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.
Bayesian model improves cure fraction estimation in survival analysis.
Proposes a deep neural network for predicting survival times with cure fractions.
We introduce a mixture model for censored durations (C-mix), and develop maximum likelihood inference for the joint estimation of the time distributions and latent regression parameters of the model. We consider a high-dimensional setting, with datasets containing a large number of biomedical covariates. We therefore p…
A Markov-chain model is developed for the purpose estimation of the cure rate of non-performing loans. The technique is performed collectively, on portfolios and it can be applicable in the process of calculation of credit impairment. It is efficient in terms of data manipulation costs which makes it accessible even to…
New model identifies cell-specific genes for cancer prognosis.
New method optimizes processes under constraints using bivariate Gaussian models.
The calibration of a measurement device is crucial for every scientific experiment, where a signal has to be inferred from data. We present CURE, the calibration uncertainty renormalized estimator, to reconstruct a signal and simultaneously the instrument's calibration from the same data without knowing the exact calib…
This paper argues, first, that a major problem in the planning of large infrastructure projects is the high level of misinformation about costs and benefits that decision makers face in deciding whether to build, and the high risks such misinformation generates. Second, it explores the causes of misinformation and risk…
CURE extracts relations without supervision by clustering similar entity pairs.
Study identifies risk factors for subsequent suicide attempts in youth.
To cure the lack of predictive power of general relativity Geroch proposed to complete the theory with an additional postulate that only "hole-free" spacetimes are permitted. I argue that this postulate is too strong -- it prohibits even the Minkowski space.
New gauge condition fixes metric divergence in hyperbolic monopole spaces.
Dan Lovallo and Daniel Kahneman must be commended for their clear identification of causes and cures to the planning fallacy in "Delusions of Success: How Optimism Undermines Executives' Decisions" (HBR July 2003). Their look at overoptimism, anchoring, competitor neglect, and the outside view in forecasting is highly …
We reconsider the multivariate Kyle model in a risk-neutral setting with a single, perfectly informed rational insider and a rational competitive market maker, setting the price of n correlated securities. We prove the unicity of a symmetric, positive definite solution for the impact matrix and provide insights on its …
Rare diseases affect a relatively small number of people, which limits investment in research for treatments and cures. Developing an efficient method for rare disease detection is a crucial first step towards subsequent clinical research. In this paper, we present a semi-supervised learning framework for rare disease …
Although shill bidding is a common auction fraud, it is however very tough to detect. Due to the unavailability and lack of training data, in this study, we build a high-quality labeled shill bidding dataset based on recently collected auctions from eBay. Labeling shill biding instances with multidimensional features i…
Divide-and-conquer method speeds sparse factorization for large matrices.
This essay suggests that a proper assessment of the presently unfolding financial crisis, and its cure, requires going back at least to the late 1990s, accounting for the cumulative effect of the ITC, real-estate and financial derivative bubbles. We focus on the deep loss of trust, not only in Wall Street, but more imp…
Enhances mixture models with classifier-defined weights.
Optimal mixtures of generative models outperform individual models on image datasets.
New bounds on sample size for identifying mixture models with grouped samples.
We consider unsupervised estimation of mixtures of discrete graphical models, where the class variable corresponding to the mixture components is hidden and each mixture component over the observed variables can have a potentially different Markov graph structure and parameters. We propose a novel approach for estimati…
Spatially constrained Gaussian mixture models reduce covariance complexity.
When estimating finite mixture models, it is common to make assumptions on the mixture components, such as parametric assumptions. In this work, we make no distributional assumptions on the mixture components and instead assume that observations from the mixture model are grouped, such that observations in the same gro…
Improved VB algorithm for NIG mixtures outperforms Gaussian mixtures for non-Gaussian data.
The two most extended density-based approaches to clustering are surely mixture model clustering and modal clustering. In the mixture model approach, the density is represented as a mixture and clusters are associated to the different mixture components. In modal clustering, clusters are understood as regions of high d…
The parsimonious Gaussian mixture models, which exploit an eigenvalue decomposition of the group covariance matrices of the Gaussian mixture, have shown their success in particular in cluster analysis. Their estimation is in general performed by maximum likelihood estimation and has also been considered from a parametr…
Paper establishes universal lower bounds and optimal rates for clustering sub-exponential mixture models.
A new method for fast Bayesian mixture model estimation.
The paper uses Gaussian mixture models for Bayesian networks and proposes an optimization algorithm.
A new method detects outliers using ensembles of Dirichlet process mixtures.
Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers of latent variables, wh…
Paper uses Gaussian mixture models and Wasserstein distance for schema matching.
Bayesian approach learns nonparametric mixture components from heterogeneous data.
SMT trains generative models by estimating mixture scores, outperforming existing methods.
New method estimates mixture model components efficiently.
New method for summarizing Bayesian mixture models using sliced Wasserstein distances.
Finite mixture models are statistical models which appear in many problems in statistics and machine learning. In such models it is assumed that data are drawn from random probability measures, called mixture components, which are themselves drawn from a probability measure P over probability measures. When estimating …
Algorithm estimates nonparametric mixtures from grouped data.
Mixture modeling is a general technique for making any simple model more expressive through weighted combination. This generality and simplicity in part explains the success of the Expectation Maximization (EM) algorithm, in which updates are easy to derive for a wide class of mixture models. However, the likelihood of…
The paper develops methods to create reliable prediction sets for complex mixture models in high-dimensional data.
A new method uses Mean Field Games to optimize mixture models of Bernoulli and categorical distributions.
A new model avoids the PH assumption for right-censored survival data.
The paper tackles learning mixtures of two multinomial logits, showing identifiability and presenting an algorithm.
Study uniform rates for estimating Gaussian mixtures without separation assumption.
The study bounds the stability of Gaussian mixtures under small perturbations.
Mixture models are a fundamental tool in applied statistics and machine learning for treating data taken from multiple subpopulations. The current practice for estimating the parameters of such models relies on local search heuristics (e.g., the EM algorithm) which are prone to failure, and existing consistent methods …
Discriminative classifier for compositional data using hierarchical mixture of Generalized Dirichlet models.