Two auxiliary environments suffice to identify causal graphs and functional mechanisms in structural causal models under acyclicity and invariance assumptions, enabling correct counterfactual inference.
Estimation of Non-Normalized Statistical Models by Score Matching
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Higher-order Langevin dynamics reduce memorization in diffusion models by governing data trajectories with a low-pass-filtered score whose smoothness increases with order.
TriOpt recovers topological order via Sherman-Morrison downdates on linear kernels then solves a convex program for the DAG edges, claiming exact recovery under the true order and large speedups on high-dimensional data.
A learnable continuous perturbation framework for LLM token prefixes via latent vector transformations, optimized through unbiased estimating equations, yields gains in out-of-domain performance.
Training and sampling in static scalar energy generative models are two instances of the same Lyapunov-driven density transport dynamics on Wasserstein space, differing only by initial condition, which yields a finite stopping criterion for Langevin sampling and additive composition rules that keep
A unified framework for exponential tilting in diffusion and flow models that includes bias-variance decompositions showing finite gradient variance for some methods, norm bounds on adjoint ODEs, and adapted losses with new Crooks and Jarzynski identities.
LHSD estimates local intrinsic dimension in high-D spaces by spectral filtering of the log-density Hessian via SLQ to isolate zero-curvature tangent directions.
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