activity
20172022
most citedGaussian Lower Bound for the Information Bottleneck Limit

5 citations · 6 across the 5 of their papers we have counts for

collaborators

15 papers

math.ST20221 cited

Confidence Intervals for Unobserved Events

Amichai Painsky

Consider a finite sample from an unknown distribution over a countable alphabet. Unobserved events are alphabet symbols which do not appear in the sample. Estimating the probabilit…

eess.SP2022

K-sample Multiple Hypothesis Testing for Signal Detection

Uriel Shiterburd, Tamir Bendory, Amichai Painsky

This paper studies the classical problem of estimating the locations of signal occurrences in a noisy measurement. Based on a multiple hypothesis testing scheme, we design a K-samp…

cs.LG2021

Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection

Afek Ilay Adler, Amichai Painsky

Gradient Boosting Machines (GBM) are among the go-to algorithms on tabular data, which produce state of the art results in many prediction tasks. Despite its popularity, the GBM fr…

cs.IT2020

Neural Joint Entropy Estimation

Yuval Shalev, Amichai Painsky, Irad Ben-Gal

Estimating the entropy of a discrete random variable is a fundamental problem in information theory and related fields. This problem has many applications in various domains, inclu…

cs.IT2018

Innovation Representation of Stochastic Processes with Application to Causal Inference

Amichai Painsky, Saharon Rosset, Meir Feder

Typically, real-world stochastic processes are not easy to analyze. In this work we study the representation of any stochastic process as a memoryless innovation process triggering…

cs.LG2018

Lossless (and Lossy) Compression of Random Forests

Amichai Painsky, Saharon Rosset

Ensemble methods are among the state-of-the-art predictive modeling approaches. Applied to modern big data, these methods often require a large number of sub-learners, where the co…