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

Partial Information Sharing Over Social Learning Networks

This work addresses the problem of sharing partial information within social learning strategies. In social learning, agents solve a distributed multiple hypothesis testing problem by performing two operations at each instant: first, agents incorporate inf ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Time vs. Truth: Age-Distortion Tradeoffs and Strategies for Distributed Inference

Yunus Inan

In 1948, Claude Shannon laid the foundations of information theory, which grew out of a study to find the ultimate limits of source compression, and of reliable communication. Since then, information theory has proved itself not only as a quest to find the ...

EPFL2023

Social Learning with Disparate Hypotheses

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon, Konstantinos Ntemos

In this paper we study the problem of social learning under multiple true hypotheses and self-interested agents. In this setup, each agent receives data that might be generated from a different hypothesis (or state) than the data other agents receive. In c ...

IEEE2022

A Fundamental Limit of Distributed Hypothesis Testing Under Memoryless Quantization

Ali H. Sayed, Emre Telatar, Mert Kayaalp, Yunus Inan

We consider a distributed binary hypothesis testing setup where multiple nodes send quantized information to a central processor, which is oblivious to the nodes’ statistics. We study the regime where the type-II error decays exponentially and the type-I e ...

2021

A Statistical Test for Probabilistic Fairness

Daniel Kuhn, Viet Anh Nguyen, Bahar Taskesen

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, ...

2021

Social Learning Under Inferential Attacks

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon, Konstantinos Ntemos

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 ...

IEEE2021

Multiple testing with test statistics following heavy-tailed distributions

Zhiwen Jiang

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 ...

EPFL2021

Strategy to validate sensor-placement methodologies in the context of sparse measurement in complex urban systems

Ian Smith, Numa Joy Bertola, Alberto Costa

The Internet of Things creates opportunities to develop data-driven design methodologies for smart cities. However, effects rather than causes are often measured in complex urban systems, requiring robust data-interpretation methodologies. Additionally, ef ...

2020

Statistical Applications of Random Matrix Theory: Comparison of Two Populations

Rémy Mariétan

During the last twenty years, Random matrix theory (RMT) has produced numerous results that allow a better understanding of large random matrices. These advances have enabled interesting applications in the domain of communication. Although this theory can ...

EPFL2019

On null hypotheses in survival analysis

Mats Julius Stensrud, Pal Christie Ryalen

The conventional nonparametric tests in survival analysis, such as the log-rank test, assess the null hypothesis that the hazards are equal at all times. However, hazards are hard to interpret causally, and other null hypotheses are more relevant in many s ...

2019

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

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Partial Information Sharing Over Social Learning Networks

Time vs. Truth: Age-Distortion Tradeoffs and Strategies for Distributed Inference

Social Learning with Disparate Hypotheses

A Fundamental Limit of Distributed Hypothesis Testing Under Memoryless Quantization

A Statistical Test for Probabilistic Fairness

Social Learning Under Inferential Attacks

Multiple testing with test statistics following heavy-tailed distributions

Strategy to validate sensor-placement methodologies in the context of sparse measurement in complex urban systems

Statistical Applications of Random Matrix Theory: Comparison of Two Populations

On null hypotheses in survival analysis

Partial Information Sharing Over Social Learning Networks

Time vs. Truth: Age-Distortion Tradeoffs and Strategies for Distributed Inference

A Statistical Test for Probabilistic Fairness

Social Learning with Disparate Hypotheses

Multiple testing with test statistics following heavy-tailed distributions

On null hypotheses in survival analysis

Statistical Applications of Random Matrix Theory: Comparison of Two Populations

Social Learning Under Inferential Attacks

A Fundamental Limit of Distributed Hypothesis Testing Under Memoryless Quantization

Strategy to validate sensor-placement methodologies in the context of sparse measurement in complex urban systems