Infinite Sparse Factor Analysis and Infinite Independent Components Analysis. - David A. Knowles; Zoubin Ghahramani (ICA, 2007)

Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process. - Finale Doshi-Velez; David A. Knowles; Shakir Mohamed; Zoubin Ghahramani (NIPS, 2009)

Nonparametric Bayesian Sparse Factor Models with application to Gene Expression modelling - David A. Knowles; Zoubin Ghahramani (CoRR, 2010)

Message Passing Algorithms for the Dirichlet Diffusion Tree. - David A. Knowles; Jurgen Van Gael; Zoubin Ghahramani (ICML, 2011)

Non-conjugate Variational Message Passing for Multinomial and Binary Regression. - David A. Knowles; Tom Minka (NIPS, 2011)

Pitman-Yor Diffusion Trees. - David A. Knowles; Zoubin Ghahramani (UAI, 2011)

An Infinite Latent Attribute Model for Network Data. - Konstantina Palla; David A. Knowles; Zoubin Ghahramani (ICML, 2012)

Gaussian Process Regression Networks. - Andrew Gordon Wilson; David A. Knowles; Zoubin Ghahramani (ICML, 2012)

A nonparametric variable clustering model. - David A. Knowles; Konstantina Palla; Zoubin Ghahramani (NIPS, 2012)

Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression - Tim Salimans; David A. Knowles (CoRR, 2012)

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models. - Novi Quadrianto; Viktoriia Sharmanska; David A. Knowles; Zoubin Ghahramani (UAI, 2013)

Beta Diffusion Trees. - Creighton Heaukulani; David A. Knowles; Zoubin Ghahramani (ICML, 2014)

A reversible infinite HMM using normalised random measures. - David A. Knowles; Zoubin Ghahramani; Konstantina Palla (ICML, 2014)

An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process. - Amar Shah 001; David A. Knowles; Zoubin Ghahramani (ICML, 2015)

Using contextual information to classify nuclei in histology images. - Kien Nguyen 005; Jörg Bredno; David A. Knowles (ISBI, 2015)

An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process. - Amar Shah 001; David A. Knowles; Zoubin Ghahramani (CoRR, 2015)

Pitman Yor Diffusion Trees for Bayesian Hierarchical Clustering. - David A. Knowles; Zoubin Ghahramani (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Relational Learning and Network Modelling Using Infinite Latent Attribute Models. - Konstantina Palla; David A. Knowles; Zoubin Ghahramani (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

A Birth-Death Process for Feature Allocation. - Konstantina Palla; David A. Knowles; Zoubin Ghahramani (ICML, 2017)

A New Distribution on the Simplex with Auto-Encoding Applications. - Andrew Stirn; Tony Jebara; David A. Knowles (NeurIPS, 2019)

Sparse discriminative latent characteristics for predicting cancer drug sensitivity from genomic features. - David A. Knowles; Gina Bouchard; Sylvia K. Plevritis (PLoS Comput. Biol., 2019)

Variational Variance: Simple and Reliable Predictive Variance Parameterization. - Andrew Stirn; David A. Knowles (CoRR, 2020)

Active Learning in CNNs via Expected Improvement Maximization. - Udai G. Nagpal; David A. Knowles (CoRR, 2020)

Faithful Heteroscedastic Regression with Neural Networks. - Andrew Stirn; Hans-Hermann Wessels; Megan Schertzer; Laura Pereira; Neville E. Sanjana; David A. Knowles (CoRR, 2022)

Deep mendelian randomization: Investigating the causal knowledge of genomic deep learning models. - Stephen Malina; Daniel Cizin; David A. Knowles (PLoS Comput. Biol., 2022)

Faithful Heteroscedastic Regression with Neural Networks. - Andrew Stirn; Harm Wessels; Megan Schertzer; Laura Pereira; Neville E. Sanjana; David A. Knowles (AISTATS, 2023)

Investigating RNA splicing as a source of cellular diversity using a binomial mixture model. - Keren Isaev; David A. Knowles (MLCB, 2023)

System Identification for Continuous-time Linear Dynamical Systems. - Peter Halmos; Jonathan W. Pillow; David A. Knowles (CoRR, 2023)

LDmat: efficiently queryable compression of linkage disequilibrium matrices. - Rockwell J. Weiner; Chirag M. Lakhani; David A. Knowles; Gamze Gürsoy (Bioinform., 2023)

Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis. - Jiayu Su; David A. Knowles; Raúl Rabadán (NeurIPS, 2024)

DIISCO: A Bayesian Framework for Inferring Dynamic Intercellular Interactions from Time-Series Single-Cell Data. - Cameron Park; Shouvik Mani; Nicolas Beltran-Velez; Katie Maurer; Satyen Gohil; Shuqiang Li; Teddy Huang; David A. Knowles; Catherine J. Wu; Elham Azizi (RECOMB, 2024)

The VampPrior Mixture Model. - Andrew Stirn; David A. Knowles (AISTATS, 2025)

Vector embeddings by sequence similarity and context for improved compression, similarity search, clustering, organization, and manipulation of cDNA libraries. - Daniel H. Um; David A. Knowles; Gail E. Kaiser (Comput. Biol. Chem., 2025)

Interpretable Neural ODEs for Gene Regulatory Network Discovery under Perturbations. - Zaikang Lin; Sei Chang; Aaron Zweig; Minseo Kang; Elham Azizi; David A. Knowles (CoRR, 2025)

Towards Identifiability of Interventional Stochastic Differential Equations. - Aaron Zweig; Zaikang Lin; Elham Azizi; David A. Knowles (CoRR, 2025)