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The No-Clash Teaching Dimension is Bounded by VC Dimension

arXiv:2603.23561v3 Announce Type: replace-cross Abstract: In the realm of machine learning theory, to prevent unnatural coding schemes between teacher and learner, No-Clash Teaching Dimension was introduced as provably optimal complexity measure for collusion-free teaching. However, whether No-Clash Teaching Dimension is…

ParetoBandit: Budget-Paced Adaptive Routing for Non-Stationary LLM Serving

arXiv:2604.00136v1 Announce Type: new Abstract: Production LLM serving often relies on multi-model portfolios spanning a ~530x cost range, where routing decisions trade off quality against cost. This trade-off is non-stationary: providers revise pricing, model quality can regress silently, and new…

Diagnosing Neural Convergence with Topological Alignment Spectra

arXiv:2411.08687v2 Announce Type: replace Abstract: Representational similarity in neural networks is inherently scale-dependent, yet widely used metrics such as Centered Kernel Alignment (CKA) and Procrustes analysis provide only global scalar estimates. These scalars often fail to distinguish micro-scale geometric jitter…

Order Optimal Regret Bounds for Sharpe Ratio Optimization under Thompson Sampling

arXiv:2508.13749v3 Announce Type: replace Abstract: In this paper, we study sequential decision-making for maximizing the Sharpe ratio (SR) in a stochastic multi-armed bandit (MAB) setting. Unlike standard bandit formulations that maximize cumulative reward, SR optimization requires balancing expected return and…

L’evy-Flow Models: Heavy-Tail-Aware Normalizing Flows for Financial Risk Management

arXiv:2604.00195v1 Announce Type: new Abstract: We introduce L’evy-Flows, a class of normalizing flow models that replace the standard Gaussian base distribution with L’evy process-based distributions, specifically Variance Gamma (VG) and Normal-Inverse Gaussian (NIG). These distributions naturally capture heavy-tailed behavior while…

Lossy Common Information in a Learnable Gray-Wyner Network

arXiv:2601.21424v2 Announce Type: replace Abstract: Many computer vision tasks share substantial overlapping information, yet conventional codecs tend to ignore this, leading to redundant and inefficient representations. The Gray-Wyner network, a classical concept from information theory, offers a principled framework for…