2,614 research outputs found
Unsupervised Domain Adaptation using Graph Transduction Games
Unsupervised domain adaptation (UDA) amounts to assigning class labels to the
unlabeled instances of a dataset from a target domain, using labeled instances
of a dataset from a related source domain. In this paper, we propose to cast
this problem in a game-theoretic setting as a non-cooperative game and
introduce a fully automatized iterative algorithm for UDA based on graph
transduction games (GTG). The main advantages of this approach are its
principled foundation, guaranteed termination of the iterative algorithms to a
Nash equilibrium (which corresponds to a consistent labeling condition) and
soft labels quantifying the uncertainty of the label assignment process. We
also investigate the beneficial effect of using pseudo-labels from linear
classifiers to initialize the iterative process. The performance of the
resulting methods is assessed on publicly available object recognition
benchmark datasets involving both shallow and deep features. Results of
experiments demonstrate the suitability of the proposed game-theoretic approach
for solving UDA tasks.Comment: Oral IJCNN 201
Ancient Coin Classification Using Graph Transduction Games
Recognizing the type of an ancient coin requires theoretical expertise and
years of experience in the field of numismatics. Our goal in this work is
automatizing this time consuming and demanding task by a visual classification
framework. Specifically, we propose to model ancient coin image classification
using Graph Transduction Games (GTG). GTG casts the classification problem as a
non-cooperative game where the players (the coin images) decide their
strategies (class labels) according to the choices made by the others, which
results with a global consensus at the final labeling. Experiments are
conducted on the only publicly available dataset which is composed of 180
images of 60 types of Roman coins. We demonstrate that our approach outperforms
the literature work on the same dataset with the classification accuracy of
73.6% and 87.3% when there are one and two images per class in the training
set, respectively
Allo-network drugs: Extension of the allosteric drug concept to protein-protein interaction and signaling networks
Allosteric drugs are usually more specific and have fewer side effects than orthosteric drugs targeting the same
protein. Here, we overview the current knowledge on allosteric signal transmission from the network point of view, and show that most intra-protein conformational changes may be dynamically transmitted across protein-protein interaction and signaling networks of the cell. Allo-network drugs influence the pharmacological target protein indirectly using specific inter-protein network pathways. We show that allo-network drugs may have a higher efficiency to change the networks of human cells than those of other organisms, and can be designed to have specific effects on cells in a diseased state. Finally, we summarize possible methods to identify allo-network drug targets and sites, which may develop to a promising new area of systems-based drug design
Disordered proteins and network disorder in network descriptions of protein structure, dynamics and function. Hypotheses and a comprehensive review
During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here we review the links between disordered proteins and the associated networks, and describe the consequences of local, mesoscopic and global network disorder on changes in protein structure and dynamics. We introduce a new classification of protein networks into ‘cumulus-type’, i.e., those similar to puffy (white) clouds, and ‘stratus-type’, i.e., those similar to flat, dense (dark) low-lying clouds, and relate these network types to protein disorder dynamics and to differences in energy transmission processes. In the first class, there is limited overlap between the modules, which implies higher rigidity of the individual units; there the conformational changes can be described by an ‘energy transfer’ mechanism. In the second class, the topology presents a compact structure with significant overlap between the modules; there the conformational changes can be described by ‘multi-trajectories’; that is, multiple highly populated pathways. We further propose that disordered protein regions evolved to help other protein segments reach ‘rarely visited’ but functionally-related states. We also show the role of disorder in ‘spatial games’ of amino acids; highlight the effects of intrinsically disordered proteins (IDPs) on cellular networks and list some possible studies linking protein disorder and protein structure networks
Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data
We consider learning, from strictly behavioral data, the structure and
parameters of linear influence games (LIGs), a class of parametric graphical
games introduced by Irfan and Ortiz (2014). LIGs facilitate causal strategic
inference (CSI): Making inferences from causal interventions on stable behavior
in strategic settings. Applications include the identification of the most
influential individuals in large (social) networks. Such tasks can also support
policy-making analysis. Motivated by the computational work on LIGs, we cast
the learning problem as maximum-likelihood estimation (MLE) of a generative
model defined by pure-strategy Nash equilibria (PSNE). Our simple formulation
uncovers the fundamental interplay between goodness-of-fit and model
complexity: good models capture equilibrium behavior within the data while
controlling the true number of equilibria, including those unobserved. We
provide a generalization bound establishing the sample complexity for MLE in
our framework. We propose several algorithms including convex loss minimization
(CLM) and sigmoidal approximations. We prove that the number of exact PSNE in
LIGs is small, with high probability; thus, CLM is sound. We illustrate our
approach on synthetic data and real-world U.S. congressional voting records. We
briefly discuss our learning framework's generality and potential applicability
to general graphical games.Comment: Journal of Machine Learning Research. (accepted, pending
publication.) Last conference version: submitted March 30, 2012 to UAI 2012.
First conference version: entitled, Learning Influence Games, initially
submitted on June 1, 2010 to NIPS 201
Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review
The Human Activity Recognition (HAR) tasks automatically identify human
activities using the sensor data, which has numerous applications in
healthcare, sports, security, and human-computer interaction. Despite
significant advances in HAR, critical challenges still exist. Game theory has
emerged as a promising solution to address these challenges in machine learning
problems including HAR. However, there is a lack of research work on applying
game theory solutions to the HAR problems. This review paper explores the
potential of game theory as a solution for HAR tasks, and bridges the gap
between game theory and HAR research work by suggesting novel game-theoretic
approaches for HAR problems. The contributions of this work include exploring
how game theory can improve the accuracy and robustness of HAR models,
investigating how game-theoretic concepts can optimize recognition algorithms,
and discussing the game-theoretic approaches against the existing HAR methods.
The objective is to provide insights into the potential of game theory as a
solution for sensor-based HAR, and contribute to develop a more accurate and
efficient recognition system in the future research directions
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