Successive refinement for hypothesis testing and lossless one-helper problem

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Decay heat calculations of spent nuclear fuel (SNF) using Polaris and ORIGEN codes in the SCALE code sys-tem, and CASMO5 code, are validated using measurements from the Clab and GE-Morris facilities. Multiple hypothesis testing, relying on permutations and ...

We derive an upper bound on the reliability function of mismatched decoding for zero-rate codes. The bound is based on a result by Komlos that shows the existence of a subcode with certain symmetry properties. The bound is shown to coincide with the expurg ...

Characteristics of the spent nuclear fuel (SNF) are typically calculated, requiring validation a priori. The validation process relies on the difference between calculations and measurements, namely the bias. Usually, predicting the bias based on benchmark ...

The beginning of 21st century provided us with many answers about how to reach the channel capacity. Polarization and spatial coupling are two techniques for achieving the capacity of binary memoryless symmetric channels under low-complexity decoding algor ...

Social learning algorithms provide models for the formation of opinions over social networks resulting from local reasoning and peer-to-peer exchanges. Interactions occur over an underlying graph topology, which describes the flow of information among the ...

As distribution shifts are inescapable in realistic clinical scenarios due to inconsistencies in imaging protocols, scanner vendors, and across different centers, well-trained deep models incur a domain generalization problem in unseen environments. Despit ...

Algorithms are now routinely used to make consequential decisions that affect human lives. Examples include college admissions, medical interventions or law enforcement. While algorithms empower us to harness all information hidden in vast amounts of data, ...

In multiple testing problems where the components come from a mixture model of noise and true effect, we seek to first test for the existence of the non-zero components, and then identify the true alternatives under a fixed significance level

\alpha

. Two ...

This work proposes a novel strategy for social learning by introducing the critical feature of adaptation. In social learning, several distributed agents update continually their belief about a phenomenon of interest through: i) direct observation of strea ...

We introduce a protocol addressing the conformance test problem, which consists in determining whether a process under test conforms to a reference one. We consider a process to be characterized by the set of end products it produces, which is generated ac ...

Null models are useful for assessing whether a dataset exhibits a non-trivial property of interest. These models have recently gained interest in the neuroimaging community as means to explore dynamic properties of functional Magnetic Resonance Imaging (fM ...

A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents aims at driving the network beliefs to the wrong hypothesis. The adversaries a ...

Successive refinement for hypothesis testing and lossless one-helper problem

Chattez avec Graph Search

Validation of spent nuclear fuel decay heat calculations using Polaris, ORIGEN and CASMO5

Mismatched Decoding Reliability Function at Zero Rate

Data-Driven Predictive Models: Calculational Bias in Characterization of Spent Nuclear Fuel

Symmetry in design and decoding of polar-like codes

Explainability and Graph Learning From Social Interactions

OptTTA: Learnable Test-Time Augmentation for Source-Free Medical Image Segmentation Under Domain Shift

A Statistical Test for Probabilistic Fairness

Multiple testing with test statistics following heavy-tailed distributions

Adaptive Social Learning

Quantum conformance test

Interpreting null models of resting-state functional MRI dynamics: not throwing the model out with the hypothesis

Social Learning Under Inferential Attacks