David A. Knowles

47 papers A* 9A 4B 1Journal 22Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Learning Lineage-guided Geodesics with Finsler Geometry. CoRR Aaron Zweig, Mingxuan Zhang, David A. Knowles, Elham Azizi
2025	J	`jnl`	Energy Guided Geometric Flow Matching. CoRR Aaron Zweig, Mingxuan Zhang, Elham Azizi, David A. Knowles
2025	J	`jnl`	Interpretable Neural ODEs for Gene Regulatory Network Discovery under Perturbations. CoRR Zaikang Lin, Sei Chang, Aaron Zweig, Minseo Kang, Elham Azizi, David A. Knowles
2025	—	`ed.`	Proceedings of the 20th Machine Learning in Computational Biology (MLCB), 10-11 September 2025, New York, NY, USA MLCB David A. Knowles, Peter K. Koo
2025	A	`conf`	The VampPrior Mixture Model. AISTATS Andrew Stirn, David A. Knowles
2025	J	`jnl`	Towards Identifiability of Interventional Stochastic Differential Equations. CoRR Aaron Zweig, Zaikang Lin, Elham Azizi, David A. Knowles
2025	J	`jnl`	Vector embeddings by sequence similarity and context for improved compression, similarity search, clustering, organization, and manipulation of cDNA libraries. Comput. Biol. Chem. Daniel H. Um, David A. Knowles, Gail E. Kaiser
2024	B	`conf`	DIISCO: A Bayesian Framework for Inferring Dynamic Intercellular Interactions from Time-Series Single-Cell Data. RECOMB Cameron Park, Shouvik Mani, Nicolas Beltran-Velez, Katie Maurer, Satyen Gohil, Shuqiang Li, Teddy Huang, David A. Knowles, Catherine J. Wu, Elham Azizi
2024	A*	`conf`	Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis. NeurIPS Jiayu Su, David A. Knowles, Raúl Rabadán
2024	J	`jnl`	Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis. CoRR Jiayu Su, David A. Knowles, Raul Rabadan
2024	—	`ed.`	Machine Learning in Computational Biology, 5-6 September 2024, Seattle, WA, USA. MLCB David A. Knowles, Sara Mostafavi
2024	J	`jnl`	The VampPrior Mixture Model. CoRR Andrew Stirn, David A. Knowles
2023	A	`conf`	Faithful Heteroscedastic Regression with Neural Networks. AISTATS Andrew Stirn, Harm Wessels, Megan Schertzer, Laura Pereira, Neville E. Sanjana, David A. Knowles
2023	—	`conf`	Investigating RNA splicing as a source of cellular diversity using a binomial mixture model. MLCB Keren Isaev, David A. Knowles
2023	J	`jnl`	LDmat: efficiently queryable compression of linkage disequilibrium matrices. Bioinform. Rockwell J. Weiner, Chirag M. Lakhani, David A. Knowles, Gamze Gürsoy
2023	—	`ed.`	Machine Learning in Computational Biology, November 30 - December 1, 2023, Seattle, WA, USA. MLCB David A. Knowles, Sara Mostafavi
2023	J	`jnl`	System Identification for Continuous-time Linear Dynamical Systems. CoRR Peter Halmos, Jonathan W. Pillow, David A. Knowles
2023	J	`jnl`	Vector Embeddings by Sequence Similarity and Context for Improved Compression, Similarity Search, Clustering, Organization, and Manipulation of cDNA Libraries. CoRR Daniel H. Um, David A. Knowles, Gail E. Kaiser
2022	J	`jnl`	Deep mendelian randomization: Investigating the causal knowledge of genomic deep learning models. PLoS Comput. Biol. Stephen Malina, Daniel Cizin, David A. Knowles
2022	J	`jnl`	Faithful Heteroscedastic Regression with Neural Networks. CoRR Andrew Stirn, Hans-Hermann Wessels, Megan Schertzer, Laura Pereira, Neville E. Sanjana, David A. Knowles
2022	—	`ed.`	Machine Learning in Computational Biology, 21-22 November 2022, Online MLCB David A. Knowles, Sara Mostafavi, Su-In Lee
2021	—	`ed.`	Machine Learning in Computational Biology Meeting, MLCB 2021, online, November 22-23, 2021. MLCB David A. Knowles, Sara Mostafavi, Su-In Lee
2020	J	`jnl`	Active Learning in CNNs via Expected Improvement Maximization. CoRR Udai G. Nagpal, David A. Knowles
2020	J	`jnl`	Variational Variance: Simple and Reliable Predictive Variance Parameterization. CoRR Andrew Stirn, David A. Knowles
2019	A*	`conf`	A New Distribution on the Simplex with Auto-Encoding Applications. NeurIPS Andrew Stirn, Tony Jebara, David A. Knowles
2019	J	`jnl`	A New Distribution on the Simplex with Auto-Encoding Applications. CoRR Andrew Stirn, Tony Jebara, David A. Knowles
2019	J	`jnl`	Sparse discriminative latent characteristics for predicting cancer drug sensitivity from genomic features. PLoS Comput. Biol. David A. Knowles, Gina Bouchard, Sylvia K. Plevritis
2017	A*	`conf`	A Birth-Death Process for Feature Allocation. ICML Konstantina Palla, David A. Knowles, Zoubin Ghahramani
2015	J	`jnl`	An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process. CoRR Amar Shah, David A. Knowles, Zoubin Ghahramani
2015	A*	`conf`	An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process. ICML Amar Shah, David A. Knowles, Zoubin Ghahramani
2015	J	`jnl`	Pitman Yor Diffusion Trees for Bayesian Hierarchical Clustering. IEEE Trans. Pattern Anal. Mach. Intell. David A. Knowles, Zoubin Ghahramani
2015	J	`jnl`	Relational Learning and Network Modelling Using Infinite Latent Attribute Models. IEEE Trans. Pattern Anal. Mach. Intell. Konstantina Palla, David A. Knowles, Zoubin Ghahramani
2015	—	`conf`	Using contextual information to classify nuclei in histology images. ISBI Kien Nguyen, Jörg Bredno, David A. Knowles
2014	A*	`conf`	A reversible infinite HMM using normalised random measures. ICML David A. Knowles, Zoubin Ghahramani, Konstantina Palla
2014	A*	`conf`	Beta Diffusion Trees. ICML Creighton Heaukulani, David A. Knowles, Zoubin Ghahramani
2013	A	`conf`	The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models. UAI Novi Quadrianto, Viktoriia Sharmanska, David A. Knowles, Zoubin Ghahramani
2013	J	`jnl`	The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models. CoRR Novi Quadrianto, Viktoriia Sharmanska, David A. Knowles, Zoubin Ghahramani
2012	—	`conf`	A nonparametric variable clustering model. NIPS David A. Knowles, Konstantina Palla, Zoubin Ghahramani
2012	A*	`conf`	An Infinite Latent Attribute Model for Network Data. ICML Konstantina Palla, David A. Knowles, Zoubin Ghahramani
2012	J	`jnl`	Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression CoRR Tim Salimans, David A. Knowles
2012	A*	`conf`	Gaussian Process Regression Networks. ICML Andrew Gordon Wilson, David A. Knowles, Zoubin Ghahramani
2011	A*	`conf`	Message Passing Algorithms for the Dirichlet Diffusion Tree. ICML David A. Knowles, Jurgen Van Gael, Zoubin Ghahramani
2011	—	`conf`	Non-conjugate Variational Message Passing for Multinomial and Binary Regression. NIPS David A. Knowles, Tom Minka
2011	A	`conf`	Pitman-Yor Diffusion Trees. UAI David A. Knowles, Zoubin Ghahramani
2010	J	`jnl`	Nonparametric Bayesian Sparse Factor Models with application to Gene Expression modelling CoRR David A. Knowles, Zoubin Ghahramani
2009	—	`conf`	Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process. NIPS Finale Doshi-Velez, David A. Knowles, Shakir Mohamed, Zoubin Ghahramani
2007	—	`conf`	Infinite Sparse Factor Analysis and Infinite Independent Components Analysis. ICA David A. Knowles, Zoubin Ghahramani