Model Detail
MiniMax-M2.7-ConfigI-MLX
—On the Stability and Generalization of First-order Bilevel Minimax Optimization
arXiv:2604.20115v1 Announce Type: new Abstract: Bilevel optimization and bilevel minimax optimization have recently emerged as unifying frameworks for a range of machine-learning tasks, including hyperparameter optimization and reinforcement learning. The existing literature focuses on empirical eff
Formal Verification of Minimax Algorithms
arXiv:2509.20138v2 Announce Type: replace Abstract: Minimax-based search algorithms with alpha-beta pruning and transposition tables are a central component of classical game-playing engines and remain widely used in practice. Despite their widespread use, these algorithms are subtle, highly optimiz
Minimax Optimality and Spectral Routing for Majority-Vote Ensembles under Markov Dependence
arXiv:2604.13414v1 Announce Type: cross Abstract: Majority-vote ensembles achieve variance reduction by averaging over diverse, approximately independent base learners. When training data exhibits Markov dependence, as in time-series forecasting, reinforcement learning (RL) replay buffers, and spati
Online Covariance Estimation in Averaged SGD: Improved Batch-Mean Rates and Minimax Optimality via Trajectory Regression
arXiv:2604.10814v1 Announce Type: new Abstract: We study online covariance matrix estimation for Polyak--Ruppert averaged stochastic gradient descent (SGD). The online batch-means estimator of Zhu, Chen and Wu (2023) achieves an operator-norm convergence rate of $O(n^{-(1-\alpha)/4})$, which yields
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
