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馃彚 Universit茅 C么te D'Azur

Score-based generative models are provably robust: an uncertainty quantification perspective
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AI Theory Robustness 馃彚 Universit茅 C么te D'Azur
Score-based generative models are provably robust to multiple error sources, as shown via a novel Wasserstein uncertainty propagation theorem.
On the Sparsity of the Strong Lottery Ticket Hypothesis
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AI Theory Optimization 馃彚 Universit茅 C么te D'Azur
Researchers rigorously prove the Strong Lottery Ticket Hypothesis, offering the first theoretical guarantees on the sparsity of winning neural network subnetworks.
High-probability complexity bounds for stochastic non-convex minimax optimization
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AI Theory Optimization 馃彚 Universit茅 C么te D'Azur
First high-probability complexity guarantees for solving stochastic nonconvex minimax problems using a single-loop method are established.