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Found 12 papers, 4 papers with code
Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impact
AI for social impact (AI4SI) offers significant potential for addressing complex societal challenges in areas such as public health, agriculture, education, conservation, and public safety.
Optimizing Vital Sign Monitoring in Resource-Constrained Maternal Care: An RL-Based Restless Bandit Approach
Maternal mortality remains a significant global public health challenge.
Improving the Prediction of Individual Engagement in Recommendations Using Cognitive Models
For public health programs with limited resources, the ability to predict how behaviors change over time and in response to interventions is crucial for deciding when and to whom interventions should be allocated.
The Bandit Whisperer: Communication Learning for Restless Bandits
Applying Reinforcement Learning (RL) to Restless Multi-Arm Bandits (RMABs) offers a promising avenue for addressing allocation problems with resource constraints and temporal dynamics.
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health
In this paper, we propose a Decision Language Model (DLM) for RMABs, enabling dynamic fine-tuning of RMAB policies in public health settings using human-language commands.
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.
