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Found 7 papers, 0 papers with code
CLIP-QDA: An Explainable Concept Bottleneck Model
In this paper, we introduce an explainable algorithm designed from a multi-modal foundation model, that performs fast and explainable image classification.
On Double Descent in Reinforcement Learning with LSTD and Random Features
We identify the ratio between the number of parameters and the number of visited states as a crucial factor and define over-parameterization as the regime when it is larger than one.
Data-driven Reachability using Christoffel Functions and Conformal Prediction
In this paper, we improve upon these results by notably improving the sample efficiency and relaxing some of the assumptions by exploiting statistical guarantees from conformal prediction with training and calibration sets.
Fourier Features in Reinforcement Learning with Neural Networks
To the best of our knowledge, this is the first reported application of Fourier features in Deep RL.
Reachability analysis of linear hybrid systems via block decomposition
Reachability analysis aims at identifying states reachable by a system within a given time horizon.
Systems and Control Dynamical Systems Optimization and Control
JuliaReach: a Toolbox for Set-Based Reachability
We present JuliaReach, a toolbox for set-based reachability analysis of dynamical systems.
Systems and Control Dynamical Systems
Reach Set Approximation through Decomposition with Low-dimensional Sets and High-dimensional Matrices
Approximating the set of reachable states of a dynamical system is an algorithmic yet mathematically rigorous way to reason about its safety.
Systems and Control Dynamical Systems
