18 citations · 30 across the 8 of their papers we have counts for
5 papers · 1 filter
Unsupervised Adaptation of Semantic Segmentation Models without Source Data
Sujoy Paul, Ansh Khurana, Gaurav Aggarwal
We consider the novel problem of unsupervised domain adaptation of source models, without access to the source data for semantic segmentation. Unsupervised domain adaptation aims t…
SITA: Single Image Test-time Adaptation
Ansh Khurana, Sujoy Paul, Piyush Rai +2
In Test-time Adaptation (TTA), given a source model, the goal is to adapt it to make better predictions for test instances from a different distribution than the source. Crucially,…
Node-Level Differentially Private Graph Neural Networks
Ameya Daigavane, Gagan Madan, Aditya Sinha +3
Graph Neural Networks (GNNs) are a popular technique for modelling graph-structured data and computing node-level representations via aggregation of information from the neighborho…
On Learning to Rank Long Sequences with Contextual Bandits
Anirban Santara, Claudio Gentile, Gaurav Aggarwal +1
Motivated by problems of learning to rank long item sequences, we introduce a variant of the cascading bandit model that considers flexible length sequences with varying rewards an…
Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare
Arpita Biswas, Gaurav Aggarwal, Pradeep Varakantham +1
In many public health settings, it is important for patients to adhere to health programs, such as taking medications and periodic health checks. Unfortunately, beneficiaries may g…