We study a classification problem where each feature can be acquired for a cost and the goal is to optimize the trade-off between classification precision and the total feature cost. We frame the problem as a sequential decision-making problem, where we classify one sample in each episode. At each step, an agent can use values of acquired features to decide whether to purchase another one or wh...

This paper aims to use the previous work related to the DELPHI method, and, in particular, the Q-Sort method for information retrieval of a panel of experts, to provide a new and simple algorithm to generate Q-Sort matrices that adjust to the size of a given survey to have more questions whose weight is null for the outcome of the round, giving experts the need to prioritise some questions abov...

We present Q-functionals, an alternative architecture for continuous control deep reinforcement learning. Instead of returning a single value state-action pair, our network transforms state into function that can be rapidly evaluated in parallel many actions, allowing us to efficiently choose high-value actions through sampling. This contrasts with the typical off-policy control, where policy i...

this study examines the role of growth and value stocks in liquidity and tobin's q ratio, and also the validity of claims existing difference between tobin q ratio of the two types of stocks in companies accepted in tehran stock exchange has been examined. the study period is between the years 1381 to 1390. we use pooled panel-data regression and egls and gmm methods to estimate the model ...