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.
New method handles uncertainty in adversarial attacks using ensemble noise simulation.
We describe parallel Markov chain Monte Carlo methods that propagate a collective ensemble of paths, with local covariance information calculated from neighboring replicas. The use of collective dynamics eliminates multiplicative noise and stabilizes the dynamics thus providing a practical approach to difficult anisotr…
We present a novel approach to learn binary classifiers when only positive and unlabeled instances are available (PU learning). This problem is routinely cast as a supervised task with label noise in the negative set. We use an ensemble of SVM models trained on bootstrap resamples of the training data for increased rob…
We consider the problem of estimating the support of a vector based on observations contaminated by noise. A significant body of work has studied behavior of -relaxations when applied to measurement matrices drawn from standard dense ensembles (e.g., Gaussian, Bernoulli). In this paper,…
SIMPGEN improves SWOT SSH data interpretation by removing noise and preserving fine-scale features.
Bayesian tree ensemble model for estimating treatment effects in high-dimensional survival data.
New approach uses under-trained deep ensembles to learn from noisy labels.
Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predictions for that i…
This paper introduces a new perspective on multi-class ensemble classification that considers training an ensemble as a state estimation problem. The new perspective considers the final ensemble classifier model as a static state, which can be estimated using a Kalman filter that combines noisy estimates made by indivi…
Theory and method for reducing prediction variance in noisy feature-subsampled ridge ensembles.
Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta learning framework, ensemble techniques can easily be applied to many machine learning techniques. In this paper we propose a neural network extended with an ensemble loss function for text classification. …
VGE provides a practical approach to uncertainty estimation in ensemble models.
HRF enhances tree diversity in random forests to improve performance.
Simulator imperfection, often known as model error, is ubiquitous in practical data assimilation problems. Despite the enormous efforts dedicated to addressing this problem, properly handling simulator imperfection in data assimilation remains to be a challenging task. In this work, we propose an approach to dealing wi…
We formalize the notion of a pseudo-ensemble, a (possibly infinite) collection of child models spawned from a parent model by perturbing it according to some noise process. E.g., dropout (Hinton et. al, 2012) in a deep neural network trains a pseudo-ensemble of child subnetworks generated by randomly masking nodes in t…
ConfEviSurrogate improves surrogate model accuracy and uncertainty quantification.
Ensemble learning is a standard approach to building machine learning systems that capture complex phenomena in real-world data. An important aspect of these systems is the complete and valid quantification of model uncertainty. We introduce a Bayesian nonparametric ensemble (BNE) approach that augments an existing ens…
HD algorithm simulates dynamics on random matrix ensembles without generating full matrices.
This paper explores BDL hyperparameters for robust polynomial mapping with noise.
Improved Bayesian inference for neuronal ensemble inference reduces computational cost.
UNREAL selectively ensembles distinct models to improve active learning performance.
Paper tackles noisy and expensive likelihoods in complex models.
The log returns of financial time series are usually modeled by means of the stationary GARCH(1,1) stochastic process or its generalizations which can not properly describe the nonstationary deterministic components of the original series. We analyze the influence of deterministic trends on the GARCH(1,1) parameters us…
Proposes HetSNGP method for joint model and data uncertainty modeling.
PANDA augments data to regularize GLM estimation and inference.
Recent studies have revealed the vulnerability of deep neural networks: A small adversarial perturbation that is imperceptible to human can easily make a well-trained deep neural network misclassify. This makes it unsafe to apply neural networks in security-critical applications. In this paper, we propose a new defense…
Causal Mosaic distinguishes cause from effect using nonlinear ICA and ensemble methods.
It is common that a trained classification model is applied to the operating data that is deviated from the training data because of noise. This paper demonstrates that an ensemble classifier, Diversified Multiple Tree (DMT), is more robust in classifying noisy data than other widely used ensemble methods. DMT is teste…
In this article supervised learning problems are solved using soft rule ensembles. We first review the importance sampling learning ensembles (ISLE) approach that is useful for generating hard rules. The soft rules are then obtained with logistic regression from the corresponding hard rules. In order to deal with the p…
This research tackles image classification with noise, proposing committees of CNNs.
Proposes engression for extrapolation in distributional regression.
Proposes a meta-learning method for robust portfolio optimization.
Paper explains how tree ensembles improve predictions by smoothing and regulating smoothness.
The paper explores how prior functions and bootstrapping improve ensemble uncertainty estimation.
DSI improves tail-risk estimation in generative models by averaging checkpoints.
BODE enhances deep neural network predictions and uncertainty quantification in safety modeling.
A fast method estimates stability of ensemble feature selectors.
Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained with noisy labels for a long enough time. To overcome this problem, we present a simple and effective method self-ensemble label filtering (SELF) to progressively filter out the wrong labels during training. Our method improves the tas…
Removing or filtering outliers and mislabeled instances prior to training a learning algorithm has been shown to increase classification accuracy. A popular approach for handling outliers and mislabeled instances is to remove any instance that is misclassified by a learning algorithm. However, an examination of which l…
Bayes-optimal limits in PCA with structured noise are determined.
A simple approach to obtaining uncertainty-aware neural networks for regression is to do Bayesian linear regression (BLR) on the representation from the last hidden layer. Recent work [Riquelme et al., 2018, Azizzadenesheli et al., 2018] indicates that the method is promising, though it has been limited to homoscedasti…
Tree ensembles like RF and GBT can be seen as kernels, improving regression and classification performance.
Bayesian inference using stochastic neural networks ensembles.
Efficient exploration is a long-standing problem in sensorimotor learning. Major advances have been demonstrated in noise-free, non-stochastic domains such as video games and simulation. However, most of these formulations either get stuck in environments with stochastic dynamics or are too inefficient to be scalable t…
The study investigates kernel-target alignment in tree ensemble kernels.
Generative model emulates climate model for 100-year forecasts.
Recently, the binary expansion testing framework was introduced to test the independence of two continuous random variables by utilizing symmetry statistics that are complete sufficient statistics for dependence. We develop a new test based on an ensemble approach that uses the sum of squared symmetry statistics and di…
Paper improves financial trading models using GPU parallelism.