Skip to search formSkip to main contentSkip to account menu

Minimax Estimation of Functionals of Discrete Distributions

The minimax rate-optimal mutual information estimator yielded by the framework leads to significant performance boosts over the Chow-Liu algorithm in learning graphical models and the practical advantages of the schemes for the estimation of entropy and mutual information.
View on IEEE (opens in a new tab)

Maximum Likelihood Estimation of Functionals of Discrete Distributions

The worst case squared error risk incurred by the maximum likelihood estimator (MLE) in estimating the Shannon entropy is described and it is established that the MLE achieves the minimax optimal rate regardless of the alphabet size.
View on IEEE (opens in a new tab)

Justification of Logarithmic Loss via the Benefit of Side Information

This work provides a new characterization of mutual information, and justifies its use as a measure of relevance, and extends the results naturally extend to measuring the causal influence between stochastic processes.
View on IEEE (opens in a new tab)

Information Measures: The Curious Case of the Binary Alphabet

It is shown that f-divergences are not the unique decomposable divergences on binary alphabets that satisfy the data processing inequality, thereby clarifying claims that have previously appeared in the literature.
View on IEEE (opens in a new tab)

Pointwise Relations Between Information and Estimation in Gaussian Noise

The pointwise relations that are found are as succinctly stated as-and give considerable insight into-the original expectation identities of random quantities.
View on IEEE (opens in a new tab)

Order-Optimal Estimation of Functionals of Discrete Distributions

CaMoDi: a new method for cancer module discovery

A robust and fast algorithm to discover cancer driver genes using an unsupervised clustering of similarly expressed genes across cancer patients is developed, and a new method for module discovery is introduced which demonstrates superior performance across a number of computational and statistical metrics.
View on Springer (opens in a new tab)

Reference based genome compression

An algorithm to compress a target genome given a known reference genome, which first generates a mapping from the reference to the target genome, and then compresses this mapping with an entropy coder.
View on IEEE (opens in a new tab)

Mutual information, relative entropy and estimation error in semi-martingale channels

It is demonstrated that fundamental relations between information and estimation hold for a much larger family of continuous-time channels, including as a special case the Gaussian and Poisson models, and new representations relating the mutual information between the channel input and output to an optimal causal filtering loss are established.
View on IEEE (opens in a new tab)

Relations Between Information and Estimation in Discrete-Time Lévy Channels

This paper introduces the natural family of discrete-time Lévy channels where the distribution of the output conditioned on the input is infinitely divisible and establishes new representations relating the mutual information between the channel input and output to an optimal expected estimation loss.
View on IEEE (opens in a new tab)
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)