arXiv research

FTRL algorithm with negative entropy regularizer achieves best-of-three-world results for linear bandits.

Adaptive linear bandit algorithm with best-of-three-worlds regret bounds.

The paper tackles statistical and computational challenges in learning correlated reward models.

We propose a neural architecture with the main characteristics of the most successful neural models of the last years: bidirectional RNNs, encoder-decoder, and the Transformer model. Evaluation on three sequence labelling tasks yields results that are close to the state-of-the-art for all tasks and better than it for s…

Improved dynamic regret analysis for strongly convex and smooth functions.

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.

New test ensures quality of shared data in machine learning.

Differential privacy allows quantifying privacy loss resulting from accessing sensitive personal data. Repeated accesses to underlying data incur increasing loss. Releasing data as privacy-preserving synthetic data would avoid this limitation, but would leave open the problem of designing what kind of synthetic data. W…

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…

Task-agnostic data valuation without validation requirements.

Data mining is about obtaining new knowledge from existing datasets. However, the data in the existing datasets can be scattered, noisy, and even incomplete. Although lots of effort is spent on developing or fine-tuning data mining models to make them more robust to the noise of the input data, their qualities still st…

New algorithm improves data imputation for complex multimodal data sets.

New algorithms for clustering and synthetic data generation of heterogeneous tabular datasets.

WeMix improves data augmentation by correcting bias in deep learning.

A new method reduces data valuation variance for more trustworthy data trading.

Adapts data analysis for growing data, improving generalization guarantees.

This work redefines data-centric AI by unifying categorical and cochain notions.

SMOTE-DP enhances synthetic data privacy without sacrificing utility.

VSAE learns from missing heterogeneous data by modeling latent dependencies.

Improves anomaly detection with contaminated unlabeled data.

ArtificialReplay improves data efficiency in bandits using historical data.

Data stream clustering tackles real-time data processing challenges.

Framework evaluates quality of synthetic data generated with differential privacy.

As data becomes the fuel driving technological and economic growth, a fundamental challenge is how to quantify the value of data in algorithmic predictions and decisions. For example, in healthcare and consumer markets, it has been suggested that individuals should be compensated for the data that they generate, but it…

Framework for pricing data products in data-poor markets.

Paper evaluates synthetic retail data for fidelity, utility, and privacy.

Introduces data ethics for mathematicians, covering background, open data, and privacy.

Method infers dynamics from incomplete time series data.

Captures data influence changes during training.

Enhances classification accuracy on low data sets using synthetic data.

New tool helps analyze complex financial data.

Paper proposes data quality measures for large-scale high-dimensional data.