Technology
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.
On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences Anonymous Author(s) Affiliation Address email
Recurrent Neural Networks (RNNs) are effective tools for processing sequential data.
Operator World Models for Reinforcement Learning
However, it is not directly applicable to Reinforcement Learning (RL) due to the inaccessibility of explicit action-value functions.
Harnessing the Power of Choices in Decision Tree Learning Guy Blanc
Decision trees are a fundamental workhorse in machine learning. Their logical and hierarchical structure makes them easy to understand and their predictions easy to explain.