arXiv:2507. 13263v4 Announce Type: replace-cross Abstract: Bayesian Optimization (BO) is a powerful tool for black-box optimization, but its application to high-dimensional permutation spaces is severely limited by the challenge of defining scalable representations.
By Zikai Xie, Linjiang Chen
arXiv:2603. 17353v2 Announce Type: replace-cross Abstract: The finite symmetric group S_n provides a natural domain for permutations, yet learning probability distributions on S_n is challenging due to its factorially growing size and discrete, non-Euclidean structure.
By Sizhuang He, Yangtian Zhang, Shiyang Zhang, David van Dijk
The paper introduces Soft-OMP and Soft-IHT, permutation‑based variants of Orthogonal Matching Pursuit and Iterative Hard Thresholding that replace the non‑differentiable argsort with continuous soft‑sort operators. These differentiable algorithms enable the construction of fully trainable neural network architectures—OMP‑Net and IHT‑Net—while preserving the core greedy sparse recovery logic. The authors show both theoretically and numerically that the soft variants approximate their hard counterparts with controllable accuracy and can be extended to structured sparse recovery by learning structure‑aware weights.
By Sina Mohammad-Taheri, Matthew J. Colbrook, Simone Brugiapaglia
arXiv:2606. 01111v1 Announce Type: new Abstract: Modern industrial recommender systems rely on thousands of heterogeneous features -- ranging from low-dimensional scalars (e.
By Yihong Huang, Chen Chu, Fei Chen, Yu Lin, Ruiduan Li, Zhihao Li
arXiv:2608.06912v2 Announce Type: replace
Abstract: Selecting the top-$k$ elements is a fundamental operation for inducing sparsity in large-scale models and optimization problems, enabling robust ex...
By Jakub Antczak, Joanna Wojciechowicz, Kamil Ksi\k{a}\.zek, Marcin Mazur, {\L}ukasz Struski, Jacek Tabor
arXiv:2510. 14812v2 Announce Type: replace Abstract: Structured weight sparsity accelerates training and inference on modern GPUs, but it trails unstructured dynamic sparse training (DST) in accuracy especially at extreme sparsity.
By Abhishek Tyagi, Arjun Iyer, Liam Young, William H Renninger, Christopher Kanan, Yuhao Zhu