arXiv research

Stochastic algo learns from evolving data, achieving optimal performance.

ACFS optimizes spectral risk under decision-dependent uncertainty using adaptive forest sampling.

Adaptive learning method for stochastic programs with latent uncertainty.

Analyzes how learning algorithms affect and are affected by data manipulation.

Simple policy outperforms complex ones in cloud auto-scaling.

POLA adapts learning rates for online time series prediction.

A new deep learning model for tabular data improves accuracy over GBDT.

A new Thompson Sampling framework handles uncertainty by imputing missing data.

The paper uses causal machine learning to optimize rework decisions in manufacturing.

Paper tackles dynamic pricing in a geometrically decaying environment, achieving better occupancy with lower rates.

New findings show ETO outperforms IEO in well-specified models with sufficient data.

New framework identifies worst-case shifts for predictive resource allocation models.

Problem definition. In retailing, discrete choice models (DCMs) are commonly used to capture the choice behavior of customers when offered an assortment of products. When estimating DCMs using transaction data, flexible models (such as machine learning models or nonparametric models) are typically not interpretable and…

New approach uses deep generative models for inventory and pricing decisions.

Package {mlr3spatiotempcv} simplifies spatiotemporal resampling methods in R.

Proposes PEMI for online selective conformal prediction with asymmetric rules.

A new algorithm uses concavity in Gaussian processes to optimize decisions in bandit problems.

This paper analyzes risk-sensitive reinforcement learning with Conditional Value-at-Risk (CVaR) for robust Markov Decision Processes.

New model optimizes assortment and pricing with dynamic customer arrivals.

Recently, product images have gained increasing attention in clothing recommendation since the visual appearance of clothing products has a significant impact on consumers' decision. Most existing methods rely on conventional features to represent an image, such as the visual features extracted by convolutional neural …

New method estimates policy performance under unobserved confounding.

AR design reduces multi-armed bandit experiment cost.

A new method for inventory control using in-context learning and generative models.

The study analyzes how covariance estimation errors affect the global minimum-variance portfolio under heavy-tailed distributions.

Improves Gower's similarity for mixed-type variables with automatic weighting.

New approach for learning with unknown utilities without explicit specification.

New framework captures long-term decision dependence in online learning.

Big data sets must be carefully partitioned into statistically similar data subsets that can be used as representative samples for big data analysis tasks. In this paper, we propose the random sample partition (RSP) data model to represent a big data set as a set of non-overlapping data subsets, called RSP data blocks,…

A new method for handling imbalanced big data using ensembles and smart data.

Prevents sensitive data generation in diffusion models using labeled and unlabeled data.

Study reveals Data Shapley's inconsistent performance in data selection tasks.

PRRO generates synthetic tabular data that improves SL performance and class distribution.

Defines data science as a natural ecosystem with challenges and missions.

Synthetic data enhances analytics but requires careful volume management.

This paper introduces C-DSL to improve data mining outcomes by considering context.

Proposes using probabilistic models for privacy-preserving synthetic data.

New test ensures quality of shared data in machine learning.

Paper creates fair synthetic data ensuring equal predictions across sensitive attributes.

DPA preserves data distribution in reduced dimensions.

Efficient synthetic data generation improves model performance on tabular data.

For most problems in science and engineering we can obtain data sets that describe the observed system from various perspectives and record the behavior of its individual components. Heterogeneous data sets can be collectively mined by data fusion. Fusion can focus on a specific target relation and exploit directly ass…

DAERNN models censored data using neural networks with data augmentation.

Data preprocessing techniques are devoted to correct or alleviate errors in data. Discretization and feature selection are two of the most extended data preprocessing techniques. Although we can find many proposals for static Big Data preprocessing, there is little research devoted to the continuous Big Data problem. A…

Data stream classification methods demonstrate promising performance on a single data stream by exploring the cohesion in the data stream. However, multiple data streams that involve several correlated data streams are common in many practical scenarios, which can be viewed as multi-task data streams. Instead of handli…

Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough …

This paper quantifies uncertainty in Data Shapley using statistical inference.

DCoM uses deep neural networks to detect semantic data types from raw column values.

Causal inference from observational data is the goal of many data analyses in the health and social sciences. However, academic statistics has often frowned upon data analyses with a causal objective. The introduction of the term "data science" provides a historic opportunity to redefine data analysis in such a way tha…