1,285 research outputs found
A Class of Mean-field LQG Games with Partial Information
The large-population system consists of considerable small agents whose
individual behavior and mass effect are interrelated via their state-average.
The mean-field game provides an efficient way to get the decentralized
strategies of large-population system when studying its dynamic optimizations.
Unlike other large-population literature, this current paper possesses the
following distinctive features. First, our setting includes the partial
information structure of large-population system which is practical from real
application standpoint. Specially, two cases of partial information structure
are considered here: the partial filtration case (see Section 2, 3) where the
available information to agents is the filtration generated by an observable
component of underlying Brownian motion; the noisy observation case (Section 4)
where the individual agent can access an additive white-noise observation on
its own state. Also, it is new in filtering modeling that our sensor function
may depend on the state-average. Second, in both cases, the limiting
state-averages become random and the filtering equations to individual state
should be formalized to get the decentralized strategies. Moreover, it is also
new that the limit average of state filters should be analyzed here. This makes
our analysis very different to the full information arguments of
large-population system. Third, the consistency conditions are equivalent to
the wellposedness of some Riccati equations, and do not involve the fixed-point
analysis as in other mean-field games. The -Nash equilibrium
properties are also presented.Comment: 19 page
Research on Technology Spillover Effects on Agricultural Productivity in China
Technology contributes to the modern economic development directly and indirectly, and it changes the operating system and working method during the economic transformation. Along with institutional reform and modern economic structural transformation of China, agriculture still plays an ineradicable role in the development of Chinese economy, and it is the cornerstone for countries with large population. In this paper, the main purpose is to study how technology spillover effects work on agricultural productivity. In order to solve this question, I focus on two aspects, the one is from R&D perspective, and the other is the improvement of actual agricultural production techniques. This paper investigates the question by empirical analysis, and I collect panel data from three statistical yearbooks of China. The datasets consist of annul data from 1992 to 2013 and cross-sectional data of 30 regions of China, the statistical package Eviews will be employed to generate empirical results. There are four models in my paper, the first three models are set to study the puzzle directly based on the hypotheses, and the last one is a modified model after some necessary tests. The results show that technology has different spillover effects on agricultural productivity in different aspects, even though some variables are insignificant in explaining the model
Mean Field Linear-Quadratic-Gaussian (LQG) Games of Forward-Backward Stochastic Differential Equations
This paper studies a new class of dynamic optimization problems of
large-population (LP) system which consists of a large number of negligible and
coupled agents. The most significant feature in our setup is the dynamics of
individual agents follow the forward-backward stochastic differential equations
(FBSDEs) in which the forward and backward states are coupled at the terminal
time. This current paper is hence different to most existing large-population
literature where the individual states are typically modeled by the SDEs
including the forward state only. The associated mean-field
linear-quadratic-Gaussian (LQG) game, in its forward-backward sense, is also
formulated to seek the decentralized strategies. Unlike the forward case, the
consistency conditions of our forward-backward mean-field games involve six
Riccati and force rate equations. Moreover, their initial and terminal
conditions are mixed thus some special decoupling technique is applied here. We
also verify the -Nash equilibrium property of the derived
decentralized strategies. To this end, some estimates to backward stochastic
system are employed. In addition, due to the adaptiveness requirement to
forward-backward system, our arguments here are not parallel to those in its
forward case.Comment: 21 page
Reduce API Debugging Overhead via Knowledge Prepositioning
OpenAPI indicates a behavior where producers offer Application Programming
Interfaces (APIs) to help end-users access their data, resources, and services.
Generally, API has many parameters that need to be entered. However, it is
challenging for users to understand and document these parameters correctly.
This paper develops an API workbench to help users learn and debug APIs. Based
on this workbench, much exploratory work has been proposed to reduce the
overhead of learning and debugging APIs. We explore the knowledge, such as
parameter characteristics (e.g., enumerability) and constraints (e.g.,
maximum/minimum value), from the massive API call logs to narrow the range of
parameter values. Then, we propose a fine-grained approach to enrich the API
documentation by extracting dependency knowledge between APIs. Finally, we
present a learning-based prediction method to predict API execution results
before the API is called, significantly reducing user debugging cycles. The
experiments evaluated on the online system show that this work's approach
substantially improves the user experience of debugging OpenAPIs.Comment: arXiv admin note: text overlap with arXiv:1509.01626,
arXiv:1502.01710 by other author
Frustratingly Easy Model Generalization by Dummy Risk Minimization
Empirical risk minimization (ERM) is a fundamental machine learning paradigm.
However, its generalization ability is limited in various tasks. In this paper,
we devise Dummy Risk Minimization (DuRM), a frustratingly easy and general
technique to improve the generalization of ERM. DuRM is extremely simple to
implement: just enlarging the dimension of the output logits and then
optimizing using standard gradient descent. Moreover, we validate the efficacy
of DuRM on both theoretical and empirical analysis. Theoretically, we show that
DuRM derives greater variance of the gradient, which facilitates model
generalization by observing better flat local minima. Empirically, we conduct
evaluations of DuRM across different datasets, modalities, and network
architectures on diverse tasks, including conventional classification, semantic
segmentation, out-of-distribution generalization, adverserial training, and
long-tailed recognition. Results demonstrate that DuRM could consistently
improve the performance under all tasks with an almost free lunch manner.
Furthermore, we show that DuRM is compatible with existing generalization
techniques and we discuss possible limitations. We hope that DuRM could trigger
new interest in the fundamental research on risk minimization.Comment: Technical report; 22 page
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