arXiv research

Quantum machine learning uses quantum cross entropy to minimize loss, but measurement loss affects this process.

Paper proposes an optimistic likelihood approximation for nonparametric likelihoods.

DAPS++ improves diffusion-based image restoration by decoupling prior and likelihood.

DAPS++ improves diffusion-based image restoration by decoupling prior and likelihood.

A new metric for detecting out-of-distribution samples using neural rendering models.

We define a generalized likelihood function based on uncertainty measures and show that maximizing such a likelihood function for different measures induces different types of classifiers. In the probabilistic framework, we obtain classifiers that optimize the cross-entropy function. In the possibilistic framework, we …

Measures neural network complexity via effective degrees of freedom.

A novel kernel-based test detects equality versus singularity of two probability measures.

We extend Bayes' theorem for upper probabilities considering likelihood uncertainty.

Method recovers complex-valued signals from speckle-noised measurements.

In this paper, a Bayesian inference technique based on Taylor series approximation of the logarithm of the likelihood function is presented. The proposed approximation is devised for the case, where the prior distribution belongs to the exponential family of distributions. The logarithm of the likelihood function is li…

In this note, we study the relationship between the variational gap and the variance of the (log) likelihood ratio. We show that the gap can be upper bounded by some form of dispersion measure of the likelihood ratio, which suggests the bias of variational inference can be reduced by making the distribution of the like…

New method for conditional sampling using M-GANs, likely-free inference.

FF algorithm uses goodness as a measure of input quality, derived from likelihood-ratio tests.

DALTON improves ODE parameter estimation by learning from noisy data.

Study of maximum likelihood under biased constraints reveals novel degeneracies and anomalous statistical behavior.

In likelihood-free settings where likelihood evaluations are intractable, approximate Bayesian computation (ABC) addresses the formidable inference task to discover plausible parameters of simulation programs that explain the observations. However, they demand large quantities of simulation calls. Critically, hyperpara…

Gaussian process regression helps approximate Bayesian inverse problems efficiently.

C-DPS improves diffusion posterior sampling for inverse problems without projection or likelihood approximation.

For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator. In this work we compare various measures of distance between such likelihoods in combination with different methods for estimation and representation. In pa…

RECLAIM discovers causal graphs in cyclic, noisy systems.

The maximum likelihood approach is adapted to the problem of estimation of drift and diffusion functions of stochastic processes from measured time series. We reconcile a previously devised iterative procedure [Kleinhans et al., Physics Letters A (346), 2005] and put the application of the method on a firm theoretical …

In a series of recent papers Barndorff-Nielsen and Shephard introduce an attractive class of continuous time stochastic volatility models for financial assets where the volatility processes are functions of positive Ornstein-Uhlenbeck(OU) processes. This models are known to be substantially more flexible than Gaussian …

When observations are organized into groups where commonalties exist amongst them, the dependent random measures can be an ideal choice for modeling. One of the propositions of the dependent random measures is that the atoms of the posterior distribution are shared amongst groups, and hence groups can borrow informatio…

New CTRL algorithm adapts to varying problem difficulty.

The Restricted Boltzmann Machines (RBM) can be used either as classifiers or as generative models. The quality of the generative RBM is measured through the average log-likelihood on test data. Due to the high computational complexity of evaluating the partition function, exact calculation of test log-likelihood is ver…

One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies solve this issue, including the traditional histogram approach used in most par…

Paper presents a robust Kalman filter for state estimation.

Paper examines stability of Bayesian posterior measures using integral probability metrics.

In many fields of science, generalized likelihood ratio tests are established tools for statistical inference. At the same time, it has become increasingly common that a simulator (or generative model) is used to describe complex processes that tie parameters $θ$ of an underlying theory and measurement apparatus to hig…

Study nonparametric density estimation via measure transport, achieving optimal rates.

The coefficient of determination, known as $R^2$, is commonly used as a goodness-of-fit criterion for fitting linear models. $R^2$ is somewhat controversial when fitting nonlinear models, although it may be generalised on a case-by-case basis to deal with specific models such as the logistic model. Assume we are fittin…

A new ensemble learning method called Residual Likelihood Forests improves performance and reduces model size.

This paper develops embeddings that preserve likelihood-based statistical inference.

Posterior inference with an intractable likelihood is becoming an increasingly common task in scientific domains which rely on sophisticated computer simulations. Typically, these forward models do not admit tractable densities forcing practitioners to make use of approximations. This work introduces a novel approach t…

We study asymptotic properties of maximum likelihood estimators of drift parameters for a jump-type Heston model based on continuous time observations, where the jump process can be any purely non-Gaussian Lévy process of not necessarily bounded variation with a Lévy measure concentrated on $(-1,\infty)$. We prove stro…

Bayesian calibration for BCP self-assembly models using image data and measure transport.

New approach to robust Gaussian process regression with bias model.

Machine learning models encounter Out-of-Distribution (OoD) errors when the data seen at test time are generated from a different stochastic generator than the one used to generate the training data. One proposal to scale OoD detection to high-dimensional data is to learn a tractable likelihood approximation of the tra…

PUMA interprets metabolomics data to predict pathway activity and assign chemical identities.

Optimal algorithm identifies best arm for risk measures in heavy-tailed distributions.

Paper proves method for calculating NML code length works for continuous models.

Reconstructing the position of an interaction for any dual-phase time projection chamber (TPC) with the best precision is key to directly detecting Dark Matter. Using the likelihood-free framework, a new algorithm to reconstruct the 2-D (x; y) position and the size of the charge signal (e) of an interaction is presente…

A new model forecasts financial risks using multiple realized measures.

Two data-dependent information metrics are developed to quantify the information of the prior and likelihood functions within a parametric Bayesian model, one of which is closely related to the reference priors from Berger, Bernardo, and Sun, and information measure introduced by Lindley. A combination of theoretical, …

This research improves neural likelihood approximation for Bayesian inverse problems.

Accelerates MMLE using SVGD with Nesterov acceleration.

In this paper, we study the effects of different prior and likelihood choices for Bayesian matrix factorisation, focusing on small datasets. These choices can greatly influence the predictive performance of the methods. We identify four groups of approaches: Gaussian-likelihood with real-valued priors, nonnegative prio…