HEW-Local SGD provides exact-weight adaptive aggregation for heterogeneous local SGD with one-step guarantees and explicit convergence results under unequal local horizons.
Mnist handwritten digit database.ATT Labs [Online]
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A new parameter reconstruction method achieves globally optimal training for spiking neural networks by convexifying parallel recurrent threshold networks that include SNNs as a special case.
Introduces a novel search direction enabling sublinear stochastic bilevel regret guarantees for first- and zeroth-order online bilevel optimization algorithms without relying on window smoothing.
AML outperforms cross-validated baselines including CNNs on 50-2000 example image datasets and is comparable to XGBoost/LightGBM on tabular data using only training data and no task-dependent hyperparameters.
Dim-R2 extends R2 to arbitrary dimensions, supplies multidimensional accuracy views, and reduces noise sensitivity for better regression evaluation.
citing papers explorer
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Heterogeneous-Horizon Exact-Weight Local SGD
HEW-Local SGD provides exact-weight adaptive aggregation for heterogeneous local SGD with one-step guarantees and explicit convergence results under unequal local horizons.
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Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction
A new parameter reconstruction method achieves globally optimal training for spiking neural networks by convexifying parallel recurrent threshold networks that include SNNs as a special case.
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Stochastic Regret Guarantees for Online Zeroth- and First-Order Bilevel Optimization
Introduces a novel search direction enabling sublinear stochastic bilevel regret guarantees for first- and zeroth-order online bilevel optimization algorithms without relying on window smoothing.
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Algebraic Machine Learning for Small-to-Medium Datasets Is Competitive against Strong Standard Baselines
AML outperforms cross-validated baselines including CNNs on 50-2000 example image datasets and is comparable to XGBoost/LightGBM on tabular data using only training data and no task-dependent hyperparameters.
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A dimensional R2 regression metric
Dim-R2 extends R2 to arbitrary dimensions, supplies multidimensional accuracy views, and reduces noise sensitivity for better regression evaluation.