10 citations · 24 across the 29 of their papers we have counts for
23 papers · 1 filter
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…
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…
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…
Neurosymbolic Imitation Learning with Human Guidance: A Privileged Information Approach
Nikhilesh Prabhakar, Varun Balaji, Athresh Karanam +2
Imitation learning is widely used for learning to act in complex environments. While pure neural-based methods handle high dimensional data effectively, they suffer from the requir…
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…
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…
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)…
Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach
Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli +2
Predicting and discovering drug-drug interactions (DDIs) using machine learning has been studied extensively. However, most of the approaches have focused on text data or textual r…
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models
Matej Zečević, Devendra Singh Dhami, Athresh Karanam +2
While probabilistic models are an important tool for studying causality, doing so suffers from the intractability of inference. As a step towards tractable causal models, we consid…
Relational Boosted Bandits
Ashutosh Kakadiya, Sriraam Natarajan, Balaraman Ravindran
Contextual bandits algorithms have become essential in real-world user interaction problems in recent years. However, these algorithms rely on context as attribute value representa…
Fitted Q-Learning for Relational Domains
Srijita Das, Sriraam Natarajan, Kaushik Roy +2
We consider the problem of Approximate Dynamic Programming in relational domains. Inspired by the success of fitted Q-learning methods in propositional settings, we develop the fir…
Lifted Hybrid Variational Inference
Yuqiao Chen, Yibo Yang, Sriraam Natarajan +1
A variety of lifted inference algorithms, which exploit model symmetry to reduce computational cost, have been proposed to render inference tractable in probabilistic relational mo…
Non-Parametric Learning of Gaifman Models
Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli +1
We consider the problem of structure learning for Gaifman models and learn relational features that can be used to derive feature representations from a knowledge base. These relat…
Beyond Textual Data: Predicting Drug-Drug Interactions from Molecular Structure Images using Siamese Neural Networks
Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli +2
Predicting and discovering drug-drug interactions (DDIs) is an important problem and has been studied extensively both from medical and machine learning point of view. Almost all o…