Automatic portfolio optimization (APO) is the process of automatically optimizing the allocation of assets in an investment portfolio through algorithms and models. Empirical evidence suggests that stock prices are likely to follow the mean reversion theory. Although existing mean reversion strategies for APO have been shown to achieve good empirical performance across many real-world datasets, they tend to construct single-stock portfolios, which results in high risk. In this paper, we propose a risk diversification version of Online Moving Average Reversion (OLMAR) [16], one of the major mean reversion strategies. The parameter k of our proposed method OLMAR-k controls the search space for optimization to guarantee the existence of solutions and prevent from solutions close to uniform. According to experimental results in resent datasets, OLMAR-k outperforms other state-of-the-art APO methods in terms of popular metrics for return and risk.

A budget-constrained combinatorial optimal facility investment problem is a problem that optimally locates multiple facilities on the vertices of a given undirected graph and allocates many different types of equipment to each facility under a budget constraint. The objective function is not given explicitly, and we can know the evaluation of our investment through a given noisy blackbox function. We propose a search method using Monte Carlo tree search (MCTS) for this problem with a noisy blackbox function. In our search through MCTS, we make use of the lattice structure of subspaces defined using a hierarchical range partition for each variable and reinforce to select good order of variable range partitions preferentially using the upper confidence bound score, into which rapid action value estimation method is incorporated, for selection operations. The effectiveness of our proposed method is demonstrated through the experiments for the layout optimization problem of distributed small power grids (nanogrids) using a public simulator and data that are based on real-world maps, population, and weather as a noisy blackbox function and another setting including an undirected graph, respectively.

Although differentially private streaming data release has been studied extensively, how to strike a good balance between privacy and utility on correlated data is still an open problem. Many existing works focus on enhancing privacy when applying differential privacy to correlated data. They show that differential privacy may suffer extra privacy leakage under correlations, and it is inevitable to resort to a small privacy budget to prevent such privacy leakage. However, there is no attempt to solve the consequential utility problem. In this work, for the first time, we propose a post-processing framework to boost the utility of differential privacy data release under temporal correlations. Specifically, we model the problem as a maximum posterior estimation given the released differentially private data and correlation model. We finally transform this problem into a nonlinear constrained programming. Our experiments demonstrate the effectiveness of the proposed approach where the utility and accuracy of differentially private data are significantly improved by nearly ten times in terms of mean square error when a strict privacy budget is given.

Monte Carlo tree search (MCTS) has received considerable interest due to its spectacular success in the difficult problem of computer Go and also proved beneficial in a range of other domains. A major issue that has received little attention in the MCTS literature is the fact that, in most games, different actions can lead to the same state, that may lead to a high degree of redundancy in tree representation and unnecessary additional computational cost. We extend MCTS to single rooted directed acyclic graph (SR-DAG), and consider the Best Arm Identification (BAI) and the Best Leaf Identification (BLI) problem of an expanding SR-DAG of arbitrary depth. We propose algorithms that are (∊, σ)-correct in the fixed confidence setting, and prove an asymptotic upper bounds of sample complexity for our BAI algorithm. As a major application for our BLI algorithm, a novel approach for Feature Selection is proposed by representing the feature set space as a SR-DAG and repeatedly evaluating feature subsets until a candidate for the best leaf is returned, a proof of concept is shown on benchmark data sets.

We study the problem of enumerating concepts in a Sperner family concept class using subconcept queries, which is a general problem including maximal frequent itemset mining as its instance. Though even the theoretically best known algorithm needs quasi-polynomial time to solve this problem in the worst case, there exist practically fast algorithms for this problem. This is because many instances of this problem in real world have low complexity in some measures. In this paper, we characterize the complexity of Sperner family concept class by the VC dimension of its intersection closure and its characteristic dimension, and analyze the worst case time complexity on the enumeration problem of its concepts in terms of the VC dimension. We also showed that the VC dimension of real data used in data mining is actually small by calculating the VC dimension of some real datasets using a new algorithm closely related to the introduced two measures, which does not only solve the problem but also let us know the VC dimension of the intersection closure of the target concept class.

We introduce a stricter Web community definition to overcome boundary ambiguity of a Web community defined by Flake, Lawrence and Giles [2], and consider the problem of finding communities that satisfy our definition. We discuss how to find such communities and hardness of this problem.We also propose Web page partitioning by equivalence relation defined using the class of communities of our definition. Though the problem of efficiently finding all communities of our definition is NP-complete, we propose an efficient method of finding a subclass of communities among the sets partitioned by each of n-1 cuts represented by a Gomory-Hu tree [10], and partitioning a Web graph by equivalence relation defined using the subclass.According to our preliminary experiments, partitioning by our method divided the pages retrieved by keyword search into several different categories to some extent.

We addressed two issues concerning the practical aspects of optimally scheduling web advertising proposed by Langheinrich et al. [5], which scheduling maximizes the total number of click-throughs for all banner advertisements. One is the problem of multi-impressions in which two or more banner ads are impressed at the same time. The other is inventory management, which is important in order to prevent over-selling and maximize revenue. We propose efficient methods which deal with these two issues.