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Found 4 papers, 3 papers with code
Joint self-supervised blind denoising and noise estimation
Assuming that the noisy observations are independent conditionally to the signal, the networks can be jointly trained without clean training data.
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
We introduce a new general identifiable framework for principled disentanglement referred to as Structured Nonlinear Independent Component Analysis (SNICA).
Pair-Matching: Links Prediction with Adaptive Queries
This paper shows that sublinear regret is achievable in the case where the graph is generated according to a Stochastic Block Model (SBM) with two communities.
On the convergence of the MLE as an estimator of the learning rate in the Exp3 algorithm
When fitting the learning data of an individual to algorithm-like learning models, the observations are so dependent and non-stationary that one may wonder what the classical Maximum Likelihood Estimator (MLE) could do, even if it is the usual tool applied to experimental cognition.
