4,091 research outputs found
Experiments on the Application of IOHMMs to Model Financial Returns Series
Input/Output Hidden Markov Models (IOHMMs) are conditional hidden Markov models in which the emission (and possibly the transition) probabilities can be conditioned on an input sequence. For example, these conditional distributions can be linear, logistic, or non-linear (using for example multi-layer neural networks). We compare the generalization performance of several models which are special cases of Input/Output Hidden Markov Models on financial time-series prediction tasks: an unconditional Gaussian, a conditional linear Gaussian, a mixture of Gaussians, a mixture of conditional linear Gaussians, a hidden Markov model, and various IOHMMs. The experiments compare these models on predicting the conditional density of returns of market and sector indices. Note that the unconditional Gaussian estimates the first moment with the historical average. The results show that, although for the first moment the historical average gives the best results, for the higher moments, the IOHMMs yielded significantly better performance, as estimated by the out-of-sample likelihood. "Input/Output Hidden Markov Models" (IOHMMs) sont des modèles de Markov cachés pour lesquels les probabilités d'émission (et possiblement de transition) peuvent dépendre d'une séquence d'entrée. Par exemple, ces distributions conditionnelles peuvent être linéaires, logistique, ou non-linéaire (utilisant, par exemple, une réseau de neurones multi-couches). Nous comparons les performances de généralisation de plusieurs modèles qui sont des cas particuliers de IOHMMs pour des problèmes de prédictions de séries financières : une gaussienne inconditionnelle, une gaussienne linéaire conditionnelle, une mixture de gaussienne, une mixture de gaussiennes linéaires conditionnelles, un modèle de Markov caché, et divers IOHMMs. Les expériences comparent ces modèles sur leur prédictions de la densité conditionnelle des rendements des indices sectoriels et du marché. Notons qu'une gaussienne inconditionnelle estime le premier moment avec une moyenne historique. Les résultats montrent que, même si la moyenne historique donne les meilleurs résultats pour le premier moment, pour les moments d'ordres supérieurs les IOHMMs performent significativement mieux, comme estimé par la vraisemblance hors-échantillon.Input/Output Hidden Markov Model (IOHMM), financial series, volatility, Modèles de Markov cachés, IOHMM, séries financières, volatilité
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
Latent Multi-task Architecture Learning
Multi-task learning (MTL) allows deep neural networks to learn from related
tasks by sharing parameters with other networks. In practice, however, MTL
involves searching an enormous space of possible parameter sharing
architectures to find (a) the layers or subspaces that benefit from sharing,
(b) the appropriate amount of sharing, and (c) the appropriate relative weights
of the different task losses. Recent work has addressed each of the above
problems in isolation. In this work we present an approach that learns a latent
multi-task architecture that jointly addresses (a)--(c). We present experiments
on synthetic data and data from OntoNotes 5.0, including four different tasks
and seven different domains. Our extension consistently outperforms previous
approaches to learning latent architectures for multi-task problems and
achieves up to 15% average error reductions over common approaches to MTL.Comment: To appear in Proceedings of AAAI 201
Generative Image Modeling Using Spatial LSTMs
Modeling the distribution of natural images is challenging, partly because of
strong statistical dependencies which can extend over hundreds of pixels.
Recurrent neural networks have been successful in capturing long-range
dependencies in a number of problems but only recently have found their way
into generative image models. We here introduce a recurrent image model based
on multi-dimensional long short-term memory units which are particularly suited
for image modeling due to their spatial structure. Our model scales to images
of arbitrary size and its likelihood is computationally tractable. We find that
it outperforms the state of the art in quantitative comparisons on several
image datasets and produces promising results when used for texture synthesis
and inpainting
Scalable Planning and Learning for Multiagent POMDPs: Extended Version
Online, sample-based planning algorithms for POMDPs have shown great promise
in scaling to problems with large state spaces, but they become intractable for
large action and observation spaces. This is particularly problematic in
multiagent POMDPs where the action and observation space grows exponentially
with the number of agents. To combat this intractability, we propose a novel
scalable approach based on sample-based planning and factored value functions
that exploits structure present in many multiagent settings. This approach
applies not only in the planning case, but also in the Bayesian reinforcement
learning setting. Experimental results show that we are able to provide high
quality solutions to large multiagent planning and learning problems
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