3,864 research outputs found
Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games
With the recent advances in solving large, zero-sum extensive form games,
there is a growing interest in the inverse problem of inferring underlying game
parameters given only access to agent actions. Although a recent work provides
a powerful differentiable end-to-end learning frameworks which embed a game
solver within a deep-learning framework, allowing unknown game parameters to be
learned via backpropagation, this framework faces significant limitations when
applied to boundedly rational human agents and large scale problems, leading to
poor practicality. In this paper, we address these limitations and propose a
framework that is applicable for more practical settings. First, seeking to
learn the rationality of human agents in complex two-player zero-sum games, we
draw upon well-known ideas in decision theory to obtain a concise and
interpretable agent behavior model, and derive solvers and gradients for
end-to-end learning. Second, to scale up to large, real-world scenarios, we
propose an efficient first-order primal-dual method which exploits the
structure of extensive-form games, yielding significantly faster computation
for both game solving and gradient computation. When tested on randomly
generated games, we report speedups of orders of magnitude over previous
approaches. We also demonstrate the effectiveness of our model on both
real-world one-player settings and synthetic data
Dynamic structure of stock communities: A comparative study between stock returns and turnover rates
The detection of community structure in stock market is of theoretical and
practical significance for the study of financial dynamics and portfolio risk
estimation. We here study the community structures in Chinese stock markets
from the aspects of both price returns and turnover rates, by using a
combination of the PMFG and infomap methods based on a distance matrix. We find
that a few of the largest communities are composed of certain specific industry
or conceptional sectors and the correlation inside a sector is generally larger
than the correlation between different sectors. In comparison with returns, the
community structure for turnover rates is more complex and the sector effect is
relatively weaker. The financial dynamics is further studied by analyzing the
community structures over five sub-periods. Sectors like banks, real estate,
health care and New Shanghai take turns to compose a few of the largest
communities for both returns and turnover rates in different sub-periods.
Several specific sectors appear in the communities with different rank orders
for the two time series even in the same sub-period. A comparison between the
evolution of prices and turnover rates of stocks from these sectors is
conducted to better understand their differences. We find that stock prices
only had large changes around some important events while turnover rates surged
after each of these events relevant to specific sectors, which may offer a
possible explanation for the complexity of stock communities for turnover
rates
Rethinking Item Importance in Session-based Recommendation
Session-based recommendation aims to predict users' based on anonymous
sessions. Previous work mainly focuses on the transition relationship between
items during an ongoing session. They generally fail to pay enough attention to
the importance of the items in terms of their relevance to user's main intent.
In this paper, we propose a Session-based Recommendation approach with an
Importance Extraction Module, i.e., SR-IEM, that considers both a user's
long-term and recent behavior in an ongoing session. We employ a modified
self-attention mechanism to estimate item importance in a session, which is
then used to predict user's long-term preference. Item recommendations are
produced by combining the user's long-term preference and current interest as
conveyed by the last interacted item. Experiments conducted on two benchmark
datasets validate that SR-IEM outperforms the start-of-the-art in terms of
Recall and MRR and has a reduced computational complexity
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Using CBR to improve the usability of numerical models
In this thesis we show that CBR systems can be constructed from numerical models, so as to improve their usability. It is shown that CBR models may be queried in a flexible manner, and that the user may formulate queries consisting of constraints over both “input” and “output” variables of the numerical model. It is also shown that the constraints may be formulated using either nominal or continuous variables. A generalization of the CBR retrieval process to include constraints over unified “input-output” space is formulated as a framework for the method.
The method is illustrated with practical engineering models: the pneumatic conveyor problem and the projectile problem. Comparisons are made on usability of CBR and numerical models for specific problems. It is shown that CBR models can answer questions difficult or impossible to formulate using numerical models, and that CBR models can be faster.
The thesis also addresses a latent problem with the general method, which is of importance generally. This is to do with interpolation over nominal values in unified space. A novel method is proposed for interpolation over nominal values, termed Generalised Shepard Nearest Neighbour method (GSNN). GSNN can utilise distance metrics defined on the solution space of a CBR system.
The properties and advantages of GSNN are examined in the thesis. A comparison is made with other CBR retrieval methods, using several examples, including the travel domain case base. It is shown that GSNN can out-perform conventional nearest neighbour methods. It is shown that GSNN has advantages in that it can find solutions not in the case base and it can find solutions not in the retrieval set. It is also shown that the performance of GSNN can be improved further by using it in conjunction with a diversity algorithm. The merit of using GSNN as a case selection component is examined, and it is shown that it can give good results in sparse case bases.
Finally the thesis concludes with a survey of numerical models where CBR construction can be useful, and where benefits can be expected
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