Soumya Ghosh

Orcid: 0000-0001-8508-0641

According to our database1, Soumya Ghosh authored at least 61 papers between 2006 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2024
Multi-Level Explanations for Generative Language Models.
CoRR, 2024

Thermometer: Towards Universal Calibration for Large Language Models.
CoRR, 2024

Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations.
CoRR, 2024

Improved Evidential Deep Learning via a Mixture of Dirichlet Distributions.
CoRR, 2024

2023
Assessment of Prediction Intervals Using Uncertainty Characteristics Curves.
CoRR, 2023

Reliable Gradient-free and Likelihood-free Prompt Tuning.
Proceedings of the Findings of the Association for Computational Linguistics: EACL 2023, 2023

Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data.
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Nat. Comput. Sci., 2022

Are you using test log-likelihood correctly?
CoRR, 2022

IoT and E-Learning with the Impact of COVID-19 Pandemic Lockdown on the Undergraduate University Student Blood Pressure Levels.
Proceedings of the Selected Papers of the Workshop on Emerging Technology Trends on the Smart Industry and the Internet of Things (TTSIIT 2022), 2022

Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Uncertainty Quantification 360: A Hands-on Tutorial.
Proceedings of the CODS-COMAD 2022: 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD), Bangalore, India, January 8, 2022

Measuring the robustness of Gaussian processes to kernel choice.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
DPVis: Visual Analytics With Hidden Markov Models for Disease Progression Pathways.
IEEE Trans. Vis. Comput. Graph., 2021

Measuring the sensitivity of Gaussian processes to kernel choice.
CoRR, 2021

Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI.
CoRR, 2021

Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals.
CoRR, 2021

Post-hoc loss-calibration for Bayesian neural networks.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders.
Proceedings of the Machine Learning for Healthcare Conference, 2021

2020
Complication Risk Profiling in Diabetes Care: A Bayesian Multi-Task and Feature Relationship Learning Approach.
IEEE Trans. Knowl. Data Eng., 2020

EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders.
CoRR, 2020

Approximate Cross-Validation for Structured Models.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Personalized Input-Output Hidden Markov Models for Disease Progression Modeling.
Proceedings of the Machine Learning for Healthcare Conference, 2020

Model Fusion with Kullback-Leibler Divergence.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Model Selection in Bayesian Neural Networks via Horseshoe Priors.
J. Mach. Learn. Res., 2019

Quality of Uncertainty Quantification for Bayesian Neural Network Inference.
CoRR, 2019

DPVis: Visual Exploration of Disease Progression Pathways.
CoRR, 2019

Statistical Model Aggregation via Parameter Matching.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Bayesian Nonparametric Federated Learning of Neural Networks.
Proceedings of the 36th International Conference on Machine Learning, 2019

Unsupervised Learning with Contrastive Latent Variable Models.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
A Review on DNA Based Cryptographic Techniques.
Int. J. Netw. Secur., 2018

Latent Projection BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights.
CoRR, 2018

Simultaneous Modeling of Multiple Complications for Risk Profiling in Diabetes Care.
CoRR, 2018

Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors.
Proceedings of the 35th International Conference on Machine Learning, 2018

Context-Sensitive Prediction of Facial Expressivity Using Multimodal Hierarchical Bayesian Neural Networks.
Proceedings of the 13th IEEE International Conference on Automatic Face & Gesture Recognition, 2018

Early Prediction of Diabetes Complications from Electronic Health Records: A Multi-Task Survival Analysis Approach.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Is There a Priority Shift in Mental Health Clinical Trials?
Proceedings of the MEDINFO 2017: Precision Healthcare through Informatics, 2017

Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Exploring Factors that Associated with Missing Values in Observational Huntington's Disease Study Data.
Proceedings of the Summit on Clinical Research Informatics, 2017

An Exploration of Latent Structure in Observational Huntington's Disease Studies.
Proceedings of the Summit on Clinical Research Informatics, 2017

Clinical Trials.Gov: A Topical Analyses.
Proceedings of the Summit on Clinical Research Informatics, 2017

A Data-Driven Method for Generating Robust Symptom Onset Indicators in Disease Registry Data.
Proceedings of the AMIA 2017, 2017

2016
Graph theoretic interpretation of Bangla traditional grammar.
Proceedings of the 13th International Conference on Natural Language Processing, 2016

Deep State Space Models for Computational Phenotyping.
Proceedings of the 2016 IEEE International Conference on Healthcare Informatics, 2016

Assumed Density Filtering Methods for Learning Bayesian Neural Networks.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Bayesian Nonparametric Discovery of Layers and Parts from Scenes and Objects.
PhD thesis, 2015

2014
Nonparametric Clustering with Distance Dependent Hierarchies.
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Investigation into machine learning algorithms as applied to motor cortex signals for classification of movement stages.
Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014

2012
From Deformations to Parts: Motion-based Segmentation of 3D Objects.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Nonparametric learning for layered segmentation of natural images.
Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

2011
A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity.
PLoS Comput. Biol., 2011

Architecture of A Scalable Dynamic Parallel WebCrawler with High Speed Downloadable Capability for a Web Search Engine
CoRR, 2011

Spatial distance dependent Chinese restaurant processes for image segmentation.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010
Automatic Annotation of Planetary Surfaces With Geomorphic Labels.
IEEE Trans. Geosci. Remote. Sens., 2010

A segmentation guided label propagation scheme for autonomous navigation.
Proceedings of the IEEE International Conference on Robotics and Automation, 2010

2009
A general framework for reconciling multiple weak segmentations of an image.
Proceedings of the IEEE Workshop on Applications of Computer Vision (WACV 2009), 2009

Topic model methods for automatically identifying out-of-scope resources.
Proceedings of the 2009 Joint International Conference on Digital Libraries, 2009

Using weak supervision in learning Gaussian mixture models.
Proceedings of the International Joint Conference on Neural Networks, 2009

2007
Machine Learning Tools for Automatic Mapping of Martian Landforms.
IEEE Intell. Syst., 2007

Machine Learning for Automatic Mapping of Planetary Surfaces.
Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

2006
Automatic Recognition of Landforms on Mars Using Terrain Segmentation and Classification.
Proceedings of the Discovery Science, 9th International Conference, 2006


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