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.
Automatically learns summary features from time series data for likelihood-free inference.
Approximate Bayesian computation is an established and popular method for likelihood-free inference with applications in many disciplines. The effectiveness of the method depends critically on the availability of well performing summary statistics. Summary statistic selection relies heavily on domain knowledge and care…
Plug-in robust NPE method adapts summaries independently of pretrained NPE.
LIDS assesses LLM summaries with interpretable key words.
Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in th…
Proposes -table for statistical SHAP explanations in regression models.
Set-Sequence model learns cross-sectional dynamics directly from time series data.
FedLog reduces communication in federated learning by sharing data summaries.
Performing exact posterior inference in complex generative models is often difficult or impossible due to an expensive to evaluate or intractable likelihood function. Approximate Bayesian computation (ABC) is an inference framework that constructs an approximation to the true likelihood based on the similarity between …
The study analyzes transfer learning in infinite-width neural networks, improving generalization on target tasks.
We propose a modification that corrects for split-improvement variable importance measures in Random Forests and other tree-based methods. These methods have been shown to be biased towards increasing the importance of features with more potential splits. We show that by appropriately incorporating split-improvement as…
Study analyzes seasonal hydroclimatic features across climates and continents.
Feature extraction has gained increasing attention in the field of machine learning, as in order to detect patterns, extract information, or predict future observations from big data, the urge of informative features is crucial. The process of extracting features is highly linked to dimensionality reduction as it impli…
New method for analyzing compositional data, addressing biases in summary statistics.
New mechanism for pure differential privacy on functional summaries using Laplace-like process.
Approximate Bayesian Computation (ABC) methods are used to approximate posterior distributions in models with unknown or computationally intractable likelihoods. Both the accuracy and computational efficiency of ABC depend on the choice of summary statistic, but outside of special cases where the optimal summary statis…
A new method compares persistent cycles in topological data.
When learning a hidden Markov model (HMM), sequen- tial observations can often be complemented by real-valued summary response variables generated from the path of hid- den states. Such settings arise in numerous domains, includ- ing many applications in biology, like motif discovery and genome annotation. In this pape…
In the last two decades, automatic extractive text summarization on lectures has demonstrated to be a useful tool for collecting key phrases and sentences that best represent the content. However, many current approaches utilize dated approaches, producing sub-par outputs or requiring several hours of manual tuning to …
Video summarisation can be posed as the task of extracting important parts of a video in order to create an informative summary of what occurred in the video. In this paper we introduce SummaryNet as a supervised learning framework for automated video summarisation. SummaryNet employs a two-stream convolutional network…
Unified approach for selecting summary statistics in ABC.
Enhances weak lensing inference with neural summaries.
The physics of classical particles in a Lorentz-breaking spacetime has numerous features resembling the properties of Finsler geometry. In particular, the Lagrange function plays a role similar to that of a Finsler structure function. A summary is presented of recent results, including new calculable Finsler structures…
New TSER algorithms outperform existing methods in time series extrinsic regression.
We introduce a framework using Generative Adversarial Networks (GANs) for likelihood--free inference (LFI) and Approximate Bayesian Computation (ABC) where we replace the black-box simulator model with an approximator network and generate a rich set of summary features in a data driven fashion. On benchmark data sets, …
This paper introduces depth functions for ranking data, improving statistical summaries.
Improves inference from sparse data with hybrid summary statistics.
Text clustering method replaces centroids with summaries for interpretability and scalability.
Infinite mixture models are commonly used for clustering. One can sample from the posterior of mixture assignments by Monte Carlo methods or find its maximum a posteriori solution by optimization. However, in some problems the posterior is diffuse and it is hard to interpret the sampled partitionings. In this paper, we…
Bayesian neural networks improve with summary information and Dirichlet process.
Few summaries enable automatic summarization of product reviews.
SafeAccess is an integrated system designed to provide easier and safer access to a smart home for people with or without disabilities. The system is designed to enhance safety and promote the independence of people with disability (i.e., visually impaired). The key functionality of the system includes the detection an…
Improved likelihood-free inference by localizing and refining low-dimensional approximations.
Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modeling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight. In this paper, we propose a novel method of inducing spatial aggregations as a component of the machine lea…
New summary measures reveal geometric structure in weighted measures on manifolds.
Detecting PE malware files is now commonly approached using statistical and machine learning models. While these models commonly use features extracted from the structure of PE files, we propose that icons from these files can also help better predict malware. We propose an innovative machine learning approach to extra…
A new method improves likelihood-free Bayesian inference by transforming summary statistics and using efficient Variational Bayes.
Opinion summarization is the task of automatically creating summaries that reflect subjective information expressed in multiple documents, such as product reviews. While the majority of previous work has focused on the extractive setting, i.e., selecting fragments from input reviews to produce a summary, we let the mod…
We consider a classifier whose test set is exposed to various perturbations that are not present in the training set. These test samples still contain enough features to map them to the same class as their unperturbed counterpart. Current architectures exhibit rapid degradation of accuracy when trained on standard data…
Paper uses learned summary statistics for Bayesian inference with difficult likelihood functions.
Flexible multi-task learning framework using summary statistics.
Develops methods to learn centre groupings from summary statistics in multi-centre studies.
TopoFisher learns topological summaries by maximizing Fisher information, improving parameter efficiency and inference quality.
Interpreting predictions from tree ensemble methods such as gradient boosting machines and random forests is important, yet feature attribution for trees is often heuristic and not individualized for each prediction. Here we show that popular feature attribution methods are inconsistent, meaning they can lower a featur…
The paper proposes using Autoencoders to learn summary statistics for Bayesian inference.
Paper introduces methods to automatically generate SOAP notes from patient-physician conversations.
Improves AUC for disadvantaged groups by adding features.
Estimates non-parametric logistic model using case-control data and external summary info.