David A. Knowles

Orcid: 0000-0002-7408-146X

Affiliations:
  • New York Genome Center, New York City, NY, USA
  • Columbia University, New York City, NY, USA
  • Stanford University, Department of Computer Science, Stanford, CA, USA
  • University of Cambridge, Department of Engineering , UK (PhD)


According to our database1, David A. Knowles authored at least 30 papers between 2007 and 2024.

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

Timeline

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Bibliography

2024
The VampPrior Mixture Model.
CoRR, 2024

2023
LDmat: efficiently queryable compression of linkage disequilibrium matrices.
Bioinform., February, 2023

System Identification for Continuous-time Linear Dynamical Systems.
CoRR, 2023

Vector Embeddings by Sequence Similarity and Context for Improved Compression, Similarity Search, Clustering, Organization, and Manipulation of cDNA Libraries.
CoRR, 2023

Faithful Heteroscedastic Regression with Neural Networks.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Deep mendelian randomization: Investigating the causal knowledge of genomic deep learning models.
PLoS Comput. Biol., October, 2022

Faithful Heteroscedastic Regression with Neural Networks.
CoRR, 2022

2020
Active Learning in CNNs via Expected Improvement Maximization.
CoRR, 2020

Variational Variance: Simple and Reliable Predictive Variance Parameterization.
CoRR, 2020

2019
Sparse discriminative latent characteristics for predicting cancer drug sensitivity from genomic features.
PLoS Comput. Biol., 2019

A New Distribution on the Simplex with Auto-Encoding Applications.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2017
A Birth-Death Process for Feature Allocation.
Proceedings of the 34th International Conference on Machine Learning, 2017

2015
Relational Learning and Network Modelling Using Infinite Latent Attribute Models.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Pitman Yor Diffusion Trees for Bayesian Hierarchical Clustering.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process.
CoRR, 2015

Using contextual information to classify nuclei in histology images.
Proceedings of the 12th IEEE International Symposium on Biomedical Imaging, 2015

An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
A reversible infinite HMM using normalised random measures.
Proceedings of the 31th International Conference on Machine Learning, 2014

Beta Diffusion Trees.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

2012
Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression
CoRR, 2012

A nonparametric variable clustering model.
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

Gaussian Process Regression Networks.
Proceedings of the 29th International Conference on Machine Learning, 2012

An Infinite Latent Attribute Model for Network Data.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Pitman-Yor Diffusion Trees.
Proceedings of the UAI 2011, 2011

Non-conjugate Variational Message Passing for Multinomial and Binary Regression.
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

Message Passing Algorithms for the Dirichlet Diffusion Tree.
Proceedings of the 28th International Conference on Machine Learning, 2011

2010
Nonparametric Bayesian Sparse Factor Models with application to Gene Expression modelling
CoRR, 2010

2009
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2007
Infinite Sparse Factor Analysis and Infinite Independent Components Analysis.
Proceedings of the Independent Component Analysis and Signal Separation, 2007


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