2 citations · 2 across the 10 of their papers we have counts for
10 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…
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…
Depth-Recurrent Attention Mixtures: Giving Latent Reasoning the Attention it Deserves
Jonas Knupp, Jan Hendrik Metzen, Jeremias Bohn +2
Depth-recurrence facilitates latent reasoning by sharing parameters across depths. However, prior work lacks combined FLOP-, parameter-, and memory-matched baselines, underutilizes…
Human-Allied Relational Reinforcement Learning
Fateme Golivand Darvishvand, Hikaru Shindo, Sahil Sidheekh +2
Reinforcement learning (RL) has experienced a second wind in the past decade. While incredibly successful in images and videos, these systems still operate within the realm of prop…
Tractable Representation Learning with Probabilistic Circuits
Steven Braun, Sahil Sidheekh, Antonio Vergari +3
Probabilistic circuits (PCs) are powerful probabilistic models that enable exact and tractable inference, making them highly suitable for probabilistic reasoning and inference task…
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…
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…