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Found 15 papers, 3 papers with code
Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification
Practitioners conducting adaptive experiments often encounter two competing priorities: reducing the cost of experimentation by effectively assigning treatments during the experiment itself, and gathering information swiftly to conclude the experiment and implement a treatment across the population.
Immunogenic cell death triggered by pathogen ligands via host germ line-encoded receptors
In response to infections, host cells dictate a variety of cell death pathways, including apoptosis, pyroptosis, necrosis, and lysosomal cell death, which are essential for amplifying immune responses and controlling pathogen dissemination.
Dual-Directed Algorithm Design for Efficient Pure Exploration
Using dual variables, we characterize the necessary and sufficient conditions for an allocation to be optimal.
A Spatial-Temporal Deformable Attention based Framework for Breast Lesion Detection in Videos
Existing video-based breast lesion detection approaches typically perform temporal feature aggregation of deep backbone features based on the self-attention operation.
Open Problem: Optimal Best Arm Identification with Fixed Budget
Best arm identification or pure exploration problems have received much attention in the COLT community since Bubeck et al. (2009) and Audibert et al. (2010).
Information-Directed Selection for Top-Two Algorithms
We consider the best-k-arm identification problem for multi-armed bandits, where the objective is to select the exact set of k arms with the highest mean rewards by sequentially allocating measurement effort.
Contextual Information-Directed Sampling
Information-directed sampling (IDS) has recently demonstrated its potential as a data-efficient reinforcement learning algorithm.
An Analysis of Ensemble Sampling
Ensemble sampling serves as a practical approximation to Thompson sampling when maintaining an exact posterior distribution over model parameters is computationally intractable.
Adaptive Experimentation in the Presence of Exogenous Nonstationary Variation
We investigate experiments that are designed to select a treatment arm for population deployment.
Optimal Best Arm Identification in Two-Armed Bandits with a Fixed Budget under a Small Gap
We show that a strategy following the Neyman allocation rule (Neyman, 1934) is asymptotically optimal when the gap between the expected rewards is small.
Rate-optimal Bayesian Simple Regret in Best Arm Identification
We consider best arm identification in the multi-armed bandit problem.
Policy Choice and Best Arm Identification: Asymptotic Analysis of Exploration Sampling
We consider the "policy choice" problem -- otherwise known as best arm identification in the bandit literature -- proposed by Kasy and Sautmann (2021) for adaptive experimental design.
From Predictions to Decisions: The Importance of Joint Predictive Distributions
A fundamental challenge for any intelligent system is prediction: given some inputs, can you predict corresponding outcomes?
Sex Differences in Severity and Mortality Among Patients With COVID-19: Evidence from Pooled Literature Analysis and Insights from Integrated Bioinformatic Analysis
Objective: To conduct a meta-analysis of current studies that examined sex differences in severity and mortality in patients with COVID-19, and identify potential mechanisms underpinning these differences.
Improving the Expected Improvement Algorithm
To overcome this shortcoming, we introduce a simple modification of the expected improvement algorithm.
