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Found 6 papers, 3 papers with code
An Interpretable Evaluation of Entropy-based Novelty of Generative Models
The massive developments of generative model frameworks and architectures require principled methods for the evaluation of a model's novelty compared to a reference dataset or baseline generative models.
Compression with Exact Error Distribution for Federated Learning
Compression schemes have been extensively used in Federated Learning (FL) to reduce the communication cost of distributed learning.
An Automated Theorem Proving Framework for Information-Theoretic Results
We present a versatile automated theorem proving framework capable of automated discovery, simplification and proofs of inner and outer bounds in network information theory, deduction of properties of information-theoretic quantities (e. g. Wyner and G\'acs-K\"orner common information), and discovery of non-Shannon-type inequalities, under a unified framework.
Automated Theorem Proving
Information Theory
Information Theory
Infinite Divisibility of Information
sequence of random variables $Z_{1},\ldots, Z_{n}$ that contains the same information as $X$, i. e., there exists an injective function $f$ such that $X=f(Z_{1},\ldots, Z_{n})$.
Information Theory Information Theory Probability 94A15, 60F05
Efficient Approximate Minimum Entropy Coupling of Multiple Probability Distributions
More precisely, we construct a coupling with entropy within 2 bits from the entropy of the greatest lower bound of $p_{1},\ldots, p_{m}$ with respect to majorization.
Information Theory Information Theory Probability
Locally Weighted Learning for Naive Bayes Classifier
We learn from this phenomenon that when the size of the training data is large, we should either relax the assumption or apply NB to a "reduced" data set, say for example use NB as a local model.
