Jia Yuan Yu

Hamid Nabati, Jia Yuan Yu

Efficient resource allocation is challenging when privacy of users is important. Distributed approaches have recently been used extensively to find a solution for such problems. In this work, the efficiency of distributed AIMD algorithm for allocation of subsidized goods is studied. First, a suitable utility function is assigned to each user describing the amount of satisfaction that it has from allocated resource. Then the resource allocation is defined as a total utilitarianism problem that is an optimization problem of sum of users utility functions subjected to capacity constraint. Recently, a stochastic state-dependent variant of AIMD algorithm is used for allocation of common goods among users with strictly increasing and concave utility functions. Here, the stochastic AIMD algorithm is derandomized and its efficiency is compared with the stochastic version. Moreover, the algorithm is improved to allocate subsidized goods to users with concave and non-monotone utility functions as well as users with Sigmoidal utility functions. To illustrate the effectiveness of the proposed solutions, simulation results is presented for a public renewable-energy powered charging station in which the electric vehicles (EV) compete to be recharged.

Ningsheng Zhao, Trang Bui, Jia Yuan Yu et al.

Many classification performance metrics exist, each suited to a specific application. However, these metrics often differ in scale and can exhibit varying sensitivity to class imbalance rates in the test set. As a result, it is difficult to use the nominal values of these metrics to interpret and evaluate classification performances, especially when imbalance rates vary. To address this problem, we introduce the outperformance score function, a universal standardization method for confusion-matrix-based classification performance (CMBCP) metrics. It maps any given metric to a common scale of $[0,1]$, while providing a clear and consistent interpretation. Specifically, the outperformance score represents the percentile rank of the observed classification performance within a reference distribution of possible performances. This unified framework enables meaningful comparison and monitoring of classification performance across test sets with differing imbalance rates. We illustrate how the outperformance scores can be applied to a variety of commonly used classification performance metrics and demonstrate the robustness of our method through experiments on real-world datasets spanning multiple classification applications.

Long Tran-Thanh, Jia Yuan Yu

We introduce the functional bandit problem, where the objective is to find an arm that optimises a known functional of the unknown arm-reward distributions. These problems arise in many settings such as maximum entropy methods in natural language processing, and risk-averse decision-making, but current best-arm identification techniques fail in these domains. We propose a new approach, that combines functional estimation and arm elimination, to tackle this problem. This method achieves provably efficient performance guarantees. In addition, we illustrate this method on a number of important functionals in risk management and information theory, and refine our generic theoretical results in those cases.