arXiv Computation and Language

Query-Oblivious Coresets for Softmax Attention: Improved Bounds and Efficient Constructions

arXiv Machine Learning
Sep 2

Dense Weak Hiding: Closing Complexity Gaps in Nonconvex and PL Finite-Sum Optimization under Individual Smoothness

The paper establishes the optimal incremental first‑order oracle (IFO) complexity for nonconvex finite‑sum optimization under individual smoothness, proving a matching lower bound that closes a previously missing √{n} factor. It also refines the analysis of the PAGE algorithm under the global Polyak‑Lojasiewicz condition, providing tighter guarantees for different ranges of the condition number. The authors introduce a novel dense weak hiding construction that yields these lower bounds and demonstrates the limits of existing methods.

By Yuxing Peng, Zhiqing Tang, Weijia Jia
arXiv Machine Learning
Sep 4

Restricted Eigenvalues Beyond Gaussian Width: Threshold Occupancy under Heavy Tails

The paper investigates restricted eigenvalue (RE) bounds for norm‑regularized estimators under heavy‑tailed designs. It shows that the previously conjectured sample‑size law based on Gaussian width fails for heavy‑tailed measurements, due to a phenomenon called simultaneous threshold occupancy. The authors provide explicit counterexamples, derive worst‑case sample‑complexity bounds, and compare the behavior of heavy‑tailed versus Gaussian designs on constant‑width polyhedral descent cones.

By Shi Fu, Huibo Xu, Qixin Zhang, Dacheng Tao
arXiv Machine Learning
Aug 18

Spectral Gaps of Hit-and-Run and Coordinate Hit-and-Run

arXiv:2608. 16878v1 Announce Type: cross Abstract: For any convex body $\mathcal{K}\subset\mathbb{R}^{n}$ containing a unit ball, the spectral gap of Hit-and-Run is $\Omega(1/(n^2 C_{\mathsf{PI}}))$, where $C_{\mathsf{PI}}$ is the Poincar\'e constant of the uniform distribution $\pi$ over $\mathcal{K}$.

By Yunbum Kook, Santosh S. Vempala
arXiv Machine Learning
Aug 12

Optimistic Rates for Multiclass PAC Learning

arXiv:2608. 10869v1 Announce Type: new Abstract: Worst-case multiclass bounds do not become smaller when the best classifier is already nearly correct: what is missing is an optimistic rate, a guarantee whose fluctuation scales with the oracle risk itself.

By Xiaoyu Li, Andi Han, Jiaojiao Jiang, Junbin Gao
arXiv Machine Learning
Jul 14

The VC dimension of partial concept classes via Radon's theorem

arXiv:2607. 10751v1 Announce Type: new Abstract: Following Alon, Hanneke, Holzman, and Moran (FOCS 2021), we define a partial concept class (PCC) as a family of partial functions \(f: V\to\{0,1,\ast\}\); equivalently, its concepts partition the ground set into black ($f^{-1}(1)$), grey ($f^{-1}(\ast)$), and white parts ($f^{-1}(0)$).

By Grigory Ivanov, Attila Jung, M\'arton Nasz\'odi