arXiv research

The paper discusses rigidity results for inequalities on weighted Riemannian manifolds.

In this paper, we derive from the supersymmetry of the Witten Laplacian Brascamp-Lieb's type inequalities for general differential forms on compact Riemannian manifolds with boundary. In addition to the supersymmetry, our results essentially follow from suitable decompositions of the quadratic forms associated with the…

This work improves information concentration for exp-concave distributions, making it dimension-independent.

The paper explores inequalities on weighted Riemannian manifolds with boundary.

It is known that by dualizing the Bochner-Lichnerowicz-Weitzenböck formula, one obtains Poincaré-type inequalities on Riemannian manifolds equipped with a density, which satisfy the Bakry-Émery Curvature-Dimension condition (combining a lower bound on its generalized Ricci curvature and an upper bound on its generalize…

The paper explores a generalized notion of transversality in harmonic analysis.

Given a probability measure $μ$ supported on a convex subset $Ω$ of Euclidean space $(\mathbb{R}^d,g_0)$, we are interested in obtaining Poincaré and log-Sobolev type inequalities on $(Ω,g_0,μ)$. To this end, we change the metric $g_0$ to a more general Riemannian one $g$, adapted in a certain sense to $μ$, and perform…

In this article, a proof of the interpolation inequality along geodesics in $p$-Wasserstein spaces is given. This interpolation inequality was the main ingredient to prove the Borel-Brascamp-Lieb inequality for general Riemannian and Finsler manifolds and led Lott-Villani and Sturm to define an abstract Ricci curvature…

Standard bubbles and partitions are stable in various model spaces.

The paper proves inequalities for optimal transport on sub-Finslerian manifolds.

The paper proves inequalities for hyperbolic sets and curves.

We prove that ideal sub-Riemannian manifolds (i.e., admitting no non-trivial abnormal minimizers) support interpolation inequalities for optimal transport. A key role is played by sub-Riemannian Jacobi fields and distortion coefficients, whose properties are remarkably different with respect to the Riemannian case. As …

Sharp inequalities for matrix means with unknown variance.

New algorithms reduce variance in solving complex mathematical problems.

The paper provides concentration inequalities for Markov chain variance estimators.

The paper proposes a conjecture for a symmetric version of Ehrhard's inequality.

New algorithms solve stochastic variational inequalities without bounded variance assumption.

Empirical Bayes method improves Gaussian sequence model inference.

Paper tackles unknown variances in best-arm identification.

The paper extends confidence sequences for infinite variance data.

We analyze the probabilistic variance of a solution of Liouville's equation for curvature, given suitable bounds on the Gaussian curvature. The related systolic geometry was recently studied by Horowitz, Katz, and Katz, where we obtained a strengthening of Loewner's torus inequality containing a "defect term", similar …

By adapting methods of \cite{AC} we prove a sharp estimate on the expansion modulus of the gradient of the log of the parabolic kernel to the Schördinger operator with convex potential, which improves an earlier work of Brascamp-Lieb. We also include alternate proofs to the improved log-concavity estimate, and to the f…

The paper generalizes Bayesian Cramér-Rao inequality using information geometry of relative α-entropy.

Introduces MWLD to measure loss inequality across groups.

Generalization in nonlinear least squares can be studied via algorithmic stability and effective dimension.

We show that Bonnesen's isoperimetic defect has a systolic analog for Loewner's torus inequality. The isosystolic defect is expressed in terms of the probabilistic variance of the conformal factor of the metric g with respect to the flat metric of unit area in the conformal class of g.

Simplified analysis of diffusion models using discrete random variables.

Optimal control strategies modify wealth inequality variance.

The paper analyzes sparse high-dimensional linear regression with random design and unknown error variance, providing adaptiveness and concentration rates.

Sharp concentration inequalities for sub-Orlicz random variables with phase transition at α=2.

We obtain a sharp lower bound on the isoperimetric deficit of a general polygon in terms of the variance of its side lengths, the variance of its radii, and its deviation from being convex. Our technique involves a functional minimization problem on a suitably constructed compact manifold and is based on the spectral t…

Paper defines Fisher co-metric on cotangent bundle and clarifies its relation to variance.

Paper improves convergence rate of Langevin Dynamics algorithms.

New concentration inequality for U-statistics of Markov chains.

New algorithm reduces regret for linear bandits with unknown noise variance.

Stochastic gradient algorithms estimate the gradient based on only one or a few samples and enjoy low computational cost per iteration. They have been widely used in large-scale optimization problems. However, stochastic gradient algorithms are usually slow to converge and achieve sub-linear convergence rates, due to t…

Adaptive kernel regression with streaming data and unknown variance.

This paper connects nonpositive sectional curvature of a Riemannian manifold with the displacement convexity of the variance functional on the space $P(M)$ of probability measures over $M$. We show that $M$ has nonpositive sectional curvature and has trivial topology (i.e, is homeomorphic to $\mathbf{R}^n$) if and only…

We give improved constants for data dependent and variance sensitive confidence bounds, called empirical Bernstein bounds, and extend these inequalities to hold uniformly over classes of functionswhose growth function is polynomial in the sample size n. The bounds lead us to consider sample variance penalization, a nov…

We study random walks on groups with the feature that, roughly speaking, successive positions of the walk tend to be "aligned". We formalize and quantify this property by means of the notion of deviation inequalities. We show that deviation inequalities have several consequences including Central Limit Theorems, the lo…

We prove new concentration inequalities for random variables.

New inequality for ternary variables improves on existing measures.

In 1963, Polyak proposed a simple condition that is sufficient to show a global linear convergence rate for gradient descent. This condition is a special case of the Łojasiewicz inequality proposed in the same year, and it does not require strong convexity (or even convexity). In this work, we show that this much-older…

Integrates prediction models into portfolio optimization for better asset allocation.

A new matrix concentration inequality for random products of matrices.

Proposes counterfactual explainability for causal attribution, extending variance analysis methods.

We give tight concentration bounds for mixtures of martingales that are simultaneously uniform over (a) mixture distributions, in a PAC-Bayes sense; and (b) all finite times. These bounds are proved in terms of the martingale variance, extending classical Bernstein inequalities, and sharpening and simplifying prior wor…

Unified framework for variance reduction to solve monotone operator problems.