A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Proposes a new multi-scale architecture for generative flows to improve log-likelihood and sampling quality.
New neural methods tackle density estimation and likelihood-free inference.
We consider two connected aspects of maximum likelihood estimation of the parameter for high-dimensional discrete graphical models: the existence of the maximum likelihood estimate (mle) and its computation. When the data is sparse, there are many zeros in the contingency table and the maximum likelihood estimate of th…
Flowification enriches neural networks with an inverse pass and likelihood monitoring.
Proposes LFGP for likelihood-free Gaussian process regression.
Anomaly detection using dimensionality reduction has been an essential technique for monitoring multidimensional data. Although deep learning-based methods have been well studied for their remarkable detection performance, their interpretability is still a problem. In this paper, we propose a novel algorithm for estima…
Efficiently estimates marginal likelihood using SGAIS.
Paper proposes energy objective for training normalizing flows without determinants.
Paper proposes a neural network for non-parametric Hawkes process kernel estimation.
We model leverage as stochastic but independent of return shocks and of volatility and perform likelihood-based inference via the recently developed iterated filtering algorithm using S&P500 data, contributing new evidence to the still slim empirical support for random leverage variation.
This paper develops embeddings that preserve likelihood-based statistical inference.
Direct neural ratio estimator for likelihood-free inference.
Modern applications and progress in deep learning research have created renewed interest for generative models of text and of images. However, even today it is unclear what objective functions one should use to train and evaluate these models. In this paper we present two contributions. Firstly, we present a critique o…
Generalising well in supervised learning tasks relies on correctly extrapolating the training data to a large region of the input space. One way to achieve this is to constrain the predictions to be invariant to transformations on the input that are known to be irrelevant (e.g. translation). Commonly, this is done thro…
In this study, we develop a deterministic nonlinear filtering algorithm based on a high-dimensional version of Kitagawa (1987) to evaluate the likelihood function of models that allow for stochastic volatility and jumps whose arrival intensity is also stochastic. We show numerically that the deterministic filtering met…
In this article we use rate-distortion theory, a branch of information theory devoted to the problem of lossy compression, to shed light on an important problem in latent variable modeling of data: is there room to improve the model? One way to address this question is to find an upper bound on the probability (equival…
Nested sampling is a powerful technique for exploring high-likelihood regions, but its theoretical derivation is complex and involves approximations.
Dimension reduction and variable selection are performed routinely in case-control studies, but the literature on the theoretical aspects of the resulting estimates is scarce. We bring our contribution to this literature by studying estimators obtained via L1 penalized likelihood optimization. We show that the optimize…
The log-likelihood loss in heteroscedastic neural networks can lead to poor parameter estimates.
With an eye towards human-centered automation, we contribute to the development of a systematic means to infer features of human decision-making from behavioral data. Motivated by the common use of softmax selection in models of human decision-making, we study the maximum likelihood parameter estimation problem for sof…
New method estimates latent gene expression factors without overlap with known confounders.
Method recovers complex-valued signals from speckle-noised measurements.
Deep learning estimates time-varying Markov model parameters.
Training models to prefer certain responses can unintentionally shift probability to harmful ones.
We propose a new inferential framework for constructing confidence regions and testing hypotheses in statistical models specified by a system of high dimensional estimating equations. We construct an influence function by projecting the fitted estimating equations to a sparse direction obtained by solving a large-scale…
Recent work used importance sampling ideas for better variational bounds on likelihoods. We clarify the applicability of these ideas to pure probabilistic inference, by showing the resulting Importance Weighted Variational Inference (IWVI) technique is an instance of augmented variational inference, thus identifying th…
Study uses multi-kernel Hawkes models to analyze high-frequency price dynamics.
We present a practical way of introducing convolutional structure into Gaussian processes, making them more suited to high-dimensional inputs like images. The main contribution of our work is the construction of an inter-domain inducing point approximation that is well-tailored to the convolutional kernel. This allows …
Paper solves issues with negative weights in sPlot technique for machine learning.
Paper introduces an online method for estimating the difference between two probability distributions.
The outlying property detection problem is the problem of discovering the properties distinguishing a given object, known in advance to be an outlier in a database, from the other database objects. In this paper, we analyze the problem within a context where numerical attributes are taken into account, which represents…
We introduce a simple method for nearly simultaneous computation of all moments needed for quasi maximum likelihood estimation of parameters in discretely observed stochastic differential equations commonly seen in finance. The method proposed in this papers is not restricted to any particular dynamics of the different…
Determinantal point processes (DPPs) are point process models that naturally encode diversity between the points of a given realization, through a positive definite kernel . DPPs possess desirable properties, such as exact sampling or analyticity of the moments, but learning the parameters of kernel through like…
New methods improve anomaly detection in deep networks by leveraging hierarchical likelihoods and multi-scale features.
Scalable Gaussian process models trained with unbiased stochastic ELBO.
The paper addresses speckle noise in coherent imaging systems.
We propose a likelihood ratio based inferential framework for high dimensional semiparametric generalized linear models. This framework addresses a variety of challenging problems in high dimensional data analysis, including incomplete data, selection bias, and heterogeneous multitask learning. Our work has three main …
Smooth neural TPPs using B-splines for better efficiency and accuracy.
TMLE improves unbiased estimation in public health studies.
We suggest a general approach to quantification of different forms of aleatoric uncertainty in regression tasks performed by artificial neural networks. It is based on the simultaneous training of two neural networks with a joint loss function and a specific hyperparameter that allows for automatically detecting …
Determinantal point processes (DPPs) offer a powerful approach to modeling diversity in many applications where the goal is to select a diverse subset. We study the problem of learning the parameters (the kernel matrix) of a DPP from labeled training data. We make two contributions. First, we show how to reparameterize…
Proposes a method to improve classification robustness against label noise.
A benchmark for simulation-based inference methods.
CRS model improves ranking data modeling with theoretical guarantees.
MAXENT method outperforms ML in sparse data with specific prior correlations.
Decoding strategies often exclude human-like tokens, creating a detectable gap in generated text.
This paper provides a method for noise-calibrated inference from DP synthetic data.
Combines deep learning with constraints for better image generation.