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Found 3 papers, 1 papers with code
Faster Algorithms for Learning Convex Functions
For the task of convex Lipschitz regression, we establish that our proposed algorithm converges with iteration complexity of $ O(n\sqrt{d}/\epsilon)$ for a dataset $\bm X \in \mathbb R^{n\times d}$ and $\epsilon > 0$.
Interpretable Visualization and Higher-Order Dimension Reduction for ECoG Data
After signal processing, this type of data may be organized as a 4-way tensor with dimensions representing trials, electrodes, frequency, and time.
Implicit regularization and solution uniqueness in over-parameterized matrix sensing
Surprisingly, recent work argues that the choice of $r \leq n$ is not pivotal: even setting $U \in \mathbb{R}^{n \times n}$ is sufficient for factored gradient descent to find the rank-$r$ solution, which suggests that operating over the factors leads to an implicit regularization.
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