Hugging Face Trending Papers
Jun 3
Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models
Purpose The WHO's COVID-19 non-pharmaceutical interventions (e. g.
Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models
arXiv:2606. 04562v1 Announce Type: new Abstract: Purpose The WHO's COVID-19 non-pharmaceutical interventions (e.
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arXiv AI
Jun 3
SL-BiLEM: Structured Learnable Behavior-in-the-Loop Epidemic Modeling for Forecasting and Policy Evaluation
arXiv:2605. 26704v2 Announce Type: replace-cross Abstract: Epidemic forecasting faces a fundamental challenge: human behavior dynamically responds to disease spread, creating feedback loops that induce distribution shifts at policy intervention points.
arXiv AI
Aug 19
Adversarial Data Modeling in Epidemiology
The paper introduces a signaling‑game framework to model how individuals strategically misreport behavioral data—such as mask usage and vaccination status—to public health authorities. It provides a generative model of such adversarial data and a method for authorities to recover reliable signals, analyzing equilibrium outcomes and evaluating how deception affects epidemic control. Large‑scale simulations and real‑world validation show that well‑designed sender and receiver strategies can still maintain effective epidemic control even with pervasive dishonesty, and that behavioral distortions often follow structured patterns rather than random noise.
arXiv Machine Learning
Jun 5
Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions
arXiv:2606. 05692v1 Announce Type: new Abstract: Deep learning has enabled significant advances in time-series causal inference, yet progress remains constrained by the lack of realistic benchmarks with observable counterfactual outcomes.
arXiv AI
Jul 13
EHR-MPC: Inference-Time Control for Sepsis Treatment with Generative Patient Digital Twins
arXiv:2607. 08793v1 Announce Type: cross Abstract: Sepsis is a leading cause of mortality, yet optimal treatment policies remain contested.
arXiv Machine Learning
Jul 21
Enhancing Personalized Bladder Cancer Treatment Through Reinforcement Learning: A Recurrent Patient State Transition Decision Support Framework
arXiv:2607. 16916v1 Announce Type: new Abstract: Bladder cancer treatment requires personalized and adaptive decision-making, particularly for recurrent disease, where treatment effectiveness changes across successive clinical episodes.
arXiv Machine Learning
Aug 11
Learning Multi-Timescale Interventions under Safety and Resource Constraints
arXiv:2508. 03875v2 Announce Type: replace Abstract: Many sequential decision problems offer qualitatively different ways of influencing the environment: some interventions act immediately, whereas others induce persistent effects that continue to shape future states long after the decision that initiated them.
arXiv AI
Jun 3
The Epi-LLM Framework: probing LLM behavioral priors through epidemiological agent-based models
arXiv:2606. 02867v1 Announce Type: cross Abstract: Human behaviour during epidemics affects infectious disease dynamics, but quantifying this remains deeply challenging.
Hugging Face Trending Papers
Jun 4
Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions
Deep learning has enabled significant advances in time-series causal inference, yet progress remains constrained by the lack of realistic benchmarks with observable counterfactual outcomes. Existing datasets either rely on real-world observations without ground-truth counterfactuals or on simplified simulations that fail to capture complex causal dynamics.
arXiv AI
2d ago
Network World Models as Environments for Algorithm Design on Complex Systems
The paper introduces an action‑conditioned Network World Model that learns how a network’s diffusion dynamics evolve under interventions over time. This model can quickly predict the outcomes of actions, enabling a coding agent to design and refine algorithms that select actions to maximize expected performance on complex network tasks. Experiments on eight network tasks and five diffusion models show that the resulting algorithms match or surpass the best existing baselines in 138 of 141 settings while achieving up to 14.5× faster rollouts than traditional Monte Carlo simulation.
arXiv Machine Learning
Sep 14
Adaptive Chemotherapy Control under Tumor Heterogeneity via Reinforcement Learning
The paper presents a study on adaptive chemotherapy control using deep reinforcement learning (DRL) to address tumor heterogeneity and drug resistance. Closed‑loop DRL dosing policies—continuous (TD3) and discrete (DQN)—are trained on a high‑dimensional heterogeneous tumor model and benchmarked against a Pontryagin's Maximum Principle (PMP) open‑loop solution. Across a 100‑patient virtual cohort with ±10% parameter perturbations, TD3 achieves higher average tumor reduction, while DQN offers tighter inter‑patient dosing consistency, highlighting an efficacy‑consistency trade‑off. The work assumes full observation of tumor subpopulations, noting that clinical translation will require handling sparse, noisy measurements.
arXiv AI
Aug 5
Learning Clinical-Trial Strategy: Offline Policy Training for Decision Agents
arXiv:2608. 03606v1 Announce Type: new Abstract: Clinical development is sequential decision-making under uncertainty, where a sponsor must plan a portfolio of experiments from heterogeneous evidence.