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Adaptive Active Hypothesis Testing under Limited Information


Adaptive Active Hypothesis Testing under Limited Information

Abstract. We consider the problem of active sequential hypothesis testing where a Bayesian decision maker must infer the true hypothesis from a set of ...

Adaptive Active Hypothesis Testing under Limited Information

We consider the problem of active sequential hypothesis testing where a Bayesian decision maker must infer the true hypothesis from a set of hypotheses. The.

Adaptive active hypothesis testing under limited information

In this paper we consider a special case where the decision maker has limited knowledge about the distribution of observations for each action, ...

Adaptive Active Hypothesis Testing under Limited Information

Adaptive active hypothesis testing under limited information. In 31st Conference on Advances in Neural Information Processing Systems (NIPS 2017), 4-9 ...

Reviews: Adaptive Active Hypothesis Testing under Limited ...

This paper considers active hypothesis testing where the algorithm only receives an indirect feedback. It proposes a method based on incomplete Bayesian update, ...

Active hypothesis testing in unknown environments using recurrent ...

Hegde, “Adaptive active hypothesis testing under limited information,” in Advances in Neural Information Processing. Systems (I. Guyon, U. V. Luxburg, S ...

Evasive Active Hypothesis Testing - NSF PAR

Abstract—We consider a situation in which a decision maker takes sequential and adaptive sensing actions to collect measure-.

Presentation: Adaptive Active Hypothesis Testing under Limited ...

Our presentation of the NIPS poster "Adaptive Active Hypothesis Testing under Limited Information", by Fabio Cecchi and Nidhi Hegde, presented: Wed Dec 6th ...

Adaptive, Rate-Optimal Hypothesis Testing in Nonparametric IV ...

Our new test builds on a simple data-driven choice of tuning parameters that ensures asymptotic size control and non-trivial power uniformly ...

Adaptive information source selection during hypothesis testing

dence more often when hypotheses are more sparse in a task in which the information options were limited to postive and negative evidence requests. There is ...

A Statistical Hypothesis Testing Strategy for Adaptively Blending ...

Therefore, developing new data assimilation methods that mitigate Gaussian assumptions is an active area of research. One strategy, which has gained momentum in ...

Fast and covariate-adaptive method amplifies detection power in ...

Multiple hypothesis testing (or multiple testing correction) is an essential component in many modern data analysis workflows. A common ...

[PDF] Active Hypothesis Testing in Unknown Environments Using ...

A combination of deep reinforcement learning and supervised learning is proposed for active sequential hypothesis testing in completely unknown environments ...

Multiple Testing with the Structure-Adaptive Benjamini–Hochberg ...

In modern scientific fields with high dimensional data, the problem of multiple testing arises whenever we search over a large number of questions or hypotheses ...

Approximation Algorithms for Active Sequential Hypothesis Testing

This paper provides the first approximation algorithms for ASHT, under two types of adaptivity. First, a policy is partially adaptive if it ...

Hypothesis testing with active information - IDEAS/RePEc

We develop hypothesis testing for active information — the averaged quantity in the Kullback–Leibler divergence. To our knowledge, this is the first paper ...

Deep Multi-Agent Reinforcement Learning for Decentralized Active ...

In particular, the problem was referred to as controlled sensing for hy- pothesis testing in [5], [6] and active hypothesis testing. (AHT) in [7] ...

Preserving Statistical Validity in Adaptive Data Analysis - UPenn CIS

A great deal of effort has been devoted to reducing the risk of spurious scientific discoveries, from the use of sophisticated validation techniques, to deep ...

Active sequential hypothesis testing - Project Euclid

Applying various results from [9, 21] in our context of active hypothesis testing and utilizing a dynamic programming interpretation, a notion of optimal ...

Neuroadaptive Bayesian Optimization and Hypothesis Testing

... in real-time data analysis and machine learning. ... limited background knowledge. ... neuroadaptive paradigms and, in particular, active sampling approaches.