Model Detail
MiniMax-M2.5-GGUF
—GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression
arXiv:2506.03074v5 Announce Type: replace-cross Abstract: We present `GL-LowPopArt`, a novel Catoni-style estimator for generalized low-rank trace regression. Building on `LowPopArt` (Jang et al., 2024), it employs a two-stage approach: nuclear norm regularization followed by matrix Catoni estimatio
Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal
arXiv:2411.19653v2 Announce Type: replace-cross Abstract: We study the kernel instrumental variable (KIV) algorithm, a kernel-based two-stage least-squares method for nonparametric instrumental variable regression. We provide a convergence analysis covering both identified and non-identified regimes
Minimaxity and Admissibility of Bayesian Neural Networks
arXiv:2604.04673v1 Announce Type: cross Abstract: Bayesian neural networks (BNNs) offer a natural probabilistic formulation for inference in deep learning models. Despite their popularity, their optimality has received limited attention through the lens of statistical decision theory. In this paper,
Water-Filling is Universally Minimax Optimal
arXiv:2603.26893v1 Announce Type: cross Abstract: Allocation of dynamically-arriving (i.e., online) divisible resources among a set of offline agents is a fundamental problem, with applications to online marketplaces, scheduling, portfolio selection, signal processing, and many other areas. The wate
The Minimax Lower Bound of Kernel Stein Discrepancy Estimation
arXiv:2510.15058v3 Announce Type: replace-cross Abstract: Kernel Stein discrepancies (KSDs) have emerged as a powerful tool for quantifying goodness-of-fit over the last decade, featuring numerous successful applications. To the best of our knowledge, all existing KSD estimators with known rate achi
Mitigating Backdoor Attacks in Federated Learning Using PPA and MiniMax Game Theory
arXiv:2603.28652v1 Announce Type: new Abstract: Federated Learning (FL) is witnessing wider adoption due to its ability to benefit from large amounts of scattered data while preserving privacy. However, despite its advantages, federated learning suffers from several setbacks that directly impact the
