The study improves colorectal cancer survivability prediction by considering ethnicity.
problem Improving colorectal cancer survivability prediction using machine learning.
method Machine learning techniques applied to SEER cancer incidence database, comparing different ethnicities.
result Models perform better on single-ethnicity populations and provide different feature importance rankings.
The paper compares Bayesian trees, Cox models, and random forests for breast cancer survival data.
problem Modeling survival data with nonlinear and additive effects.
method Bayesian Additive Regression Trees, Cox proportional hazards, and Random Survival Forests.
result Bayesian trees outperform other models in terms of bias and prediction accuracy.
SeER hybrid model improves song recommendations and explains them.
problem Improving song recommendations and explaining them.
method Collaborative filtering and deep learning sequence models on MIDI content.
result Personalized explanations capture user preferences.
We have applied a little-known data transformation to subsets of the Surveillance, Epidemiology, and End Results (SEER) publically available data of the National Cancer Institute (NCI) to make it suitable input to standard machine learning classifiers. This transformation properly treats the right-censored data in the …
Paper addresses data heterogeneity in federated learning for CoxPH models in healthcare.
problem Data heterogeneity in federated learning of CoxPH models for healthcare.
method Feature-based clustering and event-based reporting strategy.
result Enhanced model accuracy and performance in federated survival analysis.
Bayesian model for cost-effectiveness analysis with subgroup discovery.
problem Statistical challenges in cost-effectiveness analysis, especially with non-random treatment assignment and censored data.
method Developed a nonparametric Bayesian model using Dirichlet and Gamma processes to estimate cost-survival distributions and identify cost-effectiveness subgroups.
result Identified and estimated policy-relevant causal CEA estimands using a Bayesian nonparametric g-computation procedure.
Machine learning detects metastatic breast cancer cases from linked EMR and cancer registry data.
problem Lack of metastatic recurrence data in cancer registries and EMRs.
method Semi-supervised machine learning on linked EMR and CCR data.
result Model achieved high accuracy in detecting metastatic breast cancer cases.
Aligns databases with Gaussian features using MAP estimation and thresholding.
problem Aligning databases with jointly Gaussian features.
method Two algorithms: MAP estimation and thresholding of log likelihood ratios.
result Conditions on mutual information for reliable performance of algorithms.
Study tackles database variability in medical data using ensemble models and CNNs.
problem Achieving robust generalization in machine learning models across multiple medical databases.
method Ensemble of local models based on convolutional neural networks (CNNs) and various data preprocessing techniques.
result Improved inter-database generalization performance and scalability of models.
Study connects database alignment and planted matching using Gaussian features.
problem Identify matching between correlated user features in anonymized databases.
method Derived results for database alignment and planted matching, showing connections and thresholds.
result Performance thresholds for database alignment converge to planted matching when feature dimensionality is sufficiently high.
This work applies SQL to deep learning, leveraging database techniques.
problem Applying deep learning techniques to databases.
method Expressing deep learning operations using SQL, a multidimensional array language.
result Demonstrates the feasibility of using SQL for deep learning operations.
Nostradamus links climate and stock market performance.
problem Understanding the impact of climate on stock prices.
method Analyzing historical data, climate indicators, and natural disasters.
result Significant correlation between climate and stock price fluctuations.
Study compares CDS databases and finds discrepancies due to various factors.
problem Comparing discrepancies among CDS databases.
method Comparing five major sources of corporate CDS prices over 2004-2010.
result CMA quotes lead price discovery and databases disagree on stock-CDS return analysis.
Graph database outperforms in filtering ESG stocks efficiently.
problem Efficiently filtering ESG stocks from large lists of equities.
method Compared SQL, No-SQL, and graph databases; used Python for database interactions; collected data from stock price and financial news.
result Graph database is more efficient for ESG stock filtering.
CwA optimizes search performance by jointly learning a balanced database partition and a neural probing function.
problem Suboptimal search performance due to mismatched database and query distributions.
method CwA jointly learns a balanced database partition and a neural probing function to optimize search performance directly for the query distribution.
result CwA achieves up to 4.7x throughput over state-of-the-art methods at equal recall.
PyODDS is a Python system for outlier detection in databases.
problem Outlier detection in databases for various users.
method End-to-end Python system with database support, including statistical and deep learning algorithms.
result Supports outlier detection algorithms for users with or without machine learning background.
Taxonomies of cryptocurrencies and comparisons with fiat money and databases.
problem Comparing cryptocurrencies, fiat money, and databases.
method Taxonomy creation and comparison analysis.
result Cryptocurrencies and databases have unique characteristics not found in fiat money.
Bayesian entity resolution merges together multiple, noisy databases and returns the minimal collection of unique individuals represented, together with their true, latent record values. Bayesian methods allow flexible generative models that share power across databases as well as principled quantification of uncertain…
We present a database of parliamentary debates that contains the complete record of parliamentary speeches from Dáil Éireann, the lower house and principal chamber of the Irish parliament, from 1919 to 2013. In addition, the database contains background information on all TDs (Teachta Dála, members of parliament), such…
Paper improves text-to-SQL translation by encoding schema relations with self-attention.
problem Improving text-to-SQL translation accuracy across diverse databases.
method Uses relation-aware self-attention to encode schema information.
result Significant gains on Spider dataset (42.94% exact match accuracy).
Graph Neural Networks improve machine learning on relational databases.
problem Training machine learning models on relational databases requires costly data extraction and feature engineering.
method Uses Graph Neural Networks to extract features from relational databases.
result Outperforms state-of-the-art automatic feature engineering methods.
Paper presents a new Wi-Fi RSS and geomagnetic field database for indoor localization and trajectory estimation.
problem Indoor localization and trajectory estimation challenges.
method Convolutional neural network (CNN) for RSS data and LSTM network for geomagnetic field intensity.
result CNN and LSTM networks show feasibility for localization and trajectory estimation.
New deep learning method validated across multiple sleep staging databases.
problem Improving automatic sleep scoring accuracy across different datasets.
method Ensemble of local models using deep learning for automatic sleep staging.
result Good general performance compared to human experts and state-of-the-art methods.
The purpose of this paper is to construct confidence intervals for the regression coefficients in the Fine-Gray model for competing risks data with random censoring, where the number of covariates can be larger than the sample size. Despite strong motivation from biomedical applications, a high-dimensional Fine-Gray mo…
auton-survival simplifies survival analysis for healthcare data.
problem Handling censored time-to-event data in healthcare.
method Open-source package for survival regression, adjustment, counterfactual estimation, phenotyping, and treatment effects.
result Demonstrates auton-survival's ability to support complex health and epidemiological questions.
EERN uses deep learning for relational databases, outperforming other methods.
problem Deep learning for relational databases.
method Equivariant Entity-Relationship Network (EERN) using MLP equivariant to Entity-Relationship model symmetries.
result EERN outperforms other methods in synthetic and real-data experiments.
The paper proposes a neural network model for estimating conditional mixture Weibull distributions with right-censored data.
problem Survival analysis with right-censored data in predictive maintenance and health fields.
method A neural network architecture is developed to estimate two-parameter Weibull distributions conditionally to features, extending to a finite mixture of Weibull distributions.
result The model outperforms state-of-the-art methods on real-world datasets and can consider any survival time horizon.
In this paper we deal with the offline handwriting text recognition (HTR) problem with reduced training datasets. Recent HTR solutions based on artificial neural networks exhibit remarkable solutions in referenced databases. These deep learning neural networks are composed of both convolutional (CNN) and long short-ter…
Extends Aff-Wild database for affect recognition in real-world settings.
problem Complex human emotional states in real-world settings.
method Developed deep neural architectures with attention mechanism for emotion recognition.
result Improved performance in emotion recognition using Aff-Wild2.
Survival analysis has been developed and applied in the number of areas including manufacturing, finance, economics and healthcare. In healthcare domain, usually clinical data are high-dimensional, sparse and complex and sometimes there exists few amount of time-to-event (labeled) instances. Therefore building an accur…
Adapts bandit algorithms for online survival analysis under Cox PH model.
problem Online survival analysis challenges in a bandit framework.
method Adapts three bandit algorithms to balance exploration and exploitation.
result Demonstrates sublinear regret bounds and effective learning of treatment policies.
New vector quantization method reduces relevance of parallel components in database points.
problem Scaling maximum inner product search to massive databases.
method Developed anisotropic vector quantization loss functions.
result Achieves state-of-the-art results on public benchmarks.
We lay theoretical foundations for new database release mechanisms that allow third-parties to construct consistent estimators of population statistics, while ensuring that the privacy of each individual contributing to the database is protected. The proposed framework rests on two main ideas. First, releasing (an esti…
We study the distribution of fluctuations over a time scale Δt (i.e., the returns) of the S&P 500 index by analyzing three distinct databases. Database (i) contains approximately 1 million records sampled at 1 min intervals for the 13-year period 1984-1996, database (ii) contains 8686 daily records for the 35-year pe…
The multiple fundamental frequency detection problem and the source separation problem from a single-channel signal containing multiple oscillatory components and a nonstationary noise are both challenging tasks. To extract the fetal electrocardiogram (ECG) from a single-lead maternal abdominal ECG, we face both challe…
Topology-based information retrieval improves query accuracy.
problem Query accuracy in databases with complex structures.
method Dilation-invariant comparative measures of persistent homology.
result Topology-based retrieval outperforms standard methods.
A statistical algorithm for categorizing different types of matches and fraud in image databases is presented. The approach is based on a generative model of a graph representing images and connections between pairs of identities, trained using properties of a matching algorithm between images.
A new algorithm uses bandits to diversify database activity monitoring.
problem Limitation of current DAM systems in collecting diverse data.
method Redefined DAM sampling as a bandit problem and developed a novel algorithm combining expert knowledge and random exploration.
result Adding diversity to sampling using the bandit-based approach improves coverage without decreasing alert quality.
The paper studies causal effects of multiple treatments in healthcare databases with rare outcomes.
problem Estimating causal effects of multiple treatments in healthcare databases with rare outcomes.
method The paper designs three sets of simulations and compares the operating characteristics of three types of methods: Bayesian Additive Regression Trees (BART), regression adjustment on multivariate spline of generalized propensity scores (RAMS), and inverse probability of treatment weighting (IPTW) with multinomial logistic regression or generalized boosted models.
result BART and RAMS provide lower bias and mean squared error compared to IPTW methods.
PyODDS automates outlier detection for new data sources.
problem Manual outlier detection is inefficient and domain-specific.
method Automated end-to-end system with Apache Spark and database support.
result PyODDS optimizes outlier detection pipelines automatically.
New method embeds DNA sequences for faster, more informative gene comparison.
problem Slow and costly sequence comparison methods for genes without exact matches.
method Recurrent neural networks to embed sequences in a low-dimensional space.
result Embedding allows for better comparison of genes without exact matches.
Content based image retrieval, a technique which uses visual contents of image to search images from large scale image databases according to users' interests. This paper provides a comprehensive survey on recent technology used in the area of content based face image retrieval. Nowadays digital devices and photo shari…
In this paper we study predictive pattern mining problems where the goal is to construct a predictive model based on a subset of predictive patterns in the database. Our main contribution is to introduce a novel method called safe pattern pruning (SPP) for a class of predictive pattern mining problems. The SPP method a…
We propose a method to identify all the nodes that are relevant to compute all the conditional probability distributions for a given set of nodes. Our method is simple, effcient, consistent, and does not require learning a Bayesian network first. Therefore, our method can be applied to high-dimensional databases, e.g. …
We introduce multiscale invariant dictionaries to estimate quantum chemical energies of organic molecules, from training databases. Molecular energies are invariant to isometric atomic displacements, and are Lipschitz continuous to molecular deformations. Similarly to density functional theory (DFT), the molecule is re…
Hashing has been widely used for large-scale approximate nearest neighbor search because of its storage and search efficiency. Recent work has found that deep supervised hashing can significantly outperform non-deep supervised hashing in many applications. However, most existing deep supervised hashing methods adopt a …
Traditional relation extraction predicts relations within some fixed and finite target schema. Machine learning approaches to this task require either manual annotation or, in the case of distant supervision, existing structured sources of the same schema. The need for existing datasets can be avoided by using a univer…
New image restoration method using localized patches and external databases.
problem Image restoration challenges.
method Localized structured prediction and non-linear multi-task learning for optimizing a penalized energy function.
result Strong statistical guarantees and practical effectiveness demonstrated on various image restoration problems.