Covariate Shift Adaptation for Structured Regression With Frank–Wolfe Algorithms
This paper concerns structured regression problems wherein the issue of covariate shift is addressed, which aims at reducing the discrepancy in training and test data distributions, using computationally efficient and sparse optimization principles. In particular, the projection-free Frank-Wolfe opt...
Main Authors: | Santosh V. Chapaneri, Deepak J. Jayaswal |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8727878/ |
Similar Items
-
Short Paper - A note on the Frank–Wolfe algorithm for a class of nonconvex and nonsmooth optimization problems
by: de Oliveira, Welington
Published: (2023-01-01) -
Randomised block-coordinate Frank-Wolfe algorithm for distributed online learning over networks
by: Jingchao Li, et al.
Published: (2020-04-01) -
THE VISION OF FRANK LLOYD WRIGHT /
by: 282491 Heinz, Thomas A.
Published: (2002) -
On the Decomposition of Covariate Shift Assumption for the Set-to-Set Matching
by: Masanari Kimura
Published: (2023-01-01) -
An SDP Dual Relaxation for the Robust Shortest-Path Problem with Ellipsoidal Uncertainty: Pierra’s Decomposition Method and a New Primal Frank–Wolfe-Type Heuristics for Duality Gap Evaluation
by: Chifaa Al Dahik, et al.
Published: (2022-10-01)