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Found 7 papers, 0 papers with code
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval
Large neural models (such as Transformers) achieve state-of-the-art performance for information retrieval (IR).
Multivariate Trend Filtering for Lattice Data
We study a multivariate version of trend filtering, called Kronecker trend filtering or KTF, for the case in which the design points form a lattice in $d$ dimensions.
A Higher-Order Kolmogorov-Smirnov Test
We present an extension of the Kolmogorov-Smirnov (KS) two-sample test, which can be more sensitive to differences in the tails.
Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods
To move past this, we define two new higher-order TV classes, based on two ways of compiling the discrete derivatives of a parameter across the nodes.
Additive Models with Trend Filtering
We study additive models built with trend filtering, i. e., additive models whose components are each regularized by the (discrete) total variation of their $k$th (discrete) derivative, for a chosen integer $k \geq 0$.
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers
Lastly, we investigate the problem of adaptivity of the total variation denoiser to these smaller Sobolev function spaces.
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms
We develop parallel and distributed Frank-Wolfe algorithms; the former on shared memory machines with mini-batching, and the latter in a delayed update framework.
