arXiv research

New algorithms estimate and test collision probability with near-optimal sample complexity.

No-collision maps improve manifold learning for image data.

New strategy achieves optimal regret without communication or collisions in multi-player bandit.

A new metric for uncertainty quantification using class collisions.

Study shows how transformers classify symbols without naming them, proving a margin-versus-collision criterion.

Residual neural networks improve collision prediction in planetary simulations.

Study nonholonomic systems with collisions using variational principles.

Paper analyzes dynamics of nonholonomic systems with collisions using variational techniques.

Model forecasts motor vehicle collision rates with high accuracy.

Machine learning competition predicts spacecraft collision risks.

One approach to designing decision making logic for an aircraft collision avoidance system frames the problem as a Markov decision process and optimizes the system using dynamic programming. The resulting collision avoidance strategy can be represented as a numeric table. This methodology has been used in the developme…

Reduces necessary conditions for collision avoidance on curved spaces.

New algorithm for multi-player bandits with collision-dependent rewards.

The paper addresses the Multiplayer Multi-Armed Bandit (MMAB) problem, where $M$ decision makers or players collaborate to maximize their cumulative reward. When several players select the same arm, a collision occurs and no reward is collected on this arm. Players involved in a collision are informed about this collis…

Algorithm reduces regret in multi-player bandits with unknown collision rewards.

An important application of intelligent vehicles is advance detection of dangerous events such as collisions. This problem is framed as a problem of optimal alarm choice given predictive models for vehicle location and motion. Techniques for real-time collision detection are surveyed and grouped into three classes: ran…

A new algorithm reduces regret in multi-player bandits without collision info.

New algorithm for multi-player bandits in decentralized, asynchronous systems.

A new algorithm RESYNC for defenders against malicious attackers in multi-player bandits.

Bayesian deep learning predicts satellite collisions.

New algorithms tackle adversarial multi-player bandits with forced-collision communication.

New tester outperforms existing ones in uniformity testing.

We study multiplayer stochastic multi-armed bandit problems in which the players cannot communicate and if two or more players pull the same arm, a collision occurs and the involved players receive zero reward. We consider two feedback models: a model in which the players can observe whether a collision has occurred an…

The configuration manifold $M$ of a mechanical system consisting of two unconstrained rigid bodies in $\mathbb{R}^n$, $n\geq 1$, is a manifold with boundary (typically with singularities.) A complete description of the system requires boundary conditions that specify how orbits should be continued after collisions. A b…

The Kepler-Heisenberg problem is that of determining the motion of a planet around a sun in the Heisenberg group, thought of as a three-dimensional sub-Riemannian manifold. The sub-Riemannian Hamiltonian provides the kinetic energy, and the gravitational potential is given by the fundamental solution to the sub-Laplaci…

Study motion planning for points avoiding obstacles in a plane.

Logit dynamics formula reveals self-regulation in softmax policy gradient methods.

Centrality, as a geometrical property of the collision, is crucial for the physical interpretation of nucleus-nucleus and proton-nucleus experimental data. However, it cannot be directly accessed in event-by-event data analysis. Common methods for centrality estimation in A-A and p-A collisions usually rely on a single…

A multi-user multi-armed bandit (MAB) framework is used to develop algorithms for uncoordinated spectrum access. The number of users is assumed to be unknown to each user. A stochastic setting is first considered, where the rewards on a channel are the same for each user. In contrast to prior work, it is assumed that t…

A step by step procedure to derive analytically the exact dynamical evolution equations of the probability density functions (PDF) of well known kinetic wealth exchange economic models is shown. This technique gives a dynamical insight into the evolution of the PDF, e.g., allowing the calculation of its relaxation time…

Recovering manifold geometry from geodesic intersections.

We consider the non-stochastic version of the (cooperative) multi-player multi-armed bandit problem. The model assumes no communication at all between the players, and furthermore when two (or more) players select the same action this results in a maximal loss. We prove the first $\sqrt{T}$-type regret guarantee for th…

New algorithm for multi-player bandits without needing lower bounds or scaling inversely.

Two non-communicating players minimize regret in a multi-armed bandit game.

Unified approach detects traffic conflicts across various interactions.

Rolling systems limit to billiard models with no-slip collisions.

Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space C as one popular solution. Recently, machine learning methods have been introduced into sampling-based motion planning methods, which train a classifier to distinguish coll…

This work examines the role of reinforcement learning in reducing the severity of on-road collisions by controlling velocity and steering in situations in which contact is imminent. We construct a model, given camera images as input, that is capable of learning and predicting the dynamics of obstacles, cars and pedestr…

Up to symmetries, the orbits of three equal masses under an inverse cube force with zero angular momentum and constant moment of inertia can be reparametrized as the geodesics of a complete, negatively curved metric on a pair of pants. The ends of the pants represent binary collisions. Here we will examine the visibili…

Stochastic approach improves neural network training for kinetic simulations.

We consider the ideal-gas models of trading markets, where each agent is identified with a gas molecule and each trading as an elastic or money-conserving (two-body) collision. Unlike in the ideal gas, we introduce saving propensity $λ$ of agents, such that each agent saves a fraction $λ$ of its money and trades with t…

We consider the ideal-gas models of trading markets, where each agent is identified with a gas molecule and each trading as an elastic or money-conserving (two-body) collision. Unlike in the ideal gas, we introduce saving propensity $λ$ of agents, such that each agent saves a fraction $λ$ of its money and trades with t…

MinHash and SimHash are the two widely adopted Locality Sensitive Hashing (LSH) algorithms for large-scale data processing applications. Deciding which LSH to use for a particular problem at hand is an important question, which has no clear answer in the existing literature. In this study, we provide a theoretical answ…

Multipeakons are special solutions to the Camassa-Holm equation described by an integrable geodesic flow on a Riemannian manifold. We present a bi-Hamiltonian formulation of the system explicitly and write down formulae for the associated first integrals. Then we exploit the first integrals and present a novel approach…

Generalizes Landau-Ginzburg mirrors for Frobenius manifolds in Dynkin type A.

This work improves motion planning for quadcopters by learning and reasoning about controller performance.

This work optimizes signal estimation for sparse MRA with collision-free signals.

CriticSMC improves planning efficiency in constrained environments.