3,840 research outputs found
Accelerating delayed-acceptance Markov chain Monte Carlo algorithms
Delayed-acceptance Markov chain Monte Carlo (DA-MCMC) samples from a
probability distribution via a two-stages version of the Metropolis-Hastings
algorithm, by combining the target distribution with a "surrogate" (i.e. an
approximate and computationally cheaper version) of said distribution. DA-MCMC
accelerates MCMC sampling in complex applications, while still targeting the
exact distribution. We design a computationally faster, albeit approximate,
DA-MCMC algorithm. We consider parameter inference in a Bayesian setting where
a surrogate likelihood function is introduced in the delayed-acceptance scheme.
When the evaluation of the likelihood function is computationally intensive,
our scheme produces a 2-4 times speed-up, compared to standard DA-MCMC.
However, the acceleration is highly problem dependent. Inference results for
the standard delayed-acceptance algorithm and our approximated version are
similar, indicating that our algorithm can return reliable Bayesian inference.
As a computationally intensive case study, we introduce a novel stochastic
differential equation model for protein folding data.Comment: 40 pages, 21 figures, 10 table
Better Optimism By Bayes: Adaptive Planning with Rich Models
The computational costs of inference and planning have confined Bayesian
model-based reinforcement learning to one of two dismal fates: powerful
Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian
non-parametric models but using simple, myopic planning strategies such as
Thompson sampling. We ask whether it is feasible and truly beneficial to
combine rich probabilistic models with a closer approximation to fully Bayesian
planning. First, we use a collection of counterexamples to show formal problems
with the over-optimism inherent in Thompson sampling. Then we leverage
state-of-the-art techniques in efficient Bayes-adaptive planning and
non-parametric Bayesian methods to perform qualitatively better than both
existing conventional algorithms and Thompson sampling on two contextual
bandit-like problems.Comment: 11 pages, 11 figure
Quantifying Timing Leaks and Cost Optimisation
We develop a new notion of security against timing attacks where the attacker
is able to simultaneously observe the execution time of a program and the
probability of the values of low variables. We then show how to measure the
security of a program with respect to this notion via a computable estimate of
the timing leakage and use this estimate for cost optimisation.Comment: 16 pages, 2 figures, 4 tables. A shorter version is included in the
proceedings of ICICS'08 - 10th International Conference on Information and
Communications Security, 20-22 October, 2008 Birmingham, U
The age of information in gossip networks
We introduce models of gossip based communication networks in which each node
is simultaneously a sensor, a relay and a user of information. We model the
status of ages of information between nodes as a discrete time Markov chain. In
this setting a gossip transmission policy is a decision made at each node
regarding what type of information to relay at any given time (if any). When
transmission policies are based on random decisions, we are able to analyze the
age of information in certain illustrative structured examples either by means
of an explicit analysis, an algorithm or asymptotic approximations. Our key
contribution is presenting this class of models.Comment: 15 pages, 8 figure
Improved Financial Forecasting via Quantum Machine Learning
Quantum algorithms have the potential to enhance machine learning across a
variety of domains and applications. In this work, we show how quantum machine
learning can be used to improve financial forecasting. First, we use classical
and quantum Determinantal Point Processes to enhance Random Forest models for
churn prediction, improving precision by almost 6%. Second, we design quantum
neural network architectures with orthogonal and compound layers for credit
risk assessment, which match classical performance with significantly fewer
parameters. Our results demonstrate that leveraging quantum ideas can
effectively enhance the performance of machine learning, both today as
quantum-inspired classical ML solutions, and even more in the future, with the
advent of better quantum hardware
IST Austria Thesis
This dissertation concerns the automatic verification of probabilistic systems and programs with arrays by statistical and logical methods. Although statistical and logical methods are different in nature, we show that they can be successfully combined for system analysis. In the first part of the dissertation we present a new statistical algorithm for the verification of probabilistic systems with respect to unbounded properties, including linear temporal logic. Our algorithm often performs faster than the previous approaches, and at the same time requires less information about the system. In addition, our method can be generalized to unbounded quantitative properties such as mean-payoff bounds. In the second part, we introduce two techniques for comparing probabilistic systems. Probabilistic systems are typically compared using the notion of equivalence, which requires the systems to have the equal probability of all behaviors. However, this notion is often too strict, since probabilities are typically only empirically estimated, and any imprecision may break the relation between processes. On the one hand, we propose to replace the Boolean notion of equivalence by a quantitative distance of similarity. For this purpose, we introduce a statistical framework for estimating distances between Markov chains based on their simulation runs, and we investigate which distances can be approximated in our framework. On the other hand, we propose to compare systems with respect to a new qualitative logic, which expresses that behaviors occur with probability one or a positive probability. This qualitative analysis is robust with respect to modeling errors and applicable to many domains. In the last part, we present a new quantifier-free logic for integer arrays, which allows us to express counting. Counting properties are prevalent in array-manipulating programs, however they cannot be expressed in the quantified fragments of the theory of arrays. We present a decision procedure for our logic, and provide several complexity results
- …