An efficient black-box reduction from PQ to TDS learning for any Boolean concept class in the distribution-free setting implies hardness for TDS learning of halfspaces, while membership queries enable efficient PQ learning of halfspaces via iterative Forster transforms.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
Develops Stein's method for the Wishart distribution via diffusion generators and semigroups, with applications to approximation bounds, Satterthwaite approximations, inequalities, and parameter estimation.
Deterministic (1+ε)-approximation algorithm for the volume of the unit hypercube truncated by k sums-of-univariate-convex constraints, running in poly_k(n, 1/ε, L, L_o) time.
Polynomial-time algorithm samples the Sherrington-Kirkpatrick Gibbs measure at beta < 1/2 with o(1) TVD error by combining potential Hessian ascent, stochastic localization, covariance estimates, and Jarzynski equality with rejection sampling.
Lower bounds are derived for the ground-state eigenvalues of -Δ + R/4 + V and the drifted operator -Δ_f + V on Ricci shrinkers, in terms of integrals involving V and the shrinker entropy, with generalizations via Perelman's μ-functional.
citing papers explorer
-
Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift
An efficient black-box reduction from PQ to TDS learning for any Boolean concept class in the distribution-free setting implies hardness for TDS learning of halfspaces, while membership queries enable efficient PQ learning of halfspaces via iterative Forster transforms.
-
Stein's method for the Wishart distribution
Develops Stein's method for the Wishart distribution via diffusion generators and semigroups, with applications to approximation bounds, Satterthwaite approximations, inequalities, and parameter estimation.
-
Deterministic Volume Estimation of Truncated Hypercubes
Deterministic (1+ε)-approximation algorithm for the volume of the unit hypercube truncated by k sums-of-univariate-convex constraints, running in poly_k(n, 1/ε, L, L_o) time.
-
Potential Hessian Ascent III: Sampling the Sherrington--Kirkpatrick Model at Beta < 1/2
Polynomial-time algorithm samples the Sherrington-Kirkpatrick Gibbs measure at beta < 1/2 with o(1) TVD error by combining potential Hessian ascent, stochastic localization, covariance estimates, and Jarzynski equality with rejection sampling.
-
Eigenvalue Estimates for Schr\"odinger Operators on Ricci Shrinkers
Lower bounds are derived for the ground-state eigenvalues of -Δ + R/4 + V and the drifted operator -Δ_f + V on Ricci shrinkers, in terms of integrals involving V and the shrinker entropy, with generalizations via Perelman's μ-functional.