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Found 10 papers, 2 papers with code
The Fallacy of Minimizing Local Regret in the Sequential Task Setting
In the realm of Reinforcement Learning (RL), online RL is often conceptualized as an optimization problem, where an algorithm interacts with an unknown environment to minimize cumulative regret.
Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials
This paper proposes algorithm fidelity as a critical requirement for deploying online RL algorithms in clinical trials.
Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care
Dental disease is one of the most common chronic diseases despite being largely preventable.
A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes
In the reinforcement learning literature, there are many algorithms developed for either Contextual Bandit (CB) or Markov Decision Processes (MDP) environments.
Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines
Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education.
Statistical Inference After Adaptive Sampling for Longitudinal Data
In this work, we focus on longitudinal user data collected by a large class of adaptive sampling algorithms that are designed to optimize treatment decisions online using accruing data from multiple users.
Statistical Inference with M-Estimators on Adaptively Collected Data
Yet there is a lack of general methods for conducting statistical inference using more complex models on data collected with (contextual) bandit algorithms; for example, current methods cannot be used for valid inference on parameters in a logistic regression model for a binary reward.
Inference for Batched Bandits
As bandit algorithms are increasingly utilized in scientific studies and industrial applications, there is an associated increasing need for reliable inference methods based on the resulting adaptively-collected data.
Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis
We find that representations from language models consistently perform best on our syntactic auxiliary prediction tasks, even when trained on relatively small amounts of data.
Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Task Analysis
There is mounting evidence that pretraining can be valuable for neural network language understanding models, but we do not yet have a clear understanding of how the choice of pretraining objective affects the type of linguistic information that models learn.
