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YUNFAN ZHAO

9 accepted papers

2025

Optimizing Vital Sign Monitoring in Resource-Constrained Maternal Care: An RL-Based Restless Bandit Approach

Niclas Boehmer, Yunfan Zhao, Guojun Xiong, Paula Rodriguez-Diaz, Paola Del Cueto Cibrian, Joseph Ngonzi +2
AAAI 2025technical

Maternal mortality remains a significant global public health challenge. One promising approach to reducing maternal deaths occurring during facility-based childbirth is through early warning systems, which require the consistent monitoring of mothers' vital signs after giving birth. Wireless vital…

Cited by 3Source ↗PDF ↗Scholar ↗
2025

The Bandit Whisperer: Communication Learning for Restless Bandits

Yunfan Zhao, Tonghan Wang, Dheeraj Mysore Nagaraj, Aparna Taneja, Milind Tambe
AAAI 2025technical

Applying Reinforcement Learning (RL) to Restless Multi-Arm Bandits (RMABs) offers a promising avenue for addressing allocation problems with resource constraints and temporal dynamics. However, classic RMAB models largely overlook the challenges of (systematic) data errors - a common occurrence in r…

Cited by 6Source ↗PDF ↗Scholar ↗
2024

A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health

Nikhil Behari, Edwin Zhang, YUNFAN ZHAO, Aparna Taneja, Dheeraj Mysore Nagaraj, Milind Tambe
NeurIPS 2024poster

Restless multi-armed bandits (RMAB) have demonstrated success in optimizing resource allocation for large beneficiary populations in public health settings. Unfortunately, RMAB models lack flexibility to adapt to evolving public health policy priorities. Concurrently, Large Language Models (LLMs) ha…

Cited by 14Source ↗PDF ↗Scholar ↗
2024

Group Fairness in Predict-Then-Optimize Settings for Restless Bandits

Shresth Verma, Yunfan Zhao, Sanket Shah, Niclas Boehmer, Aparna Taneja, Milind Tambe
UAI 2024poster

Restless multi-arm bandits (RMABs) are a model for sequentially allocating a limited number of resources to agents modeled as Markov Decision Processes. RMABs have applications in cellular networks, anti-poaching, and in particular, healthcare. For such high-stakes use cases, allocations are often r…

Cited by 8Source ↗PDF ↗Scholar ↗
2024

Scalable Neural Network Kernels

Arijit Sehanobish, Krzysztof Marcin Choromanski, YUNFAN ZHAO, Kumar Avinava Dubey, Valerii Likhosherstov
ICLR 2024poster

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. SNNKs effectively disentangle the inputs from the parameters of the neural network in the FFL,…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗
2024

Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization

Yunfan Zhao, Nikhil Behari, Edward Hughes, Edwin Zhang, Dheeraj Nagaraj, Karl Tuyls +2
IJCAI 2024poster

Restless multi-arm bandits (RMABs) is a class of resource allocation problems with broad application in areas such as healthcare, online advertising, and anti-poaching. We explore several important question such as how to handle arms opting-in and opting-out over time without frequent retraining fro…

Cited by 12Source ↗PDF ↗code ↗Scholar ↗
2023

Efficient Graph Field Integrators Meet Point Clouds

Krzysztof Marcin Choromanski, Arijit Sehanobish, Han Lin, YUNFAN ZHAO, Eli Berger, Tetiana Parshakova +10
ICML 2023poster

We present two new classes of algorithms for efficient field integration on graphs encoding point cloud data. The first class, $\mathrm{SeparatorFactorization}$ (SF), leverages the bounded genus of point cloud mesh graphs, while the second class, $\mathrm{RFDiffusion}$ (RFD), uses popular $\epsilon$…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗