2 citations · 2 across the 12 of their papers we have counts for
13 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…
Tydra: An Efficient Hybrid Model for Tabular Data
Mieszko Komisarczyk, Saurabh Mathur, Maurice Kraus +2
Transformer-based tabular foundation models such as TabPFN achieve strong predictive performance but incur quadratic computational cost with context length. On the other hand, subq…
A Neurosymbolic Approach for Constructing Planning Domain Models from Clinical Narratives
Ranveer Singh, Saurabh Mathur, Michael Skinner +3
Surgical procedures such as laparoscopic appendectomy are complex, high-stakes processes, yet formalizing their workflows for decision support remains a significant challenge. Indu…
Causal Modeling of Adverse Pregnancy Outcomes via Adaptive LLM Proposals
Kavimayil P. Komarasamy, Saurabh Mathur, Ameet Soni +3
Adverse Pregnancy Outcomes (APOs) such as preterm birth and gestational diabetes can have long-term consequences for both the mother and child, yet an understanding of their causes…
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…
Imitation learning for clinical decision support in pediatric ECMO
Fateme Golivand, Michael Skinner, Saurabh Mathur +5
Pediatric critical care is a dynamic, high-stakes process involving constant monitoring and adjustments in life-saving treatments. Modeling these interventions is crucial for effec…
Recursive Inference Machines for Neural Reasoning
Mieszko Komisarczyk, Saurabh Mathur, Maurice Kraus +2
Neural reasoners such as Tiny Recursive Models (TRMs) solve complex problems by combining neural backbones with specialized inference schemes. Such inference schemes have been a ce…
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…
Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?
Sriraam Natarajan, Saurabh Mathur, Sahil Sidheekh +2
Human-in-the-loop (HIL) systems have emerged as a promising approach for combining the strengths of data-driven machine learning models with the contextual understanding of human e…
A Unified Framework for Human-Allied Learning of Probabilistic Circuits
Athresh Karanam, Saurabh Mathur, Sahil Sidheekh +1
Probabilistic Circuits (PCs) have emerged as an efficient framework for representing and learning complex probability distributions. Nevertheless, the existing body of research on…
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…
Explaining Deep Tractable Probabilistic Models: The sum-product network case
Athresh Karanam, Saurabh Mathur, Predrag Radivojac +3
We consider the problem of explaining a class of tractable deep probabilistic models, the Sum-Product Networks (SPNs) and present an algorithm ExSPN to generate explanations. To th…