Jan 5, 2021 · We propose a unified and modular convex optimization framework, relying on second-order cone (SOC) tightening, to encode hard affine SDP ...

In this paper, we thus propose a unified and modular convex optimization framework for kernel machines relying on SOC tightening to encode hard affine SDP ...

We propose a unified and modular convex optimization framework, relying on second-order cone (SOC) tightening, to encode hard affine SDP constraints on function ...

Jan 5, 2021 · Section 3 is about the handling of hard affine SDP shape constraints. The constraints are then embed- ded into supervised learning in Section 4.

We propose a unified and modular convex optimization framework, relying on second-order cone (SOC) tightening, to encode hard affine SDP constraints on function ...

This work proposes a unified and modular convex optimization framework, relying on second-order cone (SOC) tightening, to encode hard affine SDP constraints ...

Code to reproduce "Handling Hard Affine SDP Shape Constraints in RKHSs", Pierre-Cyril Aubin-Frankowski and Zoltan Szabo, JMLR 2022, ...

Code to reproduce "Handling Hard Affine SDP Shape Constraints in RKHSs", Pierre-Cyril Aubin-Frankowski and Zoltan Szabo, JMLR 2022, ...

Sep 8, 2022 · Focus: hard affine shape constraints on derivatives & RKHS. Proposed ... Handling hard affine SDP shape constraints in RKHSs. Technical ...

Handling Hard Affine SDP Shape Constraints in RKHSs ... The modular nature of the proposed approach allows to simultaneously handle multiple shape constraints, ...