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What is Learnable in Valiant's Theory of the Learnable? Learning Perturbations to Extrapolate Your LLM Byzantine-Robust Distributed Sparse Learning Revisited The Sample Complexity of Multiple Change Point Identification under Bandit Feedback A proximal gradient algorithm for composite log-concave sampling Model-based Bootstrap of Controlled Markov Chains Approximation of Maximally Monotone Operators : A Graph Convergence Perspective Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound A Spectral Framework for Closed-Form Relative Density Estimation Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation Scaling Limits of Long-Context Transformers A Note on Non-Negative $L_1$-Approximating Polynomials Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning Linear Response Estimators for Singular Statistical Models Statistical inference with belief functions: A survey Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity Adaptive auditing of AI systems with anytime-valid guarantees Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions Risk-Controlled Post-Processing of Decision Policies Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment Time-Inhomogeneous Preconditioned Langevin Dynamics A Fine-Grained Understanding of Uniform Convergence for Halfspaces CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency Ratio-based Loss Functions Optimal Confidence Band for Kernel Gradient Flow Estimator A renormalization-group inspired lattice-based framework for piecewise generalized linear models
Left-Inverses of Fractional Laplacian and Sparse Stochast...
Qiyu Sun, Michael Unser · 2010-09-14 · via math.ST updates on arXiv.org

The fractional Laplacian $(-\triangle)^{γ/2}$ commutes with the primary coordination transformations in the Euclidean space $\RR^d$: dilation, translation and rotation, and has tight link to splines, fractals and stable Levy processes. For $0<γ<d$, its inverse is the classical Riesz potential $I_γ$ which is dilation-invariant and translation-invariant. In this work, we investigate the functional properties (continuity, decay and invertibility) of an extended class of differential operators that share those invariance properties. In particular, we extend the definition of the classical Riesz potential $I_γ$ to any non-integer number $γ$ larger than $d$ and show that it is the unique left-inverse of the fractional Laplacian $(-\triangle)^{γ/2}$ which is dilation-invariant and translation-invariant. We observe that, for any $1\le p\le \infty$ and $γ\ge d(1-1/p)$, there exists a Schwartz function $f$ such that $I_γf$ is not $p$-integrable. We then introduce the new unique left-inverse $I_{γ, p}$ of the fractional Laplacian $(-\triangle)^{γ/2}$ with the property that $I_{γ, p}$ is dilation-invariant (but not translation-invariant) and that $I_{γ, p}f$ is $p$-integrable for any Schwartz function $f$. We finally apply that linear operator $I_{γ, p}$ with $p=1$ to solve the stochastic partial differential equation $(-\triangle)^{γ/2} Φ=w$ with white Poisson noise as its driving term $w$.