Country
Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model
It is crucial to quantify the uncertainty of these dynamics models when using them for planning.
Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models
Modularity is a critical design principle for both software systems and artificial intelligence (AI).
Scaling Riemannian Diffusion Models
Riemannian diffusion models draw inspiration from standard Euclidean space diffusion models to learn distributions on general manifolds.
Team-Fictitious Play for Reaching Team-Nash Equilibrium in Multi-team Games
Team-Nash equilibrium (TNE) predicts the outcomes of such coordinated interactions.
Provably Bounding Neural Network Preimages
However, many use cases of neural network verification require solving the inverse problem, or over-approximating the set of inputs that lead to certain outputs.
Stratified Prediction-Powered Inference for Hybrid Language Model Evaluation Adam Fisch, Joshua Maynez, R. Alex Hofer Bhuwan Dhingra Amir Globerson William W. Cohen
Evaluating machine learning models requires evaluation data.