Updates
- MISFEAT: Feature Selection for Subgroups with Mutual Information Estimation
Mutual information (MI) quantifies the dependence between features and target variables and is widely used in feature selection for downstream tasks. However, what works globally often fails locally and different feature subsets may best… Read more: MISFEAT: Feature Selection for Subgroups with Mutual Information Estimation - (no title)
Our paper “DDTR: #Diffusion #Denoising #Trace #Recovery ” (co-authored by Maximilian Matyash, Avigdor Gal, and Arik Senderovich) was awarded the best paper award at the International Conference on Process Mining… Read more: (no title) - SOUND: Sanity Checking of Pipelines for Uncertain and Sparse Data Series🎉 BREAKTHROUGH PAPER ACCEPTED AT #ICDE2025! 🎉 Beyond thrilled to announce our paper “SOUND: Sanity Checking of Pipelines for Uncertain and Sparse Data Series” has been accepted at the prestigious International Conference… Read more: SOUND: Sanity Checking of Pipelines for Uncertain and Sparse Data Series
- A Rank-Based Approach to Recommender System’s Top-K Queries with Uncertain Scores”
The paper “A Rank-Based Approach to Recommender System’s Top-K Queries with Uncertain Scores”, co-authored by Coral Scharf, Avigdor Gal, Haggai Roitman, and Carmel Domshlak will be presented in #SIGMOD 2025.… Read more: A Rank-Based Approach to Recommender System’s Top-K Queries with Uncertain Scores” - Business Process Optimization workshopWe are happy to announce that Avi (Avigdor) Gal will deliver the keynote of the Business Process Optimization workshop (https://lnkd.in/eNfDTprR). In an interactive session, he will discuss his ideas on… Read more: Business Process Optimization workshop
- An unsupervised machine learning approach to the spatial analysis of urban systems through neighbourhoods’ dynamics
The paper “An unsupervised machine learning approach to the spatial analysis of urban systems through neighbourhoods’ dynamics” is a result of a collaborative effort with Alon Sagi, Avigdor Gal, Dani… Read more: An unsupervised machine learning approach to the spatial analysis of urban systems through neighbourhoods’ dynamics