Multi-variable conformal prediction extends calibration to vector-valued scores with multiple variables, removing data splitting while preserving coverage and yielding smaller, more stable prediction sets.
A Schur-decomposition-based projection ensures asymptotic stability in state-space neural networks with minimal overparameterization, matching state-of-the-art accuracy and convergence.
The paper studies an interested seller algorithmically selling information to budget-constrained buyers to maximize revenue and induce desirable actions, proving optimal menu protocols are polynomial-time computable and analyzing single-policy restrictions.
Sech perturbation kernels make calibration functions analytic, enabling polynomial regression to estimate second-order calibration error at the minimax optimal rate of tilde O(1/sqrt(n)). This yields the first finite-sample guarantee for second-order Platt scaling and a bucket-free calibration defin
Online resource allocation with budget and general constraints achieves near-optimal dynamic regret and bounded violations via weakly adaptive Lagrangian analysis.
A robust binary-search pricing algorithm achieves regret scaling additively with corruption and logarithmically with time, resolving the open decoupling problem.
NoPo4D is the first feed-forward 4D Gaussian system that reconstructs dynamic scenes from unposed multi-view videos, outperforming baselines and optimization methods at much faster speeds.