Search Results for author:
Found 12 papers, 6 papers with code
Fairness and Bias in Truth Discovery Algorithms: An Experimental Analysis
In this paper, we conduct a systematic study of the bias and fairness of TD algorithms.
Deep learning for blocking in entity matching: a design space exploration
In this paper, we develop the DeepBlocker framework that significantly advances the state of the art in applying DL to blocking for EM.
Ranked #5 on
Blocking
on Abt-Buy
Fair Active Learning
Collecting accurate labeled data in societal applications is challenging and costly.
Fair Active Learning
Machine learning (ML) is increasingly being used in high-stakes applications impacting society.
Local Embeddings for Relational Data Integration
The embeddings are learned based on such sentences.
ZeroER: Entity Resolution using Zero Labeled Examples
We investigate an important problem that vexes practitioners: is it possible to design an effective algorithm for ER that requires Zero labeled examples, yet can achieve performance comparable to supervised approaches?
Are Outlier Detection Methods Resilient to Sampling?
In this paper, we introduce the notion of resilience to sampling for outlier detection methods.
Multi-Attribute Selectivity Estimation Using Deep Learning
Selectivity estimation - the problem of estimating the result size of queries - is a fundamental problem in databases.
Approximate Query Processing using Deep Generative Models
We use deep generative models, an unsupervised learning based approach, to learn the data distribution faithfully such that aggregate queries could be answered approximately by generating samples from the learned model.
Reuse and Adaptation for Entity Resolution through Transfer Learning
Entity resolution (ER) is one of the fundamental problems in data integration, where machine learning (ML) based classifiers often provide the state-of-the-art results.
DeepER -- Deep Entity Resolution
word embeddings), we present a novel ER system, called DeepER, that achieves good accuracy, high efficiency, as well as ease-of-use (i. e., much less human efforts).
Databases
Kharita: Robust Map Inference using Graph Spanners
Our algorithms utilize techniques from graph spanners so that they produce maps can effectively handle a wide variety of road and intersection shapes.
Other Computer Science
