policy learning under reproducible conditions. Here we present modifications to several TD methods that prevent unintentional on-policy learning from occurring. ... |
Title: On Combining Machine Learning with Decision Making |
Abstract: We present a new application and covering number bound for the framework of "Machine Learning with Operational Costs (MLOC)," which is an exploratory form of decision theory. The MLOC framework incorporates knowledge about how a predictive model will be used for a subsequent task, thus combining machine learn... |
Title: Synthesizing Robust Plans under Incomplete Domain Models |
Abstract: Most current planners assume complete domain models and focus on generating correct plans. Unfortunately, domain modeling is a laborious and error-prone task. While domain experts cannot guarantee completeness, often they are able to circumscribe the incompleteness of the model by providing annotations as to ... |
Title: Online Learning: Stochastic and Constrained Adversaries |
Abstract: Learning theory has largely focused on two main learning scenarios. The first is the classical statistical setting where instances are drawn i.i.d. from a fixed distribution and the second scenario is the online learning, completely adversarial scenario where adversary at every time step picks the worst insta... |
Title: Attacking and Defending Covert Channels and Behavioral Models |
Abstract: In this paper we present methods for attacking and defending $k$-gram statistical analysis techniques that are used, for example, in network traffic analysis and covert channel detection. The main new result is our demonstration of how to use a behavior's or process' $k$-order statistics to build a stochastic... |
Title: File Transfer Application For Sharing Femto Access |
Abstract: In wireless access network optimization, today's main challenges reside in traffic offload and in the improvement of both capacity and coverage networks. The operators are interested in solving their localized coverage and capacity problems in areas where the macro network signal is not able to serve the dema... |
Title: Flow-dependent unfolding and refolding of an RNA by nonequilibrium umbrella sampling |
Abstract: Nonequilibrium experiments of single biomolecules such as force-induced unfolding reveal details about a few degrees of freedom of a complex system. Molecular dynamics simulations can provide complementary information, but exploration of the space of possible configurations is often hindered by large barriers... |
Title: Finding Dense Clusters via "Low Rank + Sparse" Decomposition |
Abstract: Finding "densely connected clusters" in a graph is in general an important and well studied problem in the literature . It has various applications in pattern recognition, social networking and data mining . Recently, Ames and Vavasis have suggested a novel method for finding cliques in a graph by using conve... |
Title: Learning Undirected Graphical Models with Structure Penalty |
Abstract: In undirected graphical models, learning the graph structure and learning the functions that relate the predictive variables (features) to the responses given the structure are two topics that have been widely investigated in machine learning and statistics. Learning graphical models in two stages will have p... |
Title: Iterative Reweighted Algorithms for Sparse Signal Recovery with Temporally Correlated Source Vectors |
Abstract: Iterative reweighted algorithms, as a class of algorithms for sparse signal recovery, have been found to have better performance than their non-reweighted counterparts. However, for solving the problem of multiple measurement vectors (MMVs), all the existing reweighted algorithms do not account for temporal c... |
Title: Content-Based Spam Filtering on Video Sharing Social Networks |
Abstract: In this work we are concerned with the detection of spam in video sharing social networks. Specifically, we investigate how much visual content-based analysis can aid in detecting spam in videos. This is a very challenging task, because of the high-level semantic concepts involved; of the assorted nature of s... |
Title: A supervised clustering approach for fMRI-based inference of brain states |
Abstract: We propose a method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior during a scanning session. Such predictions suffer from the huge number of brain regions sampled on the voxel grid of standard fMRI data sets: the curse ... |
Title: Joint estimation of linear non-Gaussian acyclic models |
Abstract: A linear non-Gaussian structural equation model called LiNGAM is an identifiable model for exploratory causal analysis. Previous methods estimate a causal ordering of variables and their connection strengths based on a single dataset. However, in many application domains, data are obtained under different con... |
Title: Selected Operations, Algorithms, and Applications of n-Tape Weighted Finite-State Machines |
Abstract: A weighted finite-state machine with n tapes (n-WFSM) defines a rational relation on n strings. It is a generalization of weighted acceptors (one tape) and transducers (two tapes). After recalling some basic definitions about n-ary weighted rational relations and n-WFSMs, we summarize some central operations ... |
Title: On Optimality of Greedy Policy for a Class of Standard Reward Function of Restless Multi-armed Bandit Problem |
Abstract: In this paper,we consider the restless bandit problem, which is one of the most well-studied generalizations of the celebrated stochastic multi-armed bandit problem in decision theory. However, it is known be PSPACE-Hard to approximate to any non-trivial factor. Thus the optimality is very difficult to obtain... |
Title: Learning false discovery rates by fitting sigmoidal threshold functions |
Abstract: False discovery rates (FDR) are typically estimated from a mixture of a null and an alternative distribution. Here, we study a complementary approach proposed by Rice and Spiegelhalter (2008) that uses as primary quantities the null model and a parametric family for the local false discovery rate. Specificall... |
Title: Unsupervised Classification for Tiling Arrays: ChIP-chip and Transcriptome |
Abstract: Tiling arrays make possible a large scale exploration of the genome thanks to probes which cover the whole genome with very high density until 2 000 000 probes. Biological questions usually addressed are either the expression difference between two conditions or the detection of transcribed regions. In this w... |
Title: Notes on a New Philosophy of Empirical Science |
Abstract: This book presents a methodology and philosophy of empirical science based on large scale lossless data compression. In this view a theory is scientific if it can be used to build a data compression program, and it is valuable if it can compress a standard benchmark database to a small size, taking into accou... |
Title: Distributed Delayed Stochastic Optimization |
Abstract: We analyze the convergence of gradient-based optimization algorithms that base their updates on delayed stochastic gradient information. The main application of our results is to the development of gradient-based distributed optimization algorithms where a master node performs parameter updates while worker n... |
Title: Limits of Preprocessing |
Abstract: We present a first theoretical analysis of the power of polynomial-time preprocessing for important combinatorial problems from various areas in AI. We consider problems from Constraint Satisfaction, Global Constraints, Satisfiability, Nonmonotonic and Bayesian Reasoning. We show that, subject to a complexity... |
Title: Mean-Variance Optimization in Markov Decision Processes |
Abstract: We consider finite horizon Markov decision processes under performance measures that involve both the mean and the variance of the cumulative reward. We show that either randomized or history-based policies can improve performance. We prove that the complexity of computing a policy that maximizes the mean rew... |
Title: Learning high-dimensional directed acyclic graphs with latent and selection variables |
Abstract: We consider the problem of learning causal information between random variables in directed acyclic graphs (DAGs) when allowing arbitrarily many latent and selection variables. The FCI (Fast Causal Inference) algorithm has been explicitly designed to infer conditional independence and causal information in su... |
Title: Model Selection Consistency for Cointegrating Regressions |
Abstract: We study the asymptotic properties of the adaptive Lasso in cointegration regressions in the case where all covariates are weakly exogenous. We assume the number of candidate I(1) variables is sub-linear with respect to the sample size (but possibly larger) and the number of candidate I(0) variables is polyno... |
Title: Preference elicitation and inverse reinforcement learning |
Abstract: We state the problem of inverse reinforcement learning in terms of preference elicitation, resulting in a principled (Bayesian) statistical formulation. This generalises previous work on Bayesian inverse reinforcement learning and allows us to obtain a posterior distribution on the agent's preferences, policy... |
Title: An Automated Size Recognition Technique for Acetabular Implant in Total Hip Replacement |
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