19 citations · 60 across the 15 of their papers we have counts for
17 papers
Compressed Active Subspaces for Scalable Bayesian Inference
Thomas Flynn, Sanket Jantre, Byung-Jun Yoon +1
Active subspace methods provide a framework for quantifying predictive uncertainty in high-dimensional models by identifying and performing inference along parameter directions tha…
Data-efficient surrogate modeling of spectral functions using Gaussian processes: An application to the --- model
Sanket Jantre, Nathan M. Urban, Weiguo Yin +1
Spectral functions encode key many-body information but are costly to compute with high fidelity. Machine-learning surrogates have emerged as a powerful alternative, yet many appro…
Data-Augmented Few-Shot Neural Emulator for Computer-Model System Identification
Sanket Jantre, Deepak Akhare, Zhiyuan Wang +2
Partial differential equations (PDEs) underpin the modeling of many natural and engineered systems. It can be convenient to express such models as neural PDEs rather than using tra…
Robust, Online, and Adaptive Decentralized Gaussian Processes
Fernando Llorente, Daniel Waxman, Sanket Jantre +2
Gaussian processes (GPs) offer a flexible, uncertainty-aware framework for modeling complex signals, but scale cubically with data, assume static targets, and are brittle to outlie…
C-LoRA: Contextual Low-Rank Adaptation for Uncertainty Estimation in Large Language Models
Amir Hossein Rahmati, Sanket Jantre, Weifeng Zhang +4
Low-Rank Adaptation (LoRA) offers a cost-effective solution for fine-tuning large language models (LLMs), but it often produces overconfident predictions in data-scarce few-shot se…
Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis
Sanket Jantre, Tianle Wang, Gilchan Park +5
Identification of protein-protein interactions (PPIs) helps derive cellular mechanistic understanding, particularly in the context of complex conditions such as neurodegenerative d…
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders
A N M Nafiz Abeer, Sanket Jantre, Nathan M Urban +1
In recent years, deep generative models have been successfully adopted for various molecular design tasks, particularly in the life and material sciences. A critical challenge for…
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design
A N M Nafiz Abeer, Sanket Jantre, Nathan M Urban +1
Deep generative models have been accelerating the inverse design process in material and drug design. Unlike their counterpart property predictors in typical molecular design frame…
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks
Sanket Jantre, Nathan M. Urban, Xiaoning Qian +1
Bayesian inference for neural networks, or Bayesian deep learning, has the potential to provide well-calibrated predictions with quantified uncertainty and robustness. However, the…
Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks
Sanket Jantre, Shrijita Bhattacharya, Tapabrata Maiti
Network complexity and computational efficiency have become increasingly significant aspects of deep learning. Sparse deep learning addresses these challenges by recovering a spars…
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam +44
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderst…
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness
Sumegha Premchandar, Sandeep Madireddy, Sanket Jantre +1
Robust machine learning models with accurately calibrated uncertainties are crucial for safety-critical applications. Probabilistic machine learning and especially the Bayesian for…
Sequential Bayesian Neural Subnetwork Ensembles
Sanket Jantre, Shrijita Bhattacharya, Nathan M. Urban +4
Deep ensembles have emerged as a powerful technique for improving predictive performance and enhancing model robustness across various applications by leveraging model diversity. H…
Bayesian Quantile Regression for Longitudinal Count Data
Sanket Jantre
This work introduces Bayesian quantile regression modeling framework for the analysis of longitudinal count data. In this model, the response variable is not continuous and hence a…
Layer Adaptive Node Selection in Bayesian Neural Networks: Statistical Guarantees and Implementation Details
Sanket Jantre, Shrijita Bhattacharya, Tapabrata Maiti
Sparse deep neural networks have proven to be efficient for predictive model building in large-scale studies. Although several works have studied theoretical and numerical properti…
Low-Rank Tensor Regression for X-Ray Tomography
Sanket R. Jantre, Zichao Wendy Di
Tomographic imaging is useful for revealing the internal structure of a 3D sample. Classical reconstruction methods treat the object of interest as a vector to estimate its value.…
Quantile Regression Neural Networks: A Bayesian Approach
Sanket R. Jantre, Shrijita Bhattacharya, Tapabrata Maiti
This article introduces a Bayesian neural network estimation method for quantile regression assuming an asymmetric Laplace distribution (ALD) for the response variable. It is shown…