Zobrazeno 1 - 10
of 8 726
pro vyhledávání: '"Ramdas, A"'
We explicitly define the notion of (exact or approximate) compound e-values which have been implicitly presented and extensively used in the recent multiple testing literature. We show that every FDR controlling procedure can be recovered by instanti
Externí odkaz:
http://arxiv.org/abs/2409.19812
We present a sequential version of the kernelized Stein discrepancy, which allows for conducting goodness-of-fit tests for unnormalized densities that are continuously monitored and adaptively stopped. That is, the sample size need not be fixed prior
Externí odkaz:
http://arxiv.org/abs/2409.17505
Autor:
Khan, Asir Intisar, Ramdas, Akash, Lindgren, Emily, Kim, Hyun-Mi, Won, Byoungjun, Wu, Xiangjin, Saraswat, Krishna, Chen, Ching-Tzu, Suzuki, Yuri, da Jornada, Felipe H., Oh, Il-Kwon, Pop, Eric
The electrical resistivity of conventional metals, such as copper, is known to increase in thinner films due to electron-surface scattering, limiting the performance of metals in nanoscale electronics. Here, we uncover an exceptional reduction of res
Externí odkaz:
http://arxiv.org/abs/2409.17337
Existing concentration bounds for bounded vector-valued random variables include extensions of the scalar Hoeffding and Bernstein inequalities. While the latter is typically tighter, it requires knowing a bound on the variance of the random variables
Externí odkaz:
http://arxiv.org/abs/2409.06060
Autor:
Ramdas, Tejas, Wells, Martin T.
In this study, we leverage powerful non-linear machine learning methods to identify the characteristics of trades that contain valuable information. First, we demonstrate the effectiveness of our optimized neural network predictor in accurately predi
Externí odkaz:
http://arxiv.org/abs/2409.05192
Autor:
Saha, Aytijhya, Ramdas, Aaditya
We propose an e-value based framework for testing composite nulls against composite alternatives when an $\epsilon$ fraction of the data can be arbitrarily corrupted. Our tests are inherently sequential, being valid at arbitrary data-dependent stoppi
Externí odkaz:
http://arxiv.org/abs/2408.14015
Double (debiased) machine learning (DML) has seen widespread use in recent years for learning causal/structural parameters, in part due to its flexibility and adaptability to high-dimensional nuisance functions as well as its ability to avoid bias fr
Externí odkaz:
http://arxiv.org/abs/2408.09598
Autor:
Malekian, Reihaneh, Ramdas, Aaditya
The matrix Markov inequality by Ahlswede was stated using the Loewner anti-order between positive definite matrices. Wang use this to derive several other Chebyshev and Chernoff-type inequalities (Hoeffding, Bernstein, empirical Bernstein) in the Loe
Externí odkaz:
http://arxiv.org/abs/2408.05998
In online multiple testing, the hypotheses arrive one by one, and at each time we must immediately reject or accept the current hypothesis solely based on the data and hypotheses observed so far. Many procedures have been proposed, but none of them a
Externí odkaz:
http://arxiv.org/abs/2407.20683
Autor:
Fischer, Lasse, Ramdas, Aaditya
In contemporary research, data scientists often test an infinite sequence of hypotheses $H_1,H_2,\ldots $ one by one, and are required to make real-time decisions without knowing the future hypotheses or data. In this paper, we consider such an onlin
Externí odkaz:
http://arxiv.org/abs/2407.15733