22 citations · 38 across the 8 of their papers we have counts for
6 papers · 1 filter
Statistical Estimation from Dependent Data
Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala +2
We consider a general statistical estimation problem wherein binary labels across different observations are not independent conditioned on their feature vectors, but dependent, ca…
For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash Functions
Nishanth Dikkala, Gal Kaplun, Rina Panigrahy
It is well established that training deep neural networks gives useful representations that capture essential features of the inputs. However, these representations are poorly unde…
Learning from weakly dependent data under Dobrushin's condition
Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala +1
Statistical learning theory has largely focused on learning and generalization given independent and identically distributed (i.i.d.) samples. Motivated by applications involving t…
Regression from Dependent Observations
Constantinos Daskalakis, Nishanth Dikkala, Ioannis Panageas
The standard linear and logistic regression models assume that the response variables are independent, but share the same linear relationship to their corresponding vectors of cova…
HOGWILD!-Gibbs can be PanAccurate
Constantinos Daskalakis, Nishanth Dikkala, Siddhartha Jayanti
Asynchronous Gibbs sampling has been recently shown to be fast-mixing and an accurate method for estimating probabilities of events on a small number of variables of a graphical mo…
From Soft Classifiers to Hard Decisions: How fair can we be?
Ran Canetti, Aloni Cohen, Nishanth Dikkala +3
A popular methodology for building binary decision-making classifiers in the presence of imperfect information is to first construct a non-binary "scoring" classifier that is calib…