9 citations · 33 across the 12 of their papers we have counts for
11 papers · 1 filter
Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selection
Abir De, Soumen Chakrabarti
Submodular functions and variants, through their ability to characterize diversity and coverage, have emerged as a key tool for data selection and summarization. Many recent approa…
Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networks
Indradyumna Roy, Soumen Chakrabarti, Abir De
The graph retrieval problem is to search in a large corpus of graphs for ones that are most similar to a query graph. A common consideration for scoring similarity is the maximum c…
Integrating Transductive And Inductive Embeddings Improves Link Prediction Accuracy
Chitrank Gupta, Yash Jain, Abir De +1
In recent years, inductive graph embedding models, \emph{viz.}, graph neural networks (GNNs) have become increasingly accurate at link prediction (LP) in online social networks. Th…
Counterfactual Explanations in Sequential Decision Making Under Uncertainty
Stratis Tsirtsis, Abir De, Manuel Gomez-Rodriguez
Methods to find counterfactual explanations have predominantly focused on one step decision making processes. In this work, we initiate the development of methods to find counterfa…
Training Data Subset Selection for Regression with Controlled Generalization Error
Durga Sivasubramanian, Rishabh Iyer, Ganesh Ramakrishnan +1
Data subset selection from a large number of training instances has been a successful approach toward efficient and cost-effective machine learning. However, models trained on a sm…
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan +2
The great success of modern machine learning models on large datasets is contingent on extensive computational resources with high financial and environmental costs. One way to add…