Search Results for author:
Found 9 papers, 5 papers with code
FairEM360: A Suite for Responsible Entity Matching
Entity matching is one the earliest tasks that occur in the big data pipeline and is alarmingly exposed to unintentional biases that affect the quality of data.
Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching
Entity matching (EM) is a challenging problem studied by different communities for over half a century.
CREDENCE: Counterfactual Explanations for Document Ranking
Towards better explainability in the field of information retrieval, we present CREDENCE, an interactive tool capable of generating counterfactual explanations for document rankers.
Towards Algorithmic Fairness in Space-Time: Filling in Black Holes
New technologies and the availability of geospatial data have drawn attention to spatio-temporal biases present in society.
Effective Explanations for Entity Resolution Models
CERTA builds on a probabilistic framework that aims at computing the explanations evaluating the outcomes produced by using perturbed copies of the input records.
Alaska: A Flexible Benchmark for Data Integration Tasks
A limitation of such benchmarks is that they typically come with their own task definition and it can be difficult to leverage them for complex integration pipelines.
Entity Resolution
Databases
Efficient Discovery of Approximate Order Dependencies
Order dependencies (ODs) capture relationships between ordered domains of attributes.
Databases
Random Sampling for Group-By Queries
We consider random sampling for answering the ubiquitous class of group-by queries, which first group data according to one or more attributes, and then aggregate within each group after filtering through a predicate.
Databases Data Structures and Algorithms
PrivBayes： Private Data release via Bayesian networks
Given a dataset D, PRIVBAYES first constructs a Bayesian network N , which (i) provides a succinct model of the correlations among the attributes in D and (ii) allows us to approximate the distribution of data in D using a set P of lowdimensional marginals of D. After that, PRIVBAYES injects noise into each marginal in P to ensure differential privacy, and then uses the noisy marginals and the Bayesian network to construct an approximation of the data distribution in D. Finally, PRIVBAYES samples tuples from the approximate distribution to construct a synthetic dataset, and then releases the synthetic data.
