Simulator access for dependent data recovers i.i.d.-style VC bounds and enables a universal no-regret algorithm for time-bounded samplable processes.
Altschuler and Kunal Talwar
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
Optimal rates for non-log-concave sampling and log-partition estimation are sometimes equal to or faster than optimization rates, but polynomial-time algorithms fall short of near-optimal performance.
A proximal gradient sampler for composite log-concave distributions achieves near-optimal iteration complexity of order kappa sqrt(d) log^4(1/epsilon) in total variation distance under strong convexity and smoothness.
citing papers explorer
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Learning with Simulators: No Regret in a Computationally Bounded World
Simulator access for dependent data recovers i.i.d.-style VC bounds and enables a universal no-regret algorithm for time-bounded samplable processes.
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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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Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation
Optimal rates for non-log-concave sampling and log-partition estimation are sometimes equal to or faster than optimization rates, but polynomial-time algorithms fall short of near-optimal performance.
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A proximal gradient algorithm for composite log-concave sampling
A proximal gradient sampler for composite log-concave distributions achieves near-optimal iteration complexity of order kappa sqrt(d) log^4(1/epsilon) in total variation distance under strong convexity and smoothness.