From the 1 of 111 linked papers with an AI index.
7 citations · 8 across the 59 of their papers we have counts for
42 papers · 1 filter
Scalable Causal Structure Learning via Amortized Conditional Independence Testing
James Leiner, Brian Manzo, Aaditya Ramdas +1
Controlling false positives (Type I errors) through statistical hypothesis testing is a foundation of modern scientific data analysis. Existing causal structure discovery algorithm…
Closed-form empirical Bernstein confidence sequences for scalars and matrices
Ben Chugg, Aaditya Ramdas
We derive a new closed-form variance-adaptive confidence sequence (CS) for estimating the average conditional mean of a sequence of bounded random variables. Empirically, it yields…
Huber-robust likelihood ratio tests for composite nulls and alternatives
Aytijhya Saha, Aaditya Ramdas
We propose an e-value based framework for testing arbitrary composite nulls against composite alternatives, when an fraction of the data can be arbitrarily corrupted. Our test…
Rao-Blackwellized e-variables
Dante de Roos, Ben Chugg, Peter Grünwald +1
We show that for any concave utility, the expected utility of an e-variable can only increase after conditioning on a sufficient statistic. The simplest form of the result has an e…
Time-uniform Chernoff bounds via nonnegative supermartingales
Steven R. Howard, Aaditya Ramdas, Jon McAuliffe +1
We develop a class of exponential bounds for the probability that a martingale sequence crosses a time-dependent linear threshold. Our key insight is that it is both natural and fr…
Admissible online closed testing must employ e-values
Lasse Fischer, Aaditya Ramdas
In contemporary research, data scientists often test an infinite sequence of hypotheses one by one, and are required to make real-time decisions without knowing th…