This paper develops a framework for conformal prediction in dyadic regression problems under complex missingness mechanisms.

The paper introduces a computational framework using Hodge zero-modes to track the geometry of topological features in parameter-dependent data, providing metrics like curvature and holonomy to quanti…

The paper introduces a subgrid marching tetrahedra scheme that accurately recovers complex, intersection-free manifold meshes from tetrahedral grids, overcoming limitations of classic marching methods…

This paper presents an algorithmic framework for exhaustively generating and tabulating knot and link diagrams on the thickened torus.

The paper proves the real-rootedness and ultra-log-concavity of the Poincaré polynomials for the moduli space $\overline{\mathcal M}_{0,n}$ and the Fulton--MacPherson space $\mathbb{P}^1[n]$ using a n…

The paper systematically studies the trade-offs between the number of slopes, bends per edge, and required area for planar drawings of bounded-degree graphs, providing new constructions for high-degre…

The paper proposes a semi-relaxed Gromov-Wasserstein objective to estimate the latent connectivity structure of large-scale networks, achieving statistically consistent and efficient recovery of the u…

The paper proposes Personalized Federated Weighted Conformal Prediction (PFWCP), a novel framework that ensures statistically valid uncertainty quantification in multi-agent, heterogeneous settings wh…

The paper presents a novel and significantly faster algorithm for computing the second discrete homology group of a graph by identifying five basic quotient shapes of the 3-cube.

The paper introduces Cellular Sheaf Neural Operators, a discretization-aware framework that models constrained PDEs by representing physical states on oriented cell complexes to enforce structure-pres…

LoRe is a training-free wrapper that dynamically budgets interaction evaluation at each step of graph solvers, significantly improving scalability and speed while maintaining solution quality.

This paper settles the complexity of three sketching problems in graphs and distributions.

This paper derives multivariate generating functions to refine the enumeration of Fibonacci polyominoes.

The paper introduces Accordion, an end-to-end framework that significantly improves the efficiency of compiling fermionic Hamiltonians into quantum circuits for simulation on constrained quantum hardw…

The paper introduces a diffusion-based uncertainty model for robust optimization on graphs, showing that the resulting computational complexity depends critically on the interaction between the uncert…

The paper uses majorization theory to analyze lattice reduction, showing that local swaps smooth the Gram-Schmidt profile and deriving variational and telescoping identities for the worst-case profile…

The paper proposes Latent Geometric Chords (LGC) and LGC-H, a novel method that navigates decision boundaries using curvature-aware geometric search within a semantic manifold to generate high-fidelit…

The paper investigates applying Riemannian optimization techniques to low-rank matrix parameters for deep learning, but finds that the proposed methods do not conclusively outperform the AdamW baselin…

The paper proposes using pseudo-sensitivities, derived from adjoint sensitivity fields, as an optimal conditioning signal in a Bernoulli flow-matching framework to significantly improve the out-of-dis…

The paper proposes sampling directly from approximations of an LLM posterior, conditioned on high-scoring regions, to generate more coherent and useful text compared to existing post-hoc hallucination…