arXiv research

Paper proves integrability and entropy compactness for Kähler potentials with uniform log-log threshold.

The paper studies how arm selection in a bandit problem changes with shape constraints.

The so-called Pareto-Levy or power-law distribution has been successfully used as a model to describe probabilities associated to extreme variations of worldwide stock markets indexes data and it has the form $Pr(X>x) ~ x**(-alpha) for gamma< x <infinity. The selection of the threshold parameter gamma$ from empirical d…

The study finds effective lower bounds for spectra of random surfaces and bundles.

Sharp threshold found for aligning Gaussian-weighted graphs.

Study tests rough fractional volatility model across different time scales, revealing new volatility patterns.

KSS method converges and recovers correct clustering under certain conditions.

The paper explores the shape of filling-systole subspace in surface moduli space and critical points of systole function.

SVGD algorithm converges at rate 1/sqrt(log log n) for sub-Gaussian distributions.

New algorithm reduces switching costs in multinomial logit bandit problems.

The study proves a theorem about subword complexity for free group automorphisms.

Corrected graph convolutions improve node classification on graphs.

Sharp bound on smallest diameter of hyperbolic surfaces.

One-bit clustering method for two-component sub-Gaussian mixture models

If we pick $n$ random points uniformly in $[0,1]^d$ and connect each point to its $k-$nearest neighbors, then it is well known that there exists a giant connected component with high probability. We prove that in $[0,1]^d$ it suffices to connect every point to $c_{d,1} \log{\log{n}}$ points chosen randomly among its $…

New algorithms reduce matching regret by limiting frequent updates.

GN algorithm solves batched bandit for nondegenerate functions near-optimally.

Multivariate Poisson approximation of the length spectrum of random surfaces is studied by means of the Chen-Stein method. This approach delivers simple and explicit error bounds in Poisson limit theorems. They are used to prove that Poisson approximation applies to curves of length up to order $o(\log\log g)$ with $g$…

We study the problem of multi-agent reinforcement learning (MARL) with adaptivity constraints -- a new problem motivated by real-world applications where deployments of new policies are costly and the number of policy updates must be minimized. For two-player zero-sum Markov Games, we design a (policy) elimination base…

Study on adaptivity constraints in linear contextual bandits with optimal design.

Motivated by applications such as discovering strong ties in social networks and assembling genome subsequences in biology, we study the problem of recovering a hidden $2k$-nearest neighbor (NN) graph in an $n$-vertex complete graph, whose edge weights are independent and distributed according to $P_n$ for edges in the…

Self-directed learners can minimize mistakes in online classification.

The problem of estimating an unknown discrete distribution from its samples is a fundamental tenet of statistical learning. Over the past decade, it attracted significant research effort and has been solved for a variety of divergence measures. Surprisingly, an equally important problem, estimating an unknown Markov ch…

We study the minimax optimal rate for estimating the Wasserstein-$1$ metric between two unknown probability measures based on $n$ i.i.d. empirical samples from them. We show that estimating the Wasserstein metric itself between probability measures, is not significantly easier than estimating the probability measures u…

Improved approximation for socially fair clustering with $\ell_p$-objective.

We study the minimax optimal rates for estimating a range of Integral Probability Metrics (IPMs) between two unknown probability measures, based on $n$ independent samples from them. Curiously, we show that estimating the IPM itself between probability measures, is not significantly easier than estimating the probabili…

On a complete Riemannian manifold M with Ricci curvature satisfying $\textrm{Ric}(\nabla r,\nabla r) \geq -Ar^2(\log r)^2(\log(\log r))^2...(\log^{k}r)^2$ for $r\gg 1$, where A>0 is a constant, and r is the distance from an arbitrarily fixed point in M. we prove some Liouville-type theorems for a C^2 function $f:M\ri…

Two algorithms for linear contextual bandits with rare updates achieve optimal regret and efficiency.

Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective, and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator, and asks whether it i…

New RL algorithm reduces policy switching cost to loglog(T) with similar regret.

We study revenue optimization learning algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation for a good and seeks to maximize his cumulative discounted surplus. For this setting, first, we propose a novel algorithm that never decreases …

It is well known that Sparse PCA (Sparse Principal Component Analysis) is NP-hard to solve exactly on worst-case instances. What is the complexity of solving Sparse PCA approximately? Our contributions include: 1) a simple and efficient algorithm that achieves an $n^{-1/3}$-approximation; 2) NP-hardness of approximatio…

The logistic regression model is known to converge to a Poisson point process model if the binary response tends to infinitely imbalanced. In this paper, it is shown that this phenomenon is universal in a wide class of link functions on binomial regression. The proof relies on the extreme value theory. For the logit, p…

Developed a new thresholding method that connects soft and hard thresholding.

This paper considers the problem of characterizing the simplest discrete point sets that are aperiodic, using invariants based on topological dynamics. A Delone set whose patch-counting function N(T), for radius T, is finite for all T is called repetitive if there is a function M(T) such that every ball of radius M(T)+…

Erdős-Kac theorem applied to geodesics on modular surface.

Algorithm learns means of spherical Gaussian mixtures efficiently in low dimensions.

An Atlas model is a rank-based system of continuous semimartingales for which the steady-state values of the processes follow a power law, or Pareto distribution. For a power law, the log-log plot of these steady-state values versus rank is a straight line. Zipf's law is a power law for which the slope of this line is …

New algorithm learns decision trees faster than before.

New algorithm for reinforcement learning in uncertain environments with unknown thresholds.

A new SSL method uses instance-dependent thresholds to improve accuracy.

The study tightens the sample complexity for learning nonparametric mixture components.

Study stability thresholds of big line bundles, proving bounds and generalizing results.

Adaptive algorithm for outlier detection by balancing arm exploration and threshold estimation.

In this paper we studied about the wavelet identification of the thresholds and time delay for more general case without the constraint that the time delay is smaller than the order of the model. Here we composed an empirical wavelet from the SETAR (Self-Exciting Threshold Autoregressive) model and identified the thres…

The article examines different thresholding methods for improving PAM algorithm in cancer classification.

In the work of Ammann, Dahl and Humbert it has turned out that the Yamabe invariant on closed manifolds is a bordism invariant below a certain threshold constant. A similar result holds for a spinorial analogon. These threshold constants are characterized through Yamabe-type equations on products of spheres with rescal…

Although the threshold network is one of the most used tools to characterize the underlying structure of a stock market, the identification of the optimal threshold to construct a reliable stock network remains challenging. In this paper, the concept of dynamic consistence between the threshold network and the stock ma…