arXiv research

One-Class Boundary Peeling detects outliers efficiently and robustly.

New method for causal discovery using peeling algorithms for various data types.

Faster algorithm for generalized mean densest subgraph problem.

A new algorithm reduces graph complexity for better dense subgraph analysis.

New supervised and unsupervised NFLTs for elliptical distributions.

Study peels tensor equations on Schwarzschild spacetime.

New model explains neural collapse and limits on minority classes in imbalanced datasets.

Study of Dirac fields on Kerr spacetimes using peeling method.

New method speeds up solving L0-regularized least-squares problems.

In this paper we study an experimentally-observed connection between two seemingly unrelated processes, one from computational geometry and the other from differential geometry. The first one (which we call "grid peeling") is the convex-layer decomposition of subsets $G\subset \mathbb Z^2$ of the integer grid, previous…

In this paper, we show that the peeling property still holds for Bondi-Sachs metrics with nonzero cosmological constant under the boundary condition given by Sommerfeld's radiation condition together with three nontrivial $Λ$-independent functions $B$, $a$, $b$. This should indicate the new boundary condition is natura…

The paper uncovers symmetries in large language models through layer-peeled optimization.

The paper develops a robust algorithm for contextual bandits with heavy-tailed rewards.

This paper investigates graph clustering in the planted cluster model in the presence of {\em small clusters}. Traditional results dictate that for an algorithm to provably correctly recover the clusters, {\em all} clusters must be sufficiently large (in particular, $\tildeΩ(\sqrt{n})$ where $n$ is the number of nodes …

Study of Bondi-Sachs formalism for massless scalar field with zero cosmological constant.

Proposes a privacy-preserving sign selection method for distributed systems.

Paper explains neural collapse in neural networks using a new model.

Significant improvements in regret analysis for adaptive online learning problems.

Mixtures of Mallows models are a popular generative model for ranking data coming from a heterogeneous population. They have a variety of applications including social choice, recommendation systems and natural language processing. Here we give the first polynomial time algorithm for provably learning the parameters of…

Paper proposes a privacy-preserving method to control false discoveries.

A new iterative low complexity algorithm has been presented for computing the Walsh-Hadamard transform (WHT) of an $N$ dimensional signal with a $K$-sparse WHT, where $N$ is a power of two and $K = O(N^α)$, scales sub-linearly in $N$ for some $0 < α< 1$. Assuming a random support model for the non-zero transform domain…

A lamination of a graph embedded on a surface is a collection of pairwise disjoint non-contractible simple closed curves drawn on the graph. In the case when the surface is a sphere with three punctures (a.k.a. a pair of pants), we first identify the lamination space of a graph embedded on that surface as a lattice pol…

The Sample Compression Conjecture of Littlestone & Warmuth has remained unsolved for over two decades. This paper presents a systematic geometric investigation of the compression of finite maximum concept classes. Simple arrangements of hyperplanes in Hyperbolic space, and Piecewise-Linear hyperplane arrangements, are …

Paper introduces efficient top-k selection with differential privacy.

Starting from an arbitrary sequence of polygons whose total perimeter is $2n$, we can build an (oriented) surface by pairing their sides in a uniform fashion. Chmutov and Pittel (arXiv:1503.01816) have shown that, regardless of the configuration of polygons we started with, the degree sequence of the graph obtained thi…

Method estimates dynamic treatment effects using machine learning and g-estimation.

The paper analyzes generalization in deep contrastive learning.

New method reduces multi-armed bandit regret to near-optimal levels.

Study explains how noisyGD with DP improves feature learning despite high dimensionality.

New method TLC improves transductive learning bounds.

Logarithmic corrections to Price's law near black hole event horizon.

Gradient descent converges to a small neighborhood of the true parameter in logistic regression with Gaussian design.

Examines algorithmic modeling across three cultures.

Playing repeated matrix games (RMG) while maximizing the cumulative returns is a basic method to evaluate multi-agent learning (MAL) algorithms. Previous work has shown that $UCB$, $M3$, $S$ or $Exp3$ algorithms have good behaviours on average in RMG. Besides, hedging algorithms have been shown to be effective on predi…

Meta-algorithm selection aims to choose the best algorithm selector for a given problem instance.

Proposes CLRS benchmark to evaluate algorithmic reasoning.

Combines multiple bandit algorithms to create a nearly optimal single algorithm.

We propose accelerated randomized coordinate descent algorithms for stochastic optimization and online learning. Our algorithms have significantly less per-iteration complexity than the known accelerated gradient algorithms. The proposed algorithms for online learning have better regret performance than the known rando…

The exchange algorithm is studied for its convergence and asymptotic variance.

Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. In real application, training data are always incomplete or some nodes are hidden. To deal with this problem many learning parameter algorithms are suggested foreground EM, Gibbs sampling and RBE algori…

No algorithm outperforms uniform sampling in A/B testing.

Improves algorithm selection for thousands of candidates using dyadic features.

This review article surveys data augmentation MCMC algorithms.

Bayesian learning rule unifies and generalizes various machine learning algorithms.

Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm, which we believe to be the first method that can automatically discover a better algorithm. We approach th…

This review summarizes five Lasso optimization algorithms.

Neural networks mimic algorithms to solve complex problems.

Paper proposes a reinforcement learning framework for efficient hyper-parameter tuning of stochastic optimization algorithms.