ZOPPA at fixed positive temperature converges under minimal assumptions by acting as an exact proximal point method on a smoothed objective, with explicit connections back to the original function and convergence for its sampled version.
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A new diagnostic reveals that L=2 equivariant force field backbones preserve frequencies up to l=4 but collapse at l=5 on aspirin, consistent with a finite-degree span theorem and controls.
A direct plug-in kernel estimator for Schrödinger bridge time-series drifts achieves uniform non-asymptotic bounds, pointwise CLT under undersmoothing, and minimax-rate optimal adaptive selection.
SOCKET replaces hard LSH bucket matches with soft probabilistic collision aggregation to enable efficient, high-quality token selection for sparse attention, matching or exceeding prior methods with up to 1.5x throughput gains.
citing papers explorer
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Convergence of zeroth-order proximal point algorithms in the high-temperature regime
ZOPPA at fixed positive temperature converges under minimal assumptions by acting as an exact proximal point method on a smoothed objective, with explicit connections back to the original function and convergence for its sampled version.
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Diagnosing Spectral Ceilings in Equivariant Neural Force Fields
A new diagnostic reveals that L=2 equivariant force field backbones preserve frequencies up to l=4 but collapse at l=5 on aspirin, consistent with a finite-degree span theorem and controls.
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Direct Estimation of Schr\"odinger Bridge Time-Series Drifts: Finite-Sample, Asymptotic, and Adaptive Guarantees
A direct plug-in kernel estimator for Schrödinger bridge time-series drifts achieves uniform non-asymptotic bounds, pointwise CLT under undersmoothing, and minimax-rate optimal adaptive selection.
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SOCKET: SOft Collision Kernel EsTimator for Sparse Attention
SOCKET replaces hard LSH bucket matches with soft probabilistic collision aggregation to enable efficient, high-quality token selection for sparse attention, matching or exceeding prior methods with up to 1.5x throughput gains.