18 citations · 32 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
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…
Bregman Divergence Bounds and Universality Properties of the Logarithmic Loss
Amichai Painsky, Gregory W. Wornell
A loss function measures the discrepancy between the true values and their estimated fits, for a given instance of data. In classification problems, a loss function is said to be p…
On the Universality of the Logistic Loss Function
Amichai Painsky, Gregory W. Wornell
A loss function measures the discrepancy between the true values (observations) and their estimated fits, for a given instance of data. A loss function is said to be proper (unbias…
Generalized Independent Component Analysis Over Finite Alphabets
Amichai Painsky, Saharon Rosset, Meir Feder
Independent component analysis (ICA) is a statistical method for transforming an observable multidimensional random vector into components that are as statistically independent as…