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Found 8 papers, 4 papers with code
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health
Efforts to reduce maternal mortality rate, a key UN Sustainable Development target (SDG Target 3. 1), rely largely on preventative care programs to spread critical health information to high-risk populations.
Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization
Restless multi-arm bandits (RMABs), a class of resource allocation problems with broad application in areas such as healthcare, online advertising, and anti-poaching, have recently been studied from a multi-agent reinforcement learning perspective.
Scalable Neural Network Kernels
We introduce the concept of scalable neural network kernels (SNNKs), the replacements of regular feedforward layers (FFLs), capable of approximating the latter, but with favorable computational properties.
Estimate-Then-Optimize versus Integrated-Estimation-Optimization versus Sample Average Approximation: A Stochastic Dominance Perspective
In this paper, we show that a reverse behavior appears when the model class is well-specified and there is sufficient data.
Balanced Off-Policy Evaluation for Personalized Pricing
We consider a personalized pricing problem in which we have data consisting of feature information, historical pricing decisions, and binary realized demand.
Efficient Graph Field Integrators Meet Point Clouds
We present two new classes of algorithms for efficient field integration on graphs encoding point clouds.
Implicit Two-Tower Policies
We present a new class of structured reinforcement learning policy-architectures, Implicit Two-Tower (ITT) policies, where the actions are chosen based on the attention scores of their learnable latent representations with those of the input states.
Nuances in Margin Conditions Determine Gains in Active Learning
We consider nonparametric classification with smooth regression functions, where it is well known that notions of margin in $E[Y|X]$ determine fast or slow rates in both active and passive learning.
