arXiv:2509.20586v1 Announce Type: cross Abstract: Randomized controlled trials (RCTs) are widely regarded as the gold standard for causal inference in biomedical research. For instance, when estimating the average treatment effect on the treated (ATT), a doubly robust estimation procedure can…
arXiv:2509.20508v1 Announce Type: new Abstract: We address the problem of efficiently computing Wasserstein distances for multiple pairs of distributions drawn from a meta-distribution. To this end, we propose a fast estimation method based on regressing Wasserstein distance on sliced Wasserstein…
arXiv:2509.20678v1 Announce Type: cross Abstract: Optimal transport (OT) is a widely used technique in machine learning, graphics, and vision that aligns two distributions or datasets using their relative geometry. In symmetry-rich settings, however, OT alignments based solely on pairwise geometric…
arXiv:2509.20721v1 Announce Type: cross Abstract: Scaling laws, a defining feature of deep learning, reveal a striking power-law improvement in model performance with increasing dataset and model size. Yet, their mathematical origins, especially the scaling exponent, have remained elusive. In this…
arXiv:2303.05838v3 Announce Type: replace-cross Abstract: In this paper, we establish novel concentration inequalities for additive functionals of geometrically ergodic Markov chains similar to Rosenthal inequalities for sums of independent random variables. We pay special attention to the dependence of our…
arXiv:2509.20993v1 Announce Type: cross Abstract: We consider the problem of estimating inverse temperature parameter $beta$ of an $n$-dimensional truncated Ising model using a single sample. Given a graph $G = (V,E)$ with $n$ vertices, a truncated Ising model is a…
arXiv:2405.13844v4 Announce Type: replace-cross Abstract: Estimating joint distributions (a.k.a. couplings) over counterfactual outcomes is central to personalized decision-making and treatment risk assessment. Two emergent frameworks with identifiability guarantees are: (i) bijective structural causal models (SCMs), which are flexible but brittle…
arXiv:2509.21021v1 Announce Type: cross Abstract: Constraint-based causal discovery relies on numerous conditional independence tests (CITs), but its practical applicability is severely constrained by the prohibitive computational cost, especially as CITs themselves have high time complexity with respect to the sample…
arXiv:2509.21172v1 Announce Type: cross Abstract: Inverse reinforcement learning (IRL) aims to explain observed behavior by uncovering an underlying reward. In the maximum-entropy or Gumbel-shocks-to-reward frameworks, this amounts to fitting a reward function and a soft value function that together satisfy…
arXiv:2509.21181v1 Announce Type: cross Abstract: For overparameterized linear regression with isotropic Gaussian design and minimum-$ell_p$ interpolator $pin(1,2]$, we give a unified, high-probability characterization for the scaling of the family of parameter norms $ \{ lVert widehat{w_p} rVert_r \}_{r in [1,p]}…
