Sheaf-ADMM trains multi-agent systems by unrolling ADMM with sheaf-specified constraints, yielding improved MNIST robustness to shifts and higher Sudoku solve rates than MPNN baselines.
Diagonal scaling in douglas-rachford splitting and admm
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A new adaptive multiparameter penalty selection method for multiconstraint and multiblock ADMM provides robustness to scale differences and initial parameter choices.
Periodic outer-momentum restarts in two-phase optimizers exploit phase cancellation in a linearized NTK model to widen stable learning-rate and momentum ranges in language-model pretraining.
A theoretical framework establishing representer theorems, Sobolev approximation bounds, and spectral convergence for kernel-based learning of spatio-temporal dynamical systems using OV RKHS and Koopman approximations.
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Learning Multi-Agent Coordination via Sheaf-ADMM
Sheaf-ADMM trains multi-agent systems by unrolling ADMM with sheaf-specified constraints, yielding improved MNIST robustness to shifts and higher Sudoku solve rates than MPNN baselines.
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An Adaptive Multiparameter Penalty Selection Method for Multiconstraint and Multiblock ADMM
A new adaptive multiparameter penalty selection method for multiconstraint and multiblock ADMM provides robustness to scale differences and initial parameter choices.
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Outer-Momentum Restarting in High-Dimensional Two-Phase Optimization
Periodic outer-momentum restarts in two-phase optimizers exploit phase cancellation in a linearized NTK model to widen stable learning-rate and momentum ranges in language-model pretraining.
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Spatio-Temporal Prediction via Operator-Valued RKHS and Koopman Approximation
A theoretical framework establishing representer theorems, Sobolev approximation bounds, and spectral convergence for kernel-based learning of spatio-temporal dynamical systems using OV RKHS and Koopman approximations.