5479 papers
This paper solves the Dirichlet problem for minimal Killing graphs over non-compact domains in 3-manifolds fibered by a Killing vector field, establishing Collin-Krust type estimates, proving uniqueness results in the Heisenberg group, and demonstrating the removability of isolated singularities.
This paper proposes and analyzes a simultaneous M-estimator for detecting a change point and recovering diffusivity values in a stochastic heat equation with space-dependent diffusivity, demonstrating that the change point converges at rate while diffusivity constants converge at rate based on local spatial measurements.
This paper establishes canonical bundle formulas for fibrations of curves with arithmetic genus one in characteristic , distinguishing between separable and inseparable cases, and applies these results to prove that a klt pair with a nef anti-log canonical divisor and a relative dimension one Albanese morphism is a fiber space over its Albanese variety.
This paper employs an inverse optimization framework on NFL play-by-play data to demonstrate that coaches' historically conservative fourth-down decisions are consistent with optimizing low quantiles of future value, revealing that their risk preferences have become more tolerant over time and vary based on field position.
This paper establishes the strong convergence rate of an Euler-Maruyama numerical scheme for one-dimensional stochastic differential equations with distributional drifts in Hölder-Zygmund spaces, supported by theoretical bounds and numerical implementation.
This paper establishes that homogenisation and renormalisation commute for the two-dimensional periodic generalised parabolic Anderson model by introducing a novel solution ansatz to handle variable coefficients and demonstrating that the standard model can be constructed via para-controlled calculus without commutator estimates.
This paper constructs analogs of Khovanov-Jacobsson classes and the Rasmussen invariant for links in the boundary of smooth oriented 4-manifolds by utilizing skein lasagna modules derived from equivariant and deformed link homology, while establishing non-vanishing results, decomposition theorems, and conditions for extending functoriality to immersed cobordisms.
This paper proposes two novel single-loop zeroth-order primal-dual algorithms, ZO-PDAPG and ZO-RMPDPG, that achieve state-of-the-art iteration complexity guarantees for solving nonconvex-(strongly) concave minimax problems with coupled linear constraints under both deterministic and stochastic settings.
This paper proves a Gersten-type conjecture for étale sheaves, including étale logarithmic Hodge-Witt sheaves and -adic Tate twists, over henselian local rings of normal crossing varieties in positive characteristic, and applies this result to establish a relative version of the conjecture for -adic étale Tate twists over semistable families in mixed characteristic as well as a generalization of Artin's theorem on Brauer groups.
This paper introduces Orthonormal Data Collaboration (ODC), a method that enforces orthonormal bases to transform the alignment challenge into a closed-form Orthogonal Procrustes problem, thereby achieving orthogonal concordance, significantly reducing computational complexity, and improving accuracy without compromising privacy or communication efficiency.
This paper extends Control Lyapunov Value Functions to disturbed nonlinear systems by defining the Robust CLVF (R-CLVF) to identify the smallest robust control invariant set and guarantee exponential stabilization, while addressing computational challenges through warmstart and system decomposition techniques.
This paper introduces a complex of cochains known as -fractional charges to define the exterior product of Hölder differential forms, thereby extending the Young integral to arbitrary dimensions and codimensions.
This paper proposes and analyzes stochastic greedy algorithms (MRG, DRG, and Random-WSSA) to efficiently solve budget- and performance-constrained weak submodular sensor selection problems with robustness guarantees, demonstrating their effectiveness in Earth-observing satellite constellation applications.
This paper proposes a novel multi-task subset selection framework that achieves localized distributional robustness by introducing a relative-entropy regularization term, which is proven equivalent to maximizing a monotone composition of submodular functions and can be efficiently solved via greedy algorithms, as validated by experiments on satellite sensor selection and image summarization.
This paper establishes an analogue of Kaplansky's theorem for models of by introducing -bounded weakly immediate types to prove that every such model possesses a unique --spherical completion, thereby demonstrating that is definably spherically complete.
This paper establishes sharp Fourier restriction estimates for certain degenerate higher codimensional quadratic surfaces by introducing a novel iterative broad-narrow analysis that overcomes the failure of rescaling invariance through a generalized Jacobian defined via algebraic and graph-theoretic tools.
This paper investigates infinite -indexed complexes of spaces equipped with multilinear products to derive hierarchies of differential identities and closed multiple products, ultimately demonstrating how maximal differential orders, powers, and coherence conditions generate families of multi-graded differential algebras.
This paper proposes a graph signal processing framework for reversible Markov chains that defines optimal graph filters—specifically Bernstein, Chebyshev, and Legendre filters—to significantly accelerate the convergence of ergodic averages compared to traditional methods.
This paper characterizes the necessary and sufficient conditions for the least squares estimator to be minimax optimal in a convex constrained Gaussian sequence model by linking optimality to the Lipschitz property of the local Gaussian width, while providing algorithms to compute worst-case risk and demonstrating these results across various geometric sets.
This paper establishes that the value function of infinite-dimensional singular stochastic control problems in Hilbert spaces is a -viscosity solution to a variational inequality and satisfies a second-order smooth-fit principle in the controlled direction under specific spectral conditions, by leveraging connections to optimal stopping and techniques from convex and viscosity theory.