Riemannian networks are introduced for the full-rank correlation matrix manifold by extending MLR, FC, and convolutional layers to five geometries with backpropagation methods for two, showing effectiveness over SPD and Grassmannian baselines.
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HPO enables unbiased policy optimization in hybrid action spaces by mixing differentiable simulation gradients with score-function estimates, outperforming PPO as continuous dimensions increase.
A convex data-driven inverse RL framework for linear systems with uncertainty that uses a generalized LQR cost with cross terms, kernel regression from data, and differentiable SDPs for robust cost design over perturbations.
LLM.int8() performs 8-bit inference for transformers up to 175B parameters with no accuracy loss by combining vector-wise quantization for most features with 16-bit mixed-precision handling of systematic outlier dimensions.
Neural networks trained on scalar diffusion data predict adaptive coarse basis functions for Schwarz methods, transferring without retraining to linear elasticity and nonlinear p-Laplace problems.
SGD on multiclass cross-entropy loss alternates between curvature-driven oscillations and stable regimes but self-stabilizes to enable best-iterate convergence with large learning rates for linear and two-layer models.
SGD is reformulated via a master equation from discrete updates, producing a discrete Fokker-Planck equation that predicts non-stationary variance growth proportional to learning rate in flat Hessian directions.
Spectral decomposition of the logit Jacobian yields an adaptive MSA with linear convergence and a tractable Newton method for path-based SUE, with reported speedups on networks up to Chicago Regional size.
New Berry-Esseen bounds for multivariate martingale difference sequences achieve n^{-1/4} rate and polylog(d) dimension dependence in Kolmogorov distance.
AdamO modifies Adam with an orthogonality correction to ensure the spectral radius of the TD update operator stays below one, providing a theoretical stability guarantee for offline RL.
A recursive cubing framework identifies stable hyperparameter regions for MC dropout uncertainty quantification in spatial deep learning and produces competitive or superior predictive intervals versus a statistical baseline on simulations and land-surface temperature data.
A survey equating offline Monte-Carlo/SAA and online stochastic-approximation sample complexities for convex stochastic optimization arising in statistics and ML.
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Berry-Esseen bounds for multivariate martingale difference sequences in the Kolmogorov distance
New Berry-Esseen bounds for multivariate martingale difference sequences achieve n^{-1/4} rate and polylog(d) dimension dependence in Kolmogorov distance.