ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
Altschuler and Sinho Chewi
3 Pith papers cite this work. Polarity classification is still indexing.
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A stochastic Runge-Kutta Schrödinger-Föllmer sampler (SRKSFS) is introduced with a proven O(h^{3/2} |ln h|) convergence rate in L2-Wasserstein distance, extended to data-driven sampling from empirical measures.
New RSLMC sampling algorithms achieve uniform-in-time W2 error bounds of order O(sqrt(d) h) under gradient Lipschitz and log-Sobolev assumptions, with modified versions for superlinear gradient growth and supporting numerical examples.
citing papers explorer
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Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
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Accelerated Schr\"odinger-F\"ollmer samplers
A stochastic Runge-Kutta Schrödinger-Föllmer sampler (SRKSFS) is introduced with a proven O(h^{3/2} |ln h|) convergence rate in L2-Wasserstein distance, extended to data-driven sampling from empirical measures.
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When Langevin Monte Carlo Meets Randomization: New Sampling Algorithms with Non-asymptotic Error Bounds beyond Log-Concavity and Gradient Lipschitzness
New RSLMC sampling algorithms achieve uniform-in-time W2 error bounds of order O(sqrt(d) h) under gradient Lipschitz and log-Sobolev assumptions, with modified versions for superlinear gradient growth and supporting numerical examples.