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Found 11 papers, 3 papers with code
Modeling and Predicting Epidemic Spread: A Gaussian Process Regression Approach
Gaussian Process Regression excels in using small datasets and providing uncertainty bounds, and both of these properties are critical in modeling and predicting epidemic spreading processes with limited data.
Differentially Private Reward Functions for Markov Decision Processes
We then develop an algorithm for the numerical computation of the performance loss due to privacy on a case-by-case basis.
Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning
Accordingly, we propose Privacy-Engineered Value Decomposition Networks (PE-VDN), a Co-MARL algorithm that models multi-agent coordination while provably safeguarding the confidentiality of the agents' environment interaction data.
DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability
OOD data consists of test data that is significantly different from the model's training data.
Characterizing Compositionality of LQR from the Categorical Perspective
Then we develop new sufficient conditions to guarantee that the LQR designed for a composite system is equal to the LQR attained through composition of LQRs for its subsystems.
DOMINO: Domain-aware Loss for Deep Learning Calibration
Deep learning has achieved the state-of-the-art performance across medical imaging tasks; however, model calibration is often not considered.
Differential Privacy in Cooperative Multiagent Planning
We synthesize policies that are robust to privacy by reducing the value of the total correlation.
Distributed Reproduction Numbers of Networked Epidemics
These conditions are then used to derive new conditions for the existence, uniqueness, and stability of equilibrium states.
Fast Verification of Control Barrier Functions via Linear Programming
Targeting the latter problem, this paper presents a method of verifying any finite number of candidate control barrier functions with linear programming.
DOMINO: Domain-aware Model Calibration in Medical Image Segmentation
Our experiments demonstrate that our DOMINO-calibrated deep neural networks outperform non-calibrated models and state-of-the-art morphometric methods in head image segmentation.
Privacy-Preserving Kickstarting Deep Reinforcement Learning with Privacy-Aware Learners
We then develop a kickstarted deep reinforcement learning algorithm for the student that is privacy-aware because we calibrate its objective with the parameters of the teacher's privacy mechanism.
