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.
Active sampling algorithm improves accuracy of inferred scores from pairwise comparisons.
Test log-likelihood comparisons can be misleading.
MIRA scores assess conditional distribution accuracy using joint samples.
Self-consistency improves the accuracy of model comparison methods.
New method uses model comparison signals to improve LLM evaluation accuracy.
New proposed models are often compared to state-of-the-art using statistical significance testing. Literature is scarce for classifier comparison using metrics other than accuracy. We present a survey of statistical methods that can be used for classifier comparison using precision, accounting for inter-precision corre…
Rank aggregation systems collect ordinal preferences from individuals to produce a global ranking that represents the social preference. Rank-breaking is a common practice to reduce the computational complexity of learning the global ranking. The individual preferences are broken into pairwise comparisons and applied t…
This paper presents a detailed comparison of a recently proposed algorithm for optimizing decision trees, tree alternating optimization (TAO), with other popular, established algorithms. We compare their performance on a number of classification and regression datasets of various complexity, different size and dimensio…
Rank regression from pairwise comparisons requires many comparisons to accurately learn model parameters.
We propose an experimental comparison between Deep Echo State Networks (DeepESNs) and gated Recurrent Neural Networks (RNNs) on multivariate time-series prediction tasks. In particular, we compare reservoir and fully-trained RNNs able to represent signals featured by multiple time-scales dynamics. The analysis is perfo…
Rank aggregation based on pairwise comparisons over a set of items has a wide range of applications. Although considerable research has been devoted to the development of rank aggregation algorithms, one basic question is how to efficiently collect a large amount of high-quality pairwise comparisons for the ranking pur…
New models ensure monotonicity in preference learning, improving accuracy especially with limited data.
Enhances AI models with human feedback for noisy data.
Novel CNN-based gaze scanpath comparison distinguishes experts from novices in dental radiograph interpretation.
Binary feedback outperforms ordinal comparisons in ranking recovery.
In this paper, we compare the three most popular algorithms for hyperparameter optimization (Grid Search, Random Search, and Genetic Algorithm) and attempt to use them for neural architecture search (NAS). We use these algorithms for building a convolutional neural network (search architecture). Experimental results on…
Develops a fast, accurate method for comparing networks.
We propose the Heterogeneous Thurstone Model (HTM) for aggregating ranked data, which can take the accuracy levels of different users into account. By allowing different noise distributions, the proposed HTM model maintains the generality of Thurstone's original framework, and as such, also extends the Bradley-Terry-Lu…
Study compares federated learning and coreset approaches for privacy in distributed machine learning.
Transformer learns to estimate negative binomial parameters efficiently.
SC improves robustness in model comparison for misspecified models.
The article explains how to estimate confusion matrices for classifiers using unlabeled data.
Proposes a new method for deep ensembles that improves accuracy and calibration.
New scoring rules compare probabilistic top lists in classification.
Automatic cover detection -- the task of finding in an audio database all the covers of one or several query tracks -- has long been seen as a challenging theoretical problem in the MIR community and as an acute practical problem for authors and composers societies. Original algorithms proposed for this task have prove…
In this paper, prediction for linear systems with missing information is investigated. New methods are introduced to improve the Mean Squared Error (MSE) on the test set in comparison to state-of-the-art methods, through appropriate tuning of Bias-Variance trade-off. First, the use of proposed Soft Weighted Prediction …
Vecchia approximations provide the best accuracy-runtime trade-off for Gaussian process approximations.
Proposes a machine learning predictor for survey data.
Unified view on random walk and Weisfeiler-Leman kernels, improving accuracy.
Researchers study fairness-accuracy tradeoffs in predictive models for multiple groups.
Study compares machine learning models and BERT on SQuAD dataset.
Bispectral OT improves dataset comparison by preserving intrinsic coherence.
A comparison of SLDS and LSTM for pedestrian behavior prediction shows SLDS works better with shorter sequences.
A number of applications (e.g., AI bot tournaments, sports, peer grading, crowdsourcing) use pairwise comparison data and the Bradley-Terry-Luce (BTL) model to evaluate a given collection of items (e.g., bots, teams, students, search results). Past work has shown that under the BTL model, the widely-used maximum-likeli…
In recent work on both generative and discriminative score to log-likelihood-ratio calibration, it was shown that linear transforms give good accuracy only for a limited range of operating points. Moreover, these methods required tailoring of the calibration training objective functions in order to target the desired r…
Active learning optimizes correlation clustering by querying the most informative pairwise comparisons.
Multi-step ahead forecasting is still an open challenge in time series forecasting. Several approaches that deal with this complex problem have been proposed in the literature but an extensive comparison on a large number of tasks is still missing. This paper aims to fill this gap by reviewing existing strategies for m…
Research in natural language processing proceeds, in part, by demonstrating that new models achieve superior performance (e.g., accuracy) on held-out test data, compared to previous results. In this paper, we demonstrate that test-set performance scores alone are insufficient for drawing accurate conclusions about whic…
Simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can be used to non-invasively measure the spatiotemporal dynamics of the human brain. One challenge is dealing with the artifacts that each modality introduces into the other when the two are recorded concurrently, for…
Recent progress in the development of efficient computational algorithms to price financial derivatives is summarized. A first algorithm is based on a path integral approach to option pricing, while a second algorithm makes use of a neural network parameterization of option prices. The accuracy of the two methods is es…
Online Streaming Feature Selection (OSFS) is a sequential learning problem where individual features across all samples are made available to algorithms in a streaming fashion. In this work, firstly, we assert that OSFS's main assumption of having data from all the samples available at runtime is unrealistic and introd…
A method compares AI corrections to a base model for explaining predictions.
s-OTDD compares datasets efficiently without training, robust to class variations.
Random Projection (RP) technique has been widely applied in many scenarios because it can reduce high-dimensional features into low-dimensional space within short time and meet the need of real-time analysis of massive data. There is an urgent need of dimensionality reduction with fast increase of big genomics data. Ho…
Paper introduces new metrics for evaluating model accuracy.
Usually one compares the accuracy of two competing classifiers via null hypothesis significance tests (nhst). Yet the nhst tests suffer from important shortcomings, which can be overcome by switching to Bayesian hypothesis testing. We propose a Bayesian hierarchical model which jointly analyzes the cross-validation res…
Novel method for Bayesian model comparison using deep learning.
Estimates density ratio for two-sample comparison using tree models.