S. Ghosh

发表

Automatic Recognition of Landforms on Mars Using Terrain Segmentation and Classification

Soumya Ghosh, Ricardo Vilalta, Tomasz F. Stepinski, 2006, Discovery Science.

Nonparametric learning for layered segmentation of natural images

Soumya Ghosh, Erik B. Sudderth, S. Ghosh, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

From Deformations to Parts: Motion-based Segmentation of 3D Objects

Soumya Ghosh, Michael J. Black, Erik B. Sudderth, 2012, NIPS.

Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights

Finale Doshi-Velez, S. Ghosh, Jiayu Yao, 2018, 1811.07006.

Projected BNNs : Avoiding weight-space pathologies by projecting neural network weights

Finale Doshi-Velez, S. Ghosh, Jiayu Yao, 2018 .

Cross-population Variational Autoencoders

K. Severson, S. Ghosh, Joe Davison, 2019 .

Isolating Latent Structure with Cross-population Variational Autoencoders

K. Severson, S. Ghosh, Joe Davison, 2019 .

Statistical Model Aggregation via Parameter Matching

Kristjan H. Greenewald, Soumya Ghosh, Mikhail Yurochkin, 2019, NeurIPS.

Spatial distance dependent Chinese restaurant processes for image segmentation

Soumya Ghosh, David M. Blei, Erik B. Sudderth, 2011, NIPS.

A MACHINE LEARNING APPROACH FOR AUTOMATED GEOMORPHIC MAP GENERATION

R. Vilalta, S. Ghosh, 2006 .

DPVis: Visual Exploration of Disease Progression Pathways pdf

Soumya Ghosh, Kenney Ng, Vibha Anand, 2019, ArXiv.

A Non-negative Matrix Factorization Framework for Combining Multiple Image Segmentations

Joseph J. Pfeiffer, S. Ghosh, J. Mulligan, 2008 .

Hierarchical Bayesian Neural Networks for Personalized Classification

Margrit Betke, H. Pfister, S. Ghosh, 2016 .

A Data-Driven Method for Generating Robust Symptom Onset Indicators in Huntington's Disease Registry Data.

Yu Cheng, Jianying Hu, S. Ghosh, 2017, AMIA ... Annual Symposium proceedings. AMIA Symposium.

Machine Learning Tools for Automatic Mapping of Martian Landforms

Soumya Ghosh, Ricardo Vilalta, Tomasz F. Stepinski, 2007, IEEE Intelligent Systems.

Bayesian Nonparametric Discovery of Layers and Parts from Scenes and Objects

S. Ghosh, 2015 .

A Data-Driven Method for Generating Robust Symptom Onset Indicators in Disease Registry Data

Ying Li, Yu Cheng, Soumya Ghosh, 2017, American Medical Informatics Association Annual Symposium.

Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data

Kristen A. Severson, Andrea D Matlock, J. Krakauer, 2022, Nature Computational Science.

Post-hoc Uncertainty Learning using a Dirichlet Meta-Model

P. Sattigeri, G. Wornell, S. Ghosh, 2022, AAAI.

Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks

Margrit Betke, Soumya Ghosh, Hanspeter Pfister, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Bayesian Nonparametric Federated Learning of Neural Networks

Yasaman Khazaeni, Kristjan H. Greenewald, Mayank Agarwal, 2019, ICML.

Approximate Cross-Validation for Structured Models pdf

Soumya Ghosh, William T. Stephenson, Tin D. Nguyen, 2020, NeurIPS.

DPVis: Visual Analytics With Hidden Markov Models for Disease Progression Pathways

Zhaonan Sun, Kenney Ng, Vibha Anand, 2019, IEEE Transactions on Visualization and Computer Graphics.

Identifying Patterns of ALS Progression from Sparse Longitudinal Data

Kristen A. Severson, K. Sachs, E. Fraenkel, 2021, medRxiv.

Nonparametric Clustering with Distance Dependent Hierarchies

Soumya Ghosh, Leonid Sigal, Erik B. Sudderth, 2014, UAI.

Model Selection in Bayesian Neural Networks via Horseshoe Priors

Soumya Ghosh, Finale Doshi-Velez, Jiayu Yao, 2017, J. Mach. Learn. Res..

Clinical Trials.Gov: A Topical Analyses

Soumya Ghosh, Vibha Anand, Amos Cahan, 2017, CRI.

Topic model methods for automatically identifying out-of-scope resources

Soumya Ghosh, James H. Martin, Steven Bethard, 2009, JCDL '09.

Quality of Uncertainty Quantification for Bayesian Neural Network Inference pdf

Soumya Ghosh, Finale Doshi-Velez, Weiwei Pan, 2019, ArXiv.

Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors

Finale Doshi-Velez, Soumya Ghosh, Jiayu Yao, 2018, ICML.

EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders pdf

Jimeng Sun, Bradley Malin, Soumya Ghosh, 2020, MLHC.

Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals pdf

Soumya Ghosh, Prasanna Sattigeri, Jiri Navratil, 2021, ArXiv.

Automatic Annotation of Planetary Surfaces With Geomorphic Labels

Soumya Ghosh, Ricardo Vilalta, Tomasz F. Stepinski, 2010, IEEE Transactions on Geoscience and Remote Sensing.

Discovery of Parkinson's disease states and disease progression modelling: a longitudinal data study using machine learning.

Kristen A. Severson, Kenney Ng, Jianying Hu, 2021, The Lancet. Digital health.

Personalized Input-Output Hidden Markov Models for Disease Progression Modeling

Kristen A. Severson, K. A. Severson, L. M. Chahine, 2020, medRxiv.

Unsupervised learning with contrastive latent variable models

Soumya Ghosh, Kenney Ng, Kristen A. Severson, 2018, AAAI.

A probabilistic disease progression modeling approach and its application to integrated Huntington’s disease observational data

Ying Li, Yu Cheng, Soumya Ghosh, 2019, JAMIA open.

Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting

Kush R. Varshney, Pierre L. Dognin, P. Sattigeri, 2022, NeurIPS.

Measuring the robustness of Gaussian processes to kernel choice

William T. Stephenson, Tin D. Nguyen, Sameer K. Deshpande, 2021, AISTATS.

Early Prediction of Diabetes Complications from Electronic Health Records: A Multi-Task Survival Analysis Approach

Ying Li, Bin Liu, Soumya Ghosh, 2018, AAAI.

Complication Risk Profiling in Diabetes Care: A Bayesian Multi-Task and Feature Relationship Learning Approach

Ying Li, Bin Liu, Soumya Ghosh, 2020, IEEE Transactions on Knowledge and Data Engineering.

A Machine‐Learning Derived Huntington's Disease Progression Model: Insights for Clinical Trial Design

Jianying Hu, S. Ghosh, A. Mohan, 2021, Movement disorders : official journal of the Movement Disorder Society.

Approximate Bayesian Computation for Distance-Dependent Learning

Erik B. Sudderth, S. Ghosh, 2015 .

Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI pdf

Kush R. Varshney, Karthikeyan Natesan Ramamurthy, Soumya Ghosh, 2021, ArXiv.

Reliable Gradient-free and Likelihood-free Prompt Tuning

P. Sattigeri, G. Wornell, S. Ghosh, 2023, FINDINGS.

Assumed Density Filtering Methods for Learning Bayesian Neural Networks

Soumya Ghosh, Jonathan S. Yedidia, Francesco Maria Delle Fave, 2016, AAAI.

Latent Projection BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights pdf

Finale Doshi-Velez, Weiwei Pan, Soumya Ghosh, 2018, ArXiv.

Post-hoc loss-calibration for Bayesian neural networks

Soumya Ghosh, Kenney Ng, Benjamin M. Marlin, 2021, UAI.

Deep State Space Models for Computational Phenotyping

Yu Cheng, Soumya Ghosh, Zhaonan Sun, 2016, 2016 IEEE International Conference on Healthcare Informatics (ICHI).

Hierarchical Bayesian Neural Networks for Personalized Classification

Margrit Betke, H. Pfister, S. Ghosh, 2016 .

A general framework for reconciling multiple weak segmentations of an image

Soumya Ghosh, Jane Mulligan, Joseph J. Pfeiffer, 2009, 2009 Workshop on Applications of Computer Vision (WACV).

Image Understanding in a Nonparametric Bayesian Framework

S. Ghosh, 2011 .

A Non-negative Matrix Factorization Framework for Combining Multiple Image Segmentations ; CU-CS-1050-08

Soumya Ghosh, Joseph J. Pfeiffer, Joseph J. Pfeiffer, 2008 .

Model Fusion with Kullback-Leibler Divergence pdf

Justin Solomon, Soumya Ghosh, Mikhail Yurochkin, 2020, ICML.

An Exploration of Latent Structure in Observational Huntington’s Disease Studies

Ying Li, Yu Cheng, Soumya Ghosh, 2017, CRI.

Measuring the sensitivity of Gaussian processes to kernel choice pdf

Soumya Ghosh, William T. Stephenson, Tin D. Nguyen, 2021, ArXiv.

Chi-square Information for Invariant Learning

Samuel C. Hoffman, P. Sattigeri, S. Ghosh, 2020 .

Exploring Factors that Associated with Missing Values in Observational Huntington's Disease Study Data

Ying Li, Yu Cheng, Soumya Ghosh, 2017, Summit on Clinical Research Informatics.

Clinical Trials.Gov: A Topical Analyses

S. Ghosh, V. Anand, A. Cahan, 2017, CRI.

An Exploration of Latent Structure in Observational Huntington’s Disease Studies

Yu Cheng, Jianying Hu, Y. Li, 2017, CRI.

Complication Risk Profiling in Diabetes Care: A Bayesian Multi-Task and Feature Relationship Learning Approach pdf

Ying Li, Bin Liu, Soumya Ghosh, 2018, IEEE Transactions on Knowledge and Data Engineering.