Dataset pruning is the process of removing sub-optimal tuples from a dataset to improve the learning of a machine learning model. In this paper, we compared the performance of different algorithms, first on an unpruned dataset and then on an iteratively pruned dataset. The goal was to understand whether an algorithm (s…
On-device research index
arXiv research
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.
168,657 papers · 148 categories
Trend · papers per month
48 results for “algorithmic datasets”
One significant challenge to scaling entity resolution algorithms to massive datasets is understanding how performance changes after moving beyond the realm of small, manually labeled reference datasets. Unlike traditional machine learning tasks, when an entity resolution algorithm performs well on small hold-out datas…
Sommelier recommends machine learning algorithms for datasets based on scholarly knowledge.
problem Identifying the best machine learning algorithms for a dataset.
method Word embedding representations of scholarly knowledge to recommend algorithms.
result Sommelier's recommendations achieve on average 97.7% of optimal accuracy.
A new algorithm enhances minority class representation in imbalanced datasets.
problem Improving classification performance on imbalanced datasets.
method PO-QG algorithm using Proxima-Orion neighbors and q-Gaussian weighting.
result The PO-QG algorithm improves overall classification performance.
Mixed datasets consist of both numeric and categorical attributes. Various k-means-based clustering algorithms have been developed for these datasets. Generally, these algorithms use random partition as a starting point, which tends to produce different clustering results for different runs. In this paper, we propose, …
Framework for understanding overfitting and underfitting using information theory.
problem Understanding and preventing overfitting and underfitting in machine learning.
method Information-theoretic framework measuring algorithm capacity and dataset information transfer.
result Upper-bounding algorithm capacity and establishing its relationship to machine learning quantities.
Ranking recommendation algorithms across datasets using Bradley-Terry model
problem Comparing recommendation algorithms across different datasets
method Introduce a novel data-driven ranking methodology based on Bradley-Terry model
result The obtained ranking depends on key dataset statistics
Differential privacy for simple linear regression protects small datasets from individual data leaks.
problem Protecting sensitive personal information in small datasets from individual data leaks.
method Differential privacy algorithms for simple linear regression tailored for small datasets (tens to hundreds of datapoints).
result Robust estimators like Theil-Sen perform well on small datasets, but standard algorithms improve as dataset size increases.
Elastic co-clustering improves clustering of single-cell genomic data.
problem Improving clustering performance of single-cell genomic datasets.
method Elastic coupled co-clustering in an unsupervised transfer learning framework.
result Our algorithm significantly improves clustering performance over traditional methods.
First private Bayesian optimization algorithm with provable performance.
problem Privacy-preserving Bayesian optimization in outsourced settings.
method Random projection-based transformation to preserve privacy and distances.
result Regret bound similar to non-private GP-UCB established for PO-GP-UCB.
Develops efficient algorithm for distilling supervised and offline RL datasets.
problem Creating synthetic datasets for supervised and offline RL models to match training data performance.
method An efficient algorithm based on matching losses without model training, using randomly sampled regressors.
result Synthetic datasets derived with sampled regressors match MSE loss of bounded linear models on training data.
Partial Wasserstein Covering aims to identify missing patterns in datasets.
problem Identifying missing patterns in datasets compared to actual applications.
method Formulated as a discrete optimization problem with partial Wasserstein divergence. Proved submodular, allowing greedy approximation. Proposed quasi-greedy algorithms with acceleration techniques.
result Efficiently fills gaps and finds missing scenes in real driving scenes datasets.
MementoML: Performance of selected machine learning algorithm configurations on OpenML100 datasetscs.LG
Paper compiles ML algorithm performance benchmarks on OpenML datasets.
problem Finding optimal hyperparameters for ML algorithms efficiently.
method Generated benchmark data for 7 ML algorithms on 39 datasets, fixed hyperparameters before testing.
result Comprehensive dataset of ML algorithm performance sensitivity.
Study shows partially-typed NER datasets can match fully-typed ones in model performance.
problem Leveraging multiple partially-typed NER datasets for training models without fully-typed annotations.
method Systematic analysis and controlled experiments comparing partially-typed and fully-typed datasets.
result Models trained with partially-typed annotations can achieve similar performance to those trained with fully-typed annotations.
Spectral clustering approaches have led to well-accepted algorithms for finding accurate clusters in a given dataset. However, their application to large-scale datasets has been hindered by computational complexity of eigenvalue decompositions. Several algorithms have been proposed in the recent past to accelerate spec…
Paper introduces ILD algorithm to determine Bayes error for binary classification.
problem Determining the best possible performance in binary classification problems.
method Model-agnostic ILD algorithm to calculate Bayes error.
result Provides intrinsic limits of any binary classification algorithm.
New algorithm selects variables from large datasets.
problem Automatic selection of variables from large datasets.
method Uses Graphical Models and combines with OLS method.
result Outperforms LASSO method in forecasting models.
IRT improves algorithm evaluation across datasets.
problem Evaluating the performance of algorithm portfolios.
method Modified IRT framework for evaluating algorithm portfolios across datasets.
result Richer characteristics of algorithm performance are revealed.
Meta-learning symbolic default hyperparameters from dataset properties.
problem Empirical hyperparameter optimization is slow and requires manual configuration.
method Evolutionary algorithm to learn symbolic hyperparameter formulas from dataset properties.
result Meta-learning finds viable symbolic defaults for ML algorithms.
QCircuitBench provides a dataset for evaluating AI's ability to design quantum algorithms.
problem Lack of datasets for evaluating AI's capability in designing quantum algorithms.
method Developed a comprehensive benchmark dataset with 120,290 data points, including 25 algorithms and 3 task suites.
result LLMs exhibit consistent error patterns and fine-tuning does not always outperform few-shot learning.
Off-policy reinforcement learning (RL) using a fixed offline dataset of logged interactions is an important consideration in real world applications. This paper studies offline RL using the DQN replay dataset comprising the entire replay experience of a DQN agent on 60 Atari 2600 games. We demonstrate that recent off-p…
MEx: Multi-modal Exercises Dataset is a multi-sensor, multi-modal dataset, implemented to benchmark Human Activity Recognition(HAR) and Multi-modal Fusion algorithms. Collection of this dataset was inspired by the need for recognising and evaluating quality of exercise performance to support patients with Musculoskelet…
A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and selec…
KIP meta-learning compresses datasets significantly.
problem Training data size and quality issues in machine learning.
method Kernel Inducing Points (KIP) for dataset compression.
result Significant reduction in dataset size with similar model performance.
The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses the random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. Many initial…
RFAD uses random features to speed up dataset distillation.
problem Efficiently compress large datasets for reduced storage and computation.
method Random feature approximation of the Neural Network Gaussian Process kernel.
result At least 100-fold speedup over KIP with competitive accuracy.
This paper evaluates t-SNE and MDS for reducing dimensions in datasets and classifying them with KNN, ENN, and SVM.
problem Reducing dimensions in datasets for better classification performance.
method t-SNE and MDS applied to nine datasets, followed by KNN, ENN, and SVM classification.
result Performance comparison of t-SNE and MDS with KNN, ENN, and SVM.
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive inf…
Joint, Partially-joint, and Individual Independent Component Analysis in Multi-Subject fMRI Datastat.ML
Objective: Joint analysis of multi-subject brain imaging datasets has wide applications in biomedical engineering. In these datasets, some sources belong to all subjects (joint), a subset of subjects (partially-joint), or a single subject (individual). In this paper, this source model is referred to as joint/partially-…
The -means algorithm is arguably the most popular nonparametric clustering method but cannot generally be applied to datasets with incomplete records. The usual practice then is to either impute missing values under an assumed missing-completely-at-random mechanism or to ignore the incomplete records, and apply the …
We present an algorithm to generate synthetic datasets of tunable difficulty on classification of Morse code symbols for supervised machine learning problems, in particular, neural networks. The datasets are spatially one-dimensional and have a small number of input features, leading to high density of input informatio…
Enhances machine learning with background knowledge through feature generation.
problem Machine learning struggles with small datasets.
method Generates features from auxiliary datasets not similar to the original task.
result Significant improvement in various learning tasks.
This paper presents a comparison of six machine learning (ML) algorithms: GRU-SVM (Agarap, 2017), Linear Regression, Multilayer Perceptron (MLP), Nearest Neighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (Wolberg, Street, & Mangasarian, 1…
Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifierscs.LG
DIGEN benchmark provides synthetic datasets for ML algorithm evaluation.
problem Understanding and comparing machine learning algorithms' performance.
method Synthetic datasets generated using 40 mathematical functions to evaluate machine learning algorithms.
result DIGEN resource facilitates understanding why algorithms perform poorly and provides ideas for improvement.
Develops a framework for consistent clustering algorithm benchmarking.
problem Lack of consistent benchmarking and diverse datasets for clustering algorithms.
method Consistent methodology, aggregated and polished datasets, interactive explorer.
result Introduces a comprehensive framework for testing clustering algorithms.
Combinatorial optimization problems for clustering are known to be NP-hard. Most optimization methods are not able to find the global optimum solution for all datasets. To solve this problem, we propose a global optimal path-based clustering (GOPC) algorithm in this paper. The GOPC algorithm is based on two facts: (1) …
Dataset distillation is a method for reducing dataset sizes by learning a small number of synthetic samples containing all the information of a large dataset. This has several benefits like speeding up model training, reducing energy consumption, and reducing required storage space. Currently, each synthetic sample is …
Scalable Clustering: Large Scale Unsupervised Learning of Gaussian Mixture Models with Outliersstat.ML
Paper introduces scalable clustering for large datasets with outliers.
problem Lack of scalable algorithms for large datasets with outliers.
method Provable robust clustering algorithm based on loss minimization for Gaussian mixture models.
result Algorithm provides high accuracy with theoretical guarantees and outperforms existing methods.
New algorithm improves dataset distillation with 108% improvement on ImageNet.
problem Improving dataset distillation performance.
method Reparameterization and convexification of implicit gradients (RCIG).
result Establishes new state-of-the-art on various dataset distillation tasks.
Sketching algorithm finds closest point on convex hull efficiently.
problem Finding the closest point on a convex hull of large datasets.
method Sketching procedure to exploit data structure, gradient project method.
result Faster solution than standard optimization algorithms.
Discovering causal relationships from data is the ultimate goal of many research areas. Constraint based causal exploration algorithms, such as PC, FCI, RFCI, PC-simple, IDA and Joint-IDA have achieved significant progress and have many applications. A common problem with these methods is the high computational complex…
Machine learning qualifies computers to assimilate with data, without being solely programmed [1, 2]. Machine learning can be classified as supervised and unsupervised learning. In supervised learning, computers learn an objective that portrays an input to an output hinged on training input-output pairs [3]. Most effic…
In the k-nearest neighbor algorithm (k-NN), the determination of classes for test instances is usually performed via a majority vote system, which may ignore the similarities among data. In this research, the researcher proposes an approach to fine-tune the selection of neighbors to be passed to the majority vote syste…
Paper addresses challenges in benchmarking stream learning algorithms with real-world data.
problem Lack of publicly available non-stationary real-world datasets for evaluating stream algorithms.
method Proposes a new public data repository for benchmarking stream algorithms with real-world data.
result Mitigates problems related to dataset choice in experimental evaluation of stream classifiers and drift detectors.
A novel resampling technique addresses class imbalance in imbalanced datasets.
problem Class imbalance in real-world datasets, especially in rare event detection.
method Developed two oversampling algorithms: G1Nos 1-Nearest Neighbour.
result Our oversampling algorithms outperform state-of-the-art methods in all metrics.
We introduce a method called multi-scale local shape analysis, or MLSA, for extracting features that describe the local structure of points within a dataset. The method uses both geometric and topological features at multiple levels of granularity to capture diverse types of local information for subsequent machine lea…
Recently, locality sensitive hashing (LSH) was shown to be effective for MIPS and several algorithms including -ALSH, Sign-ALSH and Simple-LSH have been proposed. In this paper, we introduce the norm-range partition technique, which partitions the original dataset into sub-datasets containing items with similar 2-…
Study merges datasets to improve AI model performance.
problem Improving machine learning models with diverse datasets.
method Developed an algorithm using oracle inequality and data-driven estimators.
result Algorithm reduces population loss with high probability.