2 citations · 3 across the 11 of their papers we have counts for
11 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…
A Compositional Theory of Curvature in Probabilistic Circuits
Hrithik Suresh, Sahil Sidheekh, Shelar Parth Vijay +3
Probabilistic Circuits (PCs) are generative models that support exact inference and, unlike deep neural networks, admit an exact and tractable measure of loss-surface curvature: th…
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…
Geometry-Aware Probabilistic Circuits via Voronoi Tessellations
Sahil Sidheekh, Sriraam Natarajan
Probabilistic circuits (PCs) enable exact and tractable inference but employ data independent mixture weights that limit their ability to capture local geometry of the data manifol…
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 Sharpness-Aware Learning of Probabilistic Circuits
Hrithik Suresh, Sahil Sidheekh, Vishnu Shreeram M. P +2
Probabilistic Circuits (PCs) are a class of generative models that allow exact and tractable inference for a wide range of queries. While recent developments have enabled the learn…
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…
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…
Building Expressive and Tractable Probabilistic Generative Models: A Review
Sahil Sidheekh, Sriraam Natarajan
We present a comprehensive survey of the advancements and techniques in the field of tractable probabilistic generative modeling, primarily focusing on Probabilistic Circuits (PCs)…