Search Results for author:
Found 16 papers, 2 papers with code
Distribution Estimation under the Infinity Norm
A variety of techniques are utilized and innovated upon, including Chernoff-type inequalities and empirical Bernstein bounds.
Optimal detection of non-overlapping images via combinatorial auction
This paper studies the classical problem of detecting the location of multiple image occurrences in a two-dimensional, noisy measurement.
Confidence Intervals for Unobserved Events
Unobserved events are alphabet symbols which do not appear in the sample.
K-sample Multiple Hypothesis Testing for Signal Detection
This paper studies the classical problem of estimating the locations of signal occurrences in a noisy measurement.
Detecting non-overlapping signals with dynamic programming
This paper studies the classical problem of detecting the locations of signal occurrences in a one-dimensional noisy measurement.
Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection
The effect of this bias was extensively studied over the years, mostly in terms of predictive performance.
Neural Joint Entropy Estimation
Estimating the entropy of a discrete random variable is a fundamental problem in information theory and related fields.
REPRESENTATION COMPRESSION AND GENERALIZATION IN DEEP NEURAL NETWORKS
Specifically, we show that the training of the network is characterized by a rapid increase in the mutual information (MI) between the layers and the target label, followed by a longer decrease in the MI between the layers and the input variable.
Non-linear Canonical Correlation Analysis: A Compressed Representation Approach
In this work we introduce an information-theoretic compressed representation framework for the non-linear CCA problem (CRCCA), which extends the classical ACE approach.
Lossless (and Lossy) Compression of Random Forests
In addition, we introduce a theoretically sound lossy compression scheme, which allows us to control the trade-off between the distortion and the coding rate.
Linear Independent Component Analysis over Finite Fields: Algorithms and Bounds
Importantly, we show that the overhead of our suggested algorithm (compared with the lower bound) typically decreases, as the scale of the problem grows.
PhD Dissertation: Generalized Independent Components Analysis Over Finite Alphabets
Independent component analysis (ICA) is a statistical method for transforming an observable multi-dimensional random vector into components that are as statistically independent as possible from each other.
MSc Dissertation: Exclusive Row Biclustering for Gene Expression Using a Combinatorial Auction Approach
In this paper we focus on the exclusive row biclustering problem for gene expression data sets, in which each row can only be a member of a single bicluster while columns can participate in multiple ones.
Outperforming Good-Turing: Preliminary Report
Estimating a large alphabet probability distribution from a limited number of samples is a fundamental problem in machine learning and statistics.
Gaussian Lower Bound for the Information Bottleneck Limit
In this work we introduce a Gaussian lower bound to the IB curve; we find an embedding of the data which maximizes its "Gaussian part", on which we apply the GIB.
Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance
The most important consequence of our approach is that categorical variables with many categories can be safely used in tree building and are only chosen if they contribute to predictive power.
