6 papers
Reliable Fusion of Conflicting Experts
Pranuthi Tenali, Sahil Sidheekh, Saurabh Mathur +4
We study the problem of aggregating opinions from multiple black-box experts in noisy, conflict-prone settings where expert reliability varies across inputs. Static aggregation met…
On the Transferability of Agricultural Weed Detection Under Cross-Field Distribution Shift
Nikhilesh Prabhakar, Pranuthi Tenali, Wilfredo Abudeye Fernandez +5
Accurate agricultural weed detection in real-world field conditions is essential for precision agriculture, enabling targeted intervention and reducing yield loss. Recent work has…
Dynamic Structural Causal Modeling for Sleep
Ranveer Singh, Saurabh Mathur, Pranuthi Tenali +2
The causal dynamics of sleep-disordered breathing are complex and vary across patient populations, hindering the development of targeted interventions. We learn dynamic causal grap…
A Neurosymbolic Approach for Explainable Early Diagnosis of Alzheimer's Disease
Ranveer Singh, Pranuthi Tenali, Saurabh Mathur +6
Identifying reliable Alzheimer's disease (AD) markers typically requires manual, labor-intensive transcription and expert analysis, limiting its scale. We introduce an automated pi…
Context-specific Credibility-aware Multimodal Fusion with Conditional Probabilistic Circuits
Pranuthi Tenali, Sahil Sidheekh, Saurabh Mathur +3
Multimodal fusion requires integrating information from multiple sources that may conflict depending on context. Existing fusion approaches typically rely on static assumptions abo…
Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits
Sahil Sidheekh, Pranuthi Tenali, Saurabh Mathur +3
We consider the problem of late multi-modal fusion for discriminative learning. Motivated by noisy, multi-source domains that require understanding the reliability of each data sou…