no code implementations • 21 Apr 2024 • Ningsheng Zhao, Jia Yuan Yu, Krzysztof Dzieciolowski, Trang Bui
We theoretically analyze the potential over-informativeness and under-informativeness of existing Shapley value attribution methods.
no code implementations • 19 Feb 2022 • Farshid Faal, Ketra Schmitt, Jia Yuan Yu
Transformer-based language models are able to generate fluent text and be efficiently adapted across various natural language generation tasks.
no code implementations • 26 Apr 2021 • Syed Eqbal Alam, Fabian Wirth, Jia Yuan Yu
In a federated setting, agents coordinate with a central agent or a server to solve an optimization problem in which agents do not share their information with each other.
1 code implementation • 8 Mar 2021 • Dylan Troop, Frédéric Godin, Jia Yuan Yu
To mitigate this problem, the CVaR can be estimated by extrapolating above a lower threshold than the VaR using a generalized Pareto distribution (GPD), which is often referred to as the peaks-over-threshold (POT) approach.
no code implementations • 5 Mar 2020 • Mohammad Akif Beg, Jia Yuan Yu
Our goal is to generate a preview image which looks similar to an embroidered image, from a user-uploaded image.
1 code implementation • 3 Dec 2019 • Dylan Troop, Frédéric Godin, Jia Yuan Yu
To mitigate this problem, extreme value theory can be used to derive an estimator for the CVaR that uses extrapolation beyond available samples.
no code implementations • 9 Jul 2019 • Shuai Ma, Jia Yuan Yu, Ahmet Satir
For a given risk-sensitive problem, in which the objective and constraints are, or can be estimated by, functions of the mean and variance of return, we generate a synthetic dataset as training data.
no code implementations • 9 Jul 2019 • Shuai Ma, Jia Yuan Yu
In the numerical experiment, we illustrate state lumping in the SAT, errors from a naive reward simplification, and the validity of the SAT for the two risk estimations.
no code implementations • 17 Aug 2018 • Viral Thakar, Himani Saini, Walid Ahmed, Mohammad M Soltani, Ahmed Aly, Jia Yuan Yu
Asset monitoring in construction sites is an intricate, manually intensive task, that can highly benefit from automated solutions engineered using deep neural networks.
no code implementations • 17 Aug 2018 • Viral Thakar, Walid Ahmed, Mohammad M Soltani, Jia Yuan Yu
This uses data to reduce the uncertainty in the selection of best aspect ratios for the default boxes and improves performance of SSD for datasets containing small and complex objects (e. g., equipments at construction sites).
no code implementations • 16 Apr 2018 • Shuai Ma, Jia Yuan Yu
In the framework of MDP, although the general reward function takes three arguments-current state, action, and successor state; it is often simplified to a function of two arguments-current state and action.
no code implementations • 19 Dec 2017 • Pooyan Ehsani, Jia Yuan Yu
We propose an optimization algorithm to solve it.
no code implementations • 7 Dec 2016 • Shuai Ma, Jia Yuan Yu
Thirdly, since the estimation method is for a Markov reward process with the reward function on current state only, we present a transformation algorithm for the Markov reward process with the reward function on current and next states, in order to estimate the VaR function with an intact total reward distribution.
no code implementations • 25 Jan 2016 • Jakub Marecek, Robert Shorten, Jia Yuan Yu
For vehicle sharing schemes, where drop-off positions are not fixed, we propose a pricing scheme, where the price depends in part on the distance between where a vehicle is being dropped off and where the closest shared vehicle is parked.
no code implementations • 29 Sep 2015 • Yin-Lam Chow, Jia Yuan Yu, Marco Pavone
We consider one-way vehicle sharing systems where customers can rent a car at one station and drop it off at another.
no code implementations • 10 May 2014 • 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.
no code implementations • 9 Apr 2014 • Jakub Marecek, Robert Shorten, Jia Yuan Yu
A central authority has up-to-date knowledge of the congestion across all resources and uses randomisation to provide a scalar or an interval for each resource at each time.
no code implementations • NeurIPS 2013 • Martin Mevissen, Emanuele Ragnoli, Jia Yuan Yu
We consider robust optimization for polynomial optimization problems where the uncertainty set is a set of candidate probability density functions.
no code implementations • 20 May 2011 • Sébastien Gerchinovitz, Jia Yuan Yu
We first present regret bounds with optimal dependencies on $d$, $T$, and on the sizes $U$, $X$ and $Y$ of the $\ell^1$-ball, the input data and the observations.