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Found 3 papers, 1 papers with code
Conformalization of Sparse Generalized Linear Models
Given a sequence of observable variables $\{(x_1, y_1), \ldots, (x_n, y_n)\}$, the conformal prediction method estimates a confidence set for $y_{n+1}$ given $x_{n+1}$ that is valid for any finite sample size by merely assuming that the joint distribution of the data is permutation invariant.
Solving Robust MDPs through No-Regret Dynamics
Reinforcement Learning is a powerful framework for training agents to navigate different situations, but it is susceptible to changes in environmental dynamics.
Generalization Bounds for Magnitude-Based Pruning via Sparse Matrix Sketching
In this paper, we derive a novel bound on the generalization error of Magnitude-Based pruning of overparameterized neural networks.
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