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Author search results
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- Neil D. Lawrence
aka: Neil David Lawrence
University of Cambridge, UK
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Publication search results
found 206 matches
- 2024
- Andrei Paleyes
, Han-Bo Li
, Neil D. Lawrence
:
Can causality accelerate experimentation in software systems? CAIN 2024: 280-281 - Christian Cabrera, Andrei Paleyes, Neil D. Lawrence:
Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation. CoRR abs/2401.11370 (2024) - Sarah Zhao, Aditya Ravuri, Vidhi Lalchand, Neil D. Lawrence:
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data. CoRR abs/2405.03879 (2024) - Diana Robinson, Christian Cabrera, Andrew D. Gordon, Neil D. Lawrence, Lars Mennen:
Requirements are All You Need: The Final Frontier for End-User Software Engineering. CoRR abs/2405.13708 (2024) - Aditya Ravuri, Neil D. Lawrence:
Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE. CoRR abs/2405.17412 (2024) - Aditya Ravuri, Jen Muir, Neil D. Lawrence:
On Feature Learning for Titi Monkey Activity Detection. CoRR abs/2407.01452 (2024) - 2023
- Andrei Paleyes
, Raoul-Gabriel Urma
, Neil D. Lawrence
:
Challenges in Deploying Machine Learning: A Survey of Case Studies. ACM Comput. Surv. 55(6): 114:1-114:29 (2023) - Francisco Vargas
, Pierre Thodoroff
, Austen Lamacraft
, Neil D. Lawrence
:
Correction: Vargas et al. Solving Schrödinger Bridges via Maximum Likelihood. Entropy 2021, 23, 1134. Entropy 25(2): 289 (2023) - Francisco Vargas
, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken:
Bayesian learning via neural Schrödinger-Föllmer flows. Stat. Comput. 33(1): 3 (2023) - Andrei Paleyes
, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence:
Dataflow graphs as complete causal graphs. CAIN 2023: 7-12 - Andrei Paleyes
, Neil David Lawrence
:
Causal fault localisation in dataflow systems. EuroMLSys@EuroSys 2023: 140-147 - Christian Cabrera, Andrei Paleyes, Pierre Thodoroff, Neil D. Lawrence:
Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective. CoRR abs/2302.04810 (2023) - Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery:
AI for Science: An Emerging Agenda. CoRR abs/2303.04217 (2023) - Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence:
Dataflow graphs as complete causal graphs. CoRR abs/2303.09552 (2023) - Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D. Lawrence:
Dimensionality Reduction as Probabilistic Inference. CoRR abs/2304.07658 (2023) - Andrei Paleyes, Neil D. Lawrence:
Causal fault localisation in dataflow systems. CoRR abs/2304.11987 (2023) - Bogdan Ficiu, Neil D. Lawrence, Andrei Paleyes:
Automated discovery of trade-off between utility, privacy and fairness in machine learning models. CoRR abs/2311.15691 (2023) - 2022
- Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence:
Generalised GPLVM with Stochastic Variational Inference. AISTATS 2022: 7841-7864 - Sijia Li, Martín López-García, Neil D. Lawrence, Luisa Cutillo:
Two-way Sparse Network Inference for Count Data. AISTATS 2022: 10924-10938 - Andrei Paleyes
, Christian Cabrera, Neil D. Lawrence:
An empirical evaluation of flow based programming in the machine learning deployment context. CAIN 2022: 54-64 - Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence:
Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs. MLCB 2022: 46-60 - Samuel J. Bell, Onno Kampman, Jesse Dodge, Neil D. Lawrence:
Modeling the Machine Learning Multiverse. NeurIPS 2022 - Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence:
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference. CoRR abs/2202.12979 (2022) - Sijia Li, Martín López-García, Neil D. Lawrence, Luisa Cutillo:
Scalable Bigraphical Lasso: Two-way Sparse Network Inference for Count Data. CoRR abs/2203.07912 (2022) - Andrei Paleyes, Christian Cabrera, Neil D. Lawrence:
An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context. CoRR abs/2204.12781 (2022) - Samuel J. Bell, Neil D. Lawrence:
The Effect of Task Ordering in Continual Learning. CoRR abs/2205.13323 (2022) - Samuel J. Bell, Onno Pepijn Kampman
, Jesse Dodge, Neil D. Lawrence:
Modeling the Machine Learning Multiverse. CoRR abs/2206.05985 (2022) - Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence:
Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs. CoRR abs/2209.06716 (2022) - Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite, Will Tebbutt, Richard E. Turner, J. Scott Hosking, Neil D. Lawrence, Markus Kaiser:
Ice Core Dating using Probabilistic Programming. CoRR abs/2210.16568 (2022) - Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery:
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382). Dagstuhl Reports 12(9): 150-199 (2022)
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