Articles with public access mandates - Eric NalisnickLearn more
Available somewhere: 20
Stick-breaking variational autoencoders
E Nalisnick, P Smyth
arXiv preprint arXiv:1605.06197, 2016
Mandates: US National Science Foundation
Bayesian deep learning via subnetwork inference
E Daxberger, E Nalisnick, JU Allingham, J Antorán, ...
International Conference on Machine Learning, 2510-2521, 2021
Mandates: UK Engineering and Physical Sciences Research Council
Do bayesian neural networks need to be fully stochastic?
M Sharma, S Farquhar, E Nalisnick, T Rainforth
International Conference on Artificial Intelligence and Statistics, 7694-7722, 2023
Mandates: UK Engineering and Physical Sciences Research Council
Calibrated learning to defer with one-vs-all classifiers
R Verma, E Nalisnick
International Conference on Machine Learning, 22184-22202, 2022
Mandates: Netherlands Organisation for Scientific Research
Learning to defer to multiple experts: Consistent surrogate losses, confidence calibration, and conformal ensembles
R Verma, D Barrejón, E Nalisnick
International Conference on Artificial Intelligence and Statistics, 11415-11434, 2023
Mandates: Netherlands Organisation for Scientific Research, European Commission …
Adapting the linearised laplace model evidence for modern deep learning
J Antorán, D Janz, JU Allingham, E Daxberger, RR Barbano, E Nalisnick, ...
International Conference on Machine Learning, 796-821, 2022
Mandates: UK Engineering and Physical Sciences Research Council
Hate speech criteria: A modular approach to task-specific hate speech definitions
U Khurana, I Vermeulen, E Nalisnick, M Van Noorloos, A Fokkens
Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH), 176-191, 2022
Mandates: Netherlands Organisation for Scientific Research
Learning priors for invariance
E Nalisnick, P Smyth
International Conference on Artificial Intelligence and Statistics, 366-375, 2018
Mandates: US National Science Foundation, US National Institutes of Health
Learning approximately objective priors
E Nalisnick, P Smyth
arXiv preprint arXiv:1704.01168, 2017
Mandates: US National Science Foundation, US National Institutes of Health
On the impact of non-IID data on the performance and fairness of differentially private federated learning
S Amiri, A Belloum, E Nalisnick, S Klous, L Gommans
2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems …, 2022
Mandates: Netherlands Organisation for Scientific Research
Linearised laplace inference in networks with normalisation layers and the neural g-prior
J Antorán, JU Allingham, D Janz, E Daxberger, E Nalisnick, ...
Fourth Symposium on Advances in Approximate Bayesian Inference, 2022
Mandates: UK Engineering and Physical Sciences Research Council
Towards anytime classification in early-exit architectures by enforcing conditional monotonicity
M Jazbec, J Allingham, D Zhang, E Nalisnick
Advances in Neural Information Processing Systems 36, 2024
Mandates: UK Engineering and Physical Sciences Research Council
Analyzing NIH funding patterns over time with statistical text analysis
J Park, M Blume-Kohout, R Krestel, E Nalisnick, P Smyth
Workshops at the Thirtieth AAAI Conference on Artificial Intelligence, 2016
Mandates: US National Science Foundation, US National Institutes of Health
Bayesian trees for automated cytometry data analysis
D Ji, E Nalisnick, Y Qian, RH Scheuermann, P Smyth
Machine Learning for Healthcare Conference, 465-483, 2018
Mandates: US National Science Foundation, US National Institutes of Health
Learning to Defer to a Population: A Meta-Learning Approach
D Tailor, A Patra, R Verma, P Manggala, E Nalisnick
International Conference on Artificial Intelligence and Statistics, 3475-3483, 2024
Mandates: Netherlands Organisation for Scientific Research
A brief tour of deep learning from a statistical perspective
E Nalisnick, P Smyth, D Tran
Annual Review of Statistics and Its Application 10 (1), 219-246, 2023
Mandates: US National Science Foundation
On the calibration of learning to defer to multiple experts
R Verma, D Barrejón, E Nalisnick
Workshop on Human-Machine Collaboration and Teaming in International Confere …, 2022
Mandates: Netherlands Organisation for Scientific Research
Generating heavy-tailed synthetic data with normalizing flows
S Amiri, E Nalisnick, A Belloum, S Klous, L Gommans
The 5th Workshop on Tractable Probabilistic Modeling, 2022
Mandates: Netherlands Organisation for Scientific Research
Learning generative models with invariance to symmetries
JU Allingham, J Antoran, S Padhy, E Nalisnick, JM Hernández-Lobato
NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, 2022
Mandates: UK Engineering and Physical Sciences Research Council
A Product of Experts Approach to Early-Exit Ensembles
JU Allingham, E Nalisnick
Technical report, 2022
Mandates: UK Engineering and Physical Sciences Research Council
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