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Found 5 papers, 1 papers with code
GloptiNets: Scalable Non-Convex Optimization with Certificates
We present a novel approach to non-convex optimization with certificates, which handles smooth functions on the hypercube or on the torus.
On the Benefits of Large Learning Rates for Kernel Methods
This paper studies an intriguing phenomenon related to the good generalization performance of estimators obtained by using large learning rates within gradient descent algorithms.
Beyond Tikhonov: Faster Learning with Self-Concordant Losses via Iterative Regularization
The theory of spectral filtering is a remarkable tool to understand the statistical properties of learning with kernels.
Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization
The theory of spectral filtering is a remarkable tool to understand the statistical properties of learning with kernels.
Improving Approximate Optimal Transport Distances using Quantization
Optimal transport (OT) is a popular tool in machine learning to compare probability measures geometrically, but it comes with substantial computational burden.
