1 citations · 1 across the 4 of their papers we have counts for
4 papers
VoS: Variate Ordering Strategies for Skyline Query Optimization
Abhinav Gorantla, Pratanu Mandal, K. Selçuk Candan +1
Efficiency of skyline algorithms is highly influenced by the underlying data characteristics. Traditionally, optimization efforts have focused on minimizing the total number of tup…
The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction
Shu Wan, Abhinav Gorantla, Huan Liu +1
Under standard graphical assumptions, the Markov boundary of a target variable is the smallest set of features that renders every other feature redundant. Once the boundary is obse…
Causal Search for Skylines (CSS): Causally-Informed Selective Data De-Correlation
Pratanu Mandal, Abhinav Gorantla, K. Selçuk Candan +1
Skyline queries are popular and effective tools in multi-criteria decision support as they extract interesting (pareto-optimal) points that help summarize the available data with r…
Introducing CausalBench: A Flexible Benchmark Framework for Causal Analysis and Machine Learning
Ahmet Kapkiç, Pratanu Mandal, Shu Wan +5
While witnessing the exceptional success of machine learning (ML) technologies in many applications, users are starting to notice a critical shortcoming of ML: correlation is a poo…