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Found 11 papers, 5 papers with code
Improved learning rates in multi-unit uniform price auctions
Indeed, we prove that a learning algorithm leveraging the structure of this problem achieves a regret of $\tilde{O}(K^{4/3}T^{2/3})$ under bandit feedback, improving over the bound of $\tilde{O}(K^{7/4}T^{3/4})$ previously obtained in the literature.
Distribution-Aware Mean Estimation under User-level Local Differential Privacy
We consider the problem of mean estimation under user-level local differential privacy, where $n$ users are contributing through their local pool of data samples.
Constant or logarithmic regret in asynchronous multiplayer bandits
This motivates the harder, asynchronous multiplayer bandits problem, which was first tackled with an explore-then-commit (ETC) algorithm (see Dakdouk, 2022), with a regret upper-bound in $\mathcal{O}(T^{\frac{2}{3}})$.
On Preemption and Learning in Stochastic Scheduling
We study single-machine scheduling of jobs, each belonging to a job type that determines its duration distribution.
Shared Independent Component Analysis for Multi-Subject Neuroimaging
While ShICA-J is based on second-order statistics, we further propose to leverage non-Gaussianity of the components using a maximum-likelihood method, ShICA-ML, that is both more accurate and more costly.
Functional Magnetic Resonance Imaging data augmentation through conditional ICA
Advances in computational cognitive neuroimaging research are related to the availability of large amounts of labeled brain imaging data, but such data are scarce and expensive to generate.
Adaptive Multi-View ICA: Estimation of noise levels for optimal inference
By contrast, we propose Adaptive multiView ICA (AVICA), a noisy ICA model where each view is a linear mixture of shared independent sources with additive noise on the sources.
Modeling Shared Responses in Neuroimaging Studies through MultiView ICA
Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization.
mvlearn: Multiview Machine Learning in Python
As data are generated more and more from multiple disparate sources, multiview data sets, where each sample has features in distinct views, have ballooned in recent years.
Fast shared response model for fMRI data
The shared response model provides a simple but effective framework to analyse fMRI data of subjects exposed to naturalistic stimuli.
Optimizing deep video representation to match brain activity
The comparison of observed brain activity with the statistics generated by artificial intelligence systems is useful to probe brain functional organization under ecological conditions.
