Generalization is a testable hedging property of the learner's response law, recovered via f-divergence regularizers that induce information-geometric curves between training loss and sample dependence.
Tyrrell Rockafellar.Convex Analysis
3 Pith papers cite this work. Polarity classification is still indexing.
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A modular CPU-GPU batching framework for branch-and-bound delivers 10-100x speedups with zero optimality gap when certifying optimal cardinality-constrained GLMs.
A randomized (1+ε)-approximation algorithm for ordered-norm load balancing uses O((n+d)(ε^{-2} + log log d) log(n+d)) linear-oracle calls via follow-the-regularized-leader prices and martingale progress analysis.
citing papers explorer
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Bounded-Rationality, Hedging, and Generalization
Generalization is a testable hedging property of the learner's response law, recovered via f-divergence regularizers that induce information-geometric curves between training loss and sample dependence.
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From Sequential Nodes to GPU Batches: Parallel Branch and Bound for Optimal $k$-Sparse GLMs
A modular CPU-GPU batching framework for branch-and-bound delivers 10-100x speedups with zero optimality gap when certifying optimal cardinality-constrained GLMs.
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An Efficient Algorithm for Minimizing Ordered Norms in Fractional Load Balancing
A randomized (1+ε)-approximation algorithm for ordered-norm load balancing uses O((n+d)(ε^{-2} + log log d) log(n+d)) linear-oracle calls via follow-the-regularized-leader prices and martingale progress analysis.