activity
20152017
most citedModel-Powered Conditional Independence Test

25 citations · 47 across the 5 of their papers we have counts for

collaborators

9 papers

stat.ML20171 cited

Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition

Shankar Kumar, Michael Nirschl, Daniel Holtmann-Rice +3

Recurrent neural network (RNN) language models (LMs) and Long Short Term Memory (LSTM) LMs, a variant of RNN LMs, have been shown to outperform traditional N-gram LMs on speech rec…

stat.ML201725 cited

Model-Powered Conditional Independence Test

Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam +2

We consider the problem of non-parametric Conditional Independence testing (CI testing) for continuous random variables. Given i.i.d samples from the joint distribution

cs.LG201718 cited

Maximum Selection and Ranking under Noisy Comparisons

Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati +1

We consider -PAC maximum-selection and ranking for general probabilistic models whose comparisons probabilities satisfy strong stochastic transitivity and stochastic triangl…

cs.IT2017

Minimax Risk for Missing Mass Estimation

Nikhilesh Rajaraman, Andrew Thangaraj, Ananda Theertha Suresh

The problem of estimating the missing mass or total probability of unseen elements in a sequence of random samples is considered under the squared error loss function. The wors…

cs.DS2016

Maximum Selection and Sorting with Adversarial Comparators and an Application to Density Estimation

Jayadev Acharya, Moein Falahatgar, Ashkan Jafarpour +2

We study maximum selection and sorting of numbers using pairwise comparators that output the larger of their two inputs if the inputs are more than a given threshold apart, and…

cs.IT2015

Universal Compression of Power-Law Distributions

Moein Falahatgar, Ashkan Jafarpour, Alon Orlitsky +2

English words and the outputs of many other natural processes are well-known to follow a Zipf distribution. Yet this thoroughly-established property has never been shown to help co…