A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Extends martingale Schrödinger bridge to arbitrary dimensions and characterizes it.
New RL method uses distance between states instead of rewards for sparse reward environments.
This paper bridges Markowitz planning and deep reinforcement learning for portfolio optimization.
QDSB accelerates Schrödinger bridge learning with quantized approximations.
This article asks how planning scholarship may effectively gain impact in planning practice through media exposure. In liberal democracies the public sphere is dominated by mass media. Therefore, working with such media is a prerequisite for effective public impact of planning research. Using the example of megaproject…
In this paper, we propose to combine imitation and reinforcement learning via the idea of reward shaping using an oracle. We study the effectiveness of the near-optimal cost-to-go oracle on the planning horizon and demonstrate that the cost-to-go oracle shortens the learner's planning horizon as function of its accurac…
Study motion planning for points avoiding obstacles in a plane.
Geodesics in R^n configuration spaces for points apart by epsilon.
New AI model improves grid planning efficiency and reliability.
Visual construction of maps linking to two-bridge links.
Deep network solves maze path planning without training.
The study counts ideal points in 2-bridge knot complements using knot diagrams.
Survey on manifold complexities and motion planning in robotics.
LightSBB-M improves generative diffusion modeling with lower 2-Wasserstein distances.
Machine and reinforcement learning (RL) are increasingly being applied to plan and control the behavior of autonomous systems interacting with the physical world. Examples include self-driving vehicles, distributed sensor networks, and agile robots. However, when machine learning is to be applied in these new settings,…
In this paper, we introduce an extension of a Brownian bridge with a random length by including uncertainty also in the pinning level of the bridge. The main result of this work is that unlike for deterministic pinning point, the bridge process fails to be Markovian if the pining point distribution is absolutely contin…
Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars. Existing motion planning methods become ineffective as their computational complexity increases exponentially with the dimensionality of the motion planning problem. To address this issue…
Myriad offers a testbed for integrating machine learning and trajectory optimization.
We designed a grid world task to study human planning and re-planning behavior in an unknown stochastic environment. In our grid world, participants were asked to travel from a random starting point to a random goal position while maximizing their reward. Because they were not familiar with the environment, they needed…
Federated CTMC model estimates bridge deterioration hazards without sharing raw data.
This work studies the contraction coefficients of Schrödinger bridge problems in linear systems.
Paper tackles non-uniform coverage planning for robots.
Algorithm improves blockchain bridge efficiency.
The paper calculates bridge numbers for knots using machine learning.
Bayesian segmentation and uncertainty estimation improve 3D model accuracy for factory planning.
With a point of departure in the concept "uncomfortable knowledge," this article presents a case study of how the American Planning Association (APA) deals with such knowledge. APA was found to actively suppress publicity of malpractice concerns and bad planning in order to sustain a boosterish image of planning. In th…
Comparative statistical properties of Parkinson, Garman-Klass, Roger-Satchell and bridge oscillation estimators are discussed. Point and interval estimations, related with mentioned estimators are considered
In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this paper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions. We sho…
PBCS combines RL and motion planning for better exploration.
The paper analyzes stability and convergence rates of entropic and Sinkhorn potentials.
New method estimates Schrödinger bridge potentials via empirical risk minimization.
New method samples from time-integrated stochastic bridges using neural networks.
Given integers b, c, g, and n, we construct a manifold M containing a c-component link L so that there is a bridge surface Sigma for (M,L) of genus g that intersects L in 2b points and has distance at least n. More generally, given two possibly disconnected surfaces S and S', each with some even number (possibly zero) …
Formula for spectrum linking braid and bridge indices.
Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or c…
We design an algorithm writing down presentations of graph braid groups. Generators are represented in terms of actual motions of robots moving without collisions on a given graph. A key ingredient is a new motion planning algorithm whose complexity is linear in the number of edges and quadratic in the number of robots…
Bayesian neural networks improve uncertainty estimation in 3D point cloud segmentation for factory planning.
In order to alleviate data sparsity and overfitting problems in maximum likelihood estimation (MLE) for sequence prediction tasks, we propose the Generative Bridging Network (GBN), in which a novel bridge module is introduced to assist the training of the sequence prediction model (the generator network). Unlike MLE di…
We consider the bridge linear regression modeling, which can produce a sparse or non-sparse model. A crucial point in the model building process is the selection of adjusted parameters including a regularization parameter and a tuning parameter in bridge regression models. The choice of the adjusted parameters can be v…
The study proves a conjecture about arborescent links with many twigs.
Generative AI agents improve ERP systems by automating complex financial tasks.
In this paper we address cardinality estimation problem which is an important subproblem in query optimization. Query optimization is a part of every relational DBMS responsible for finding the best way of the execution for the given query. These ways are called plans. The execution time of different plans may differ b…
New algorithm computes Schrödinger Bridge for unpaired data translation.
New method learns cell trajectories from multiple snapshots.
The topological complexity TC(X) is a numerical homotopy invariant of a topological space X which is motivated by robotics and is similar in spirit to the classical Lusternik-Schnirelmann category of X. Given a mechanical system with configuration space X, the invariant TC(X) measures the complexity of all possible mot…
We propose in this paper a constructive procedure that transforms locally, even at singular configurations, the kinematics of a car towing trailers into Kumpera-Ruiz normal form. This construction converts the nonholonomic motion planning problem into an algebraic problem (the resolution of a system of polynomial equat…
GP-ND avoids obstacles in trajectory planning using Gaussian Process regression.
Schrödinger bridge solved with Weyl calculus for quadratic state cost.