arXiv research

New method for private learning with public features improves convergence rates.

New method uses public data to achieve optimal nonparametric classification with privacy constraints.

We propose a new indicator for technical analysis. The indicator emphasizes maximums and minimums in price series with inherent smoothing and has a potential to be useful in both mechanical trading rules and chart pattern analysis.

This paper analyzes divide-and-conquer estimators for functional linear regression without assuming target function in the RKHS.

The paper explores heat flow and constants on graphs, proving properties and proposing new concepts.

Standard adversarial training involves two agents, namely a generator and a discriminator, playing a mini-max game. However, even if the players converge to an equilibrium, the generator may only recover a part of the target data distribution, in a situation commonly referred to as mode collapse. In this work, we prese…

Novel unsupervised MIG detectors improve signal detection in cluttered environments.

This work analyzes how overparameterization aids GANs in reaching global saddle points.

SGD improves DR by solving two-stage sampling problems.

We consider the off-policy estimation problem of estimating the expected reward of a target policy using samples collected by a different behavior policy. Importance sampling (IS) has been a key technique to derive (nearly) unbiased estimators, but is known to suffer from an excessively high variance in long-horizon pr…

We present a new algorithm to train a robust neural network against adversarial attacks. Our algorithm is motivated by the following two ideas. First, although recent work has demonstrated that fusing randomness can improve the robustness of neural networks (Liu 2017), we noticed that adding noise blindly to all the la…

New algorithm improves bandit with graph feedback by decomposing regret.

GAN approach optimizes investment under market uncertainty.

Active learning aims to develop label-efficient algorithms by sampling the most representative queries to be labeled by an oracle. We describe a pool-based semi-supervised active learning algorithm that implicitly learns this sampling mechanism in an adversarial manner. Unlike conventional active learning algorithms, o…

Outlier detection is an important topic in machine learning and has been used in a wide range of applications. In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution. However, due to the sparsity of data in high-dimensional spac…

The paper studies adversarial training for linear regression models.

In binary classification problems, mainly two approaches have been proposed; one is loss function approach and the other is uncertainty set approach. The loss function approach is applied to major learning algorithms such as support vector machine (SVM) and boosting methods. The loss function represents the penalty of …

Proves weak convergence equals mean convergence in GGC.

This is an intuitive survey of extrinsic and intrinsic notions of convergence of manifolds complete with pictures of key examples and a discussion of the properties associated with each notion. We begin with a description of three extrinsic notions which have been applied to study sequences of submanifolds in Euclidean…

The abstract discusses convergence properties of Lipschitz functions and sets defined by equations.

Study shows intrinsic timed Hausdorff convergence leads to Gromov-Hausdorff and big bang convergence.

Studied SGD convergence under weak conditions.

Study on convergence rate of $Q$-curvature flow in 6 dimensions.

The objective of this paper is to introduce the notion of generalized almost statistical (briefly, GAS) convergence of bounded real sequences, which generalizes the notion of almost convergence as well as statistical convergence of bounded real sequences. As a special kind of Banach limit functional, we also introduce …

Establishes geometric convergence of iterative optimization algorithms.

Uniform counting formulas for orthogeodesics in Kleinian groups converge.

The paper explores null distance convergence for warped product spacetimes.

New quasi-Newton method guarantees global superlinear convergence.

We investigate finite-time decoupled convergence in nonlinear two-time-scale stochastic approximation.

The article introduces a new convergence concept for Lorentzian spaces and applies it to generalized cones.

AdaBoost's classifier and margins converge to a known value.

Study shows gap between uniform convergence and test error in random feature models.

The Sinkhorn-Knopp derivatives converge with linear rate.

The paper examines convergence of distances in Lipschitz structures on manifolds.

We introduce a natural definition of $L^p$-convergence of maps, $p \ge 1$, in the case where the domain is a convergent sequence of measured metric space with respect to the measured Gromov-Hausdorff topology and the target is a Gromov-Hausdorff convergent sequence. With the $L^p$-convergence, we establish a theory of …

We prove a criterion of convergence in the augmented Teichmueller space that can be phrased in terms of convergence of the hyperbolic metrics or of quasiconformal convergence away from the nodes.

We develop a new approach to geometric quantization using the theory of convergence of metric measure spaces. Given a family of Kähler polarizations converging to a non-singular real polarization on a prequantized symplectic manifold, we show the spectral convergence result of $\bar{\partial}$-Laplacians, as well as th…

Sharp convergence theorem for sphere submanifolds proved.

DCDC calculates convergence rates for Markov chains using neural networks.

Proves curvature tensor convergence for smoothable spaces.

Develops clustering methods based on likelihood and convergence proved.

New technique explains convergence in ML models with data modifications.

Characterizes problems solvable via linear convergence algorithms.

Equivalence shown between two mathematical concepts for hyperbolic surfaces.

The paper provides convergence guarantees for multicalibration gradient boosting.

Study Gromov-Hausdorff convergence of metric pairs and tuples.

Study on convergence rate of Bergman metrics on Kähler manifolds.

New proof shows coupling-based flows converge linearly to diagonalize data covariance.