arXiv research

New method proves dimension-free convergence for ULD in KL divergence.

This work analyzes Gibbs samplers for Bayesian hierarchical models without dimensionality constraints.

New method bounds high-dimensional regression without estimating design covariance.

New rates for GLD and SGLD in infinite-dimensional spaces without dimensionality issues.

Logistic regression gets a new, simpler uniform bound.

On a closed weighted Riemannian manifold with nonnegative Bakry-Émery Ricci curvature, it is shown that the ratio of the $k$-th to first eigenvalues of the weighted Laplacian is dominated by $641k^2$, using an argument via the Cheeger constant. While improving the previous exponential upper bound, the order of $k$ here…

By using an explicit Bellman function, we prove a bilinear embedding theorem for the Laplacian associated with a weighted Riemannian manifold $(M,μ_φ)$ having the Bakry-Emery curvature bounded from below. The embedding, acting on the cartesian product of $L^p(M,μ_φ)$ and $L^q(T^*M,μ_φ)$, $1/p+1/q=1$, involves estimates…

This paper analyzes neural network classifiers' performance in binary classification.

In this paper, the author discusses the elliptic type gradient estimate for the solution of the time-dependent Schrödinger equations on noncompact manifolds. As its application, the dimension-free Harnack inequality and the Liouville type theorem for the Schrödinger equation are proved.

Unified framework for convergence of discrete diffusion models without state space size dependence.

Sharp inequality in spaces with non-negative Ricci curvature.

Graphs with nonnegative Bakry-Émery curvature have volume doubling and Poincaré inequalities.

Algorithm achieves optimal pricing with minimal exploration for dynamic markets.

Unified bounds for iterative algorithms with Gaussian data matrices.

New bounds for high-dimensional sparse linear bandits, balancing information and regret.

The paper derives Harnack inequalities for evolving Riemannian manifolds without dimensionality restrictions.

New algorithms achieve decision calibration without sample complexity dependent on feature dimension.

New error bound for diffusion models without dimensionality constraints.

The paper provides generalization bounds for metric learning using neural network embeddings.

The study assesses ML model robustness under worst-case subpopulations.

New algorithm reduces complexity in multi-agent reinforcement learning.

We develop time-uniform confidence spheres for estimating means of random vectors.

We improve bounds for stochastic processes, especially those with heavy tails.

In this paper, we prove the Li-Yau type Harnack inequality and Hamilton type dimension free Harnack inequality for the heat equation $\partial_t u=Lu$ associated with the time dependent Witten Laplacian on complete Riemannian manifolds equipped with a variant of the $(K, m)$-super Perelman Ricci flows and the $K$-super…

The goal of the paper is to sharpen and generalise bounds involving the Cheeger's isoperimetric constant $h$ and the first eigenvalue $λ_{1}$ of the Laplacian. A celebrated lower bound of $λ_{1}$ in terms of $h$, $λ_{1}\geq h^{2}/4$, was proved by Cheeger in 1970 for smooth Riemannian manifolds. An upper bound on $λ_{1…

We study Smoothed Online Convex Optimization, a version of online convex optimization where the learner incurs a penalty for changing her actions between rounds. Given a $Ω(\sqrt{d})$ lower bound on the competitive ratio of any online algorithm, where $d$ is the dimension of the action space, we ask under what conditio…

The paper introduces a new method for tail bounds of random vectors and matrices.

The paper uses deep neural networks to estimate and infer ATE without needing to know the dimension of the data.

Improved diffusion models for generative tasks without dimensionality constraints.

We derive exponential tail inequalities for sums of random matrices with no dependence on the explicit matrix dimensions. These are similar to the matrix versions of the Chernoff bound and Bernstein inequality except with the explicit matrix dimensions replaced by a trace quantity that can be small even when the dimens…

Study shows high-dimensional sparse RL hardness and Lasso Q-iteration's nearly dimension-free regret.

Paper improves Frank-Wolfe algorithm's efficiency bounds.

We study the regret minimization problem in the novel setting of generalized kernelized bandits (GKBs), where we optimize an unknown function $f^*$ belonging to a reproducing kernel Hilbert space (RKHS) having access to samples generated by an exponential family (EF) reward model whose mean is a non-linear function $μ(…

The goal of predictive sparse coding is to learn a representation of examples as sparse linear combinations of elements from a dictionary, such that a learned hypothesis linear in the new representation performs well on a predictive task. Predictive sparse coding algorithms recently have demonstrated impressive perform…

TailCoR measures co-movement of financial crises events.

E-ROBOT improves robust statistics and ML via Schrödinger bridge theory.

Optimal sampling bounds for various classification losses under different regularization terms.

This paper presents competitive algorithms for a novel class of online optimization problems with memory. We consider a setting where the learner seeks to minimize the sum of a hitting cost and a switching cost that depends on the previous $p$ decisions. This setting generalizes Smoothed Online Convex Optimization. The…

HAR regression improves performance on small datasets.

New private mean estimation method works well for anisotropic data.

In this paper, we propose a new adaptive stochastic gradient Langevin dynamics (ASGLD) algorithmic framework and its two specialized versions, namely adaptive stochastic gradient (ASG) and adaptive gradient Langevin dynamics(AGLD), for non-convex optimization problems. All proposed algorithms can escape from saddle poi…

Let $(X,d,μ)$ be a $RCD^\ast(K, N)$ space with $K\in \mathbb{R}$ and $N\in [1,\infty]$. For $N\in [1,\infty)$, we derive the upper and lower bounds of the heat kernel on $(X,d,μ)$ by applying the parabolic Harnack inequality and the comparison principle, and then sharp bounds for its gradient, which are also sharp in t…

We break dimension dependence in sparse distribution estimation with communication constraints.

Skeleton clustering detects clusters in high-dimensional data without needing prototypes.

We show how to take any two parameter-free online learning algorithms with different regret guarantees and obtain a single algorithm whose regret is the minimum of the two base algorithms. Our method is embarrassingly simple: just add the iterates. This trick can generate efficient algorithms that adapt to many norms s…

In this paper, we study the conjecture of Gardner and Zvavitch from \cite{GZ}, which suggests that the standard Gaussian measure $γ$ enjoys $\frac{1}{n}$-concavity with respect to the Minkowski addition of \textbf{symmetric} convex sets. We prove this fact up to a factor of 2: that is, we show that for symmetric convex…

We consider minimizing a nonconvex, smooth function $f$ on a Riemannian manifold $\mathcal{M}$. We show that a perturbed version of Riemannian gradient descent algorithm converges to a second-order stationary point (and hence is able to escape saddle points on the manifold). The rate of convergence depends as $1/ε^2$ o…

Information concentration of probability measures have important implications in learning theory. Recently, it is discovered that the information content of a log-concave distribution concentrates around their differential entropy, albeit with an unpleasant dependence on the ambient dimension. In this work, we prove th…