
Powerful Knockoffs via Minimizing Reconstructability
ModelX knockoffs allows analysts to perform feature selection using alm...
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Exact Asymptotics for Linear Quadratic Adaptive Control
Recent progress in reinforcement learning has led to remarkable performa...
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A Power Analysis of the Conditional Randomization Test and Knockoffs
In many scientific problems, researchers try to relate a response variab...
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Crossvalidation Confidence Intervals for Test Error
This work develops central limit theorems for crossvalidation and consi...
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Testing goodnessoffit and conditional independence with approximate cosufficient sampling
Goodnessoffit (GoF) testing is ubiquitous in statistics, with direct t...
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Floodgate: inference for modelfree variable importance
Many modern applications seek to understand the relationship between an ...
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Fast and Powerful Conditional Randomization Testing via Distillation
In relating a response variable Y to covariates (Z,X), a key question is...
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Inference for Batched Bandits
As bandit algorithms are increasingly utilized in scientific studies, th...
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MapPredictive Motion Planning in Unknown Environments
Algorithms for motion planning in unknown environments are generally lim...
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Revisiting the Asymptotic Optimality of RRT*
RRT* is one of the most widely used samplingbased algorithms for asympt...
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Relaxing the Assumptions of Knockoffs by Conditioning
The recent paper Candès et al. (2018) introduced modelX knockoffs, a me...
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Metropolized Knockoff Sampling
ModelX knockoffs is a wrapper that transforms essentially any feature i...
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Safe Motion Planning in Unknown Environments: Optimality Benchmarks and Tractable Policies
This paper addresses the problem of planning a safe (i.e., collisionfre...
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RiskConstrained Reinforcement Learning with Percentile Risk Criteria
In many sequential decisionmaking problems one is interested in minimiz...
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Lucas Janson
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