Tootfinder

Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventions
Asim H. Gazi, Bhanu T. Gullapalli, Daiqi Gao, Benjamin M. Marlin, Vivek Shetty, Susan A. Murphy
https://arxiv.org/abs/2507.10798

Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventions
Timely decision making is critical to the effectiveness of mobile health (mHealth) interventions. At predefined timepoints called "decision points," intelligent mHealth systems such as just-in-time adaptive interventions (JITAIs) estimate an individual's biobehavioral context from sensor or survey data and determine whether and how to intervene. For interventions targeting habitual behavior (e.g., oral hygiene), effectiveness often hinges on delivering support shortly before the target behavior…

Kernel Learning for Mean-Variance Trading Strategies
Owen Futter, Nicola Muca Cirone, Blanka Horvath
https://arxiv.org/abs/2507.10701 https://

Kernel Learning for Mean-Variance Trading Strategies
In this article, we develop a kernel-based framework for constructing dynamic, pathdependent trading strategies under a mean-variance optimisation criterion. Building on the theoretical results of (Muca Cirone and Salvi, 2025), we parameterise trading strategies as functions in a reproducing kernel Hilbert space (RKHS), enabling a flexible and non-Markovian approach to optimal portfolio problems. We compare this with the signature-based framework of (Futter, Horvath, Wiese, 2023) and demonstrat…

Introducing the PIT-plot -- a new tool in the portfolio manager's toolkit
Stig-Johan Wiklund, Magnus Ytterstad
https://arxiv.org/abs/2506.12068 https:/…

Introducing the PIT-plot -- a new tool in the portfolio manager's toolkit
Project portfolio management is an essential process for organizations aiming to optimize the value of their R&D investments. In this article, we introduce a new tool designed to support the prioritization of projects within project portfolio management. We label this tool the PIT-plot, an acronym for Project Impact Tornado plot, with reference to the similarity to the Tornado plot often used for sensitivity analyses. Many traditional practices in portfolio management focus on the properties of…

Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes
Yu Tan, Yongxiang Li, Xiaowu Dai, Kwok-Leung Tsui
https://arxiv.org/abs/2506.12701

Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes
Functional ANOVA (FANOVA) is a widely used variance-based sensitivity analysis tool. However, studies on functional-output FANOVA remain relatively scarce, especially for black-box computer experiments, which often involve complex and nonlinear functional-output relationships with unknown data distribution. Conventional approaches often rely on predefined basis functions or parametric structures that lack the flexibility to capture complex nonlinear relationships. Additionally, strong assumptio…

Extracting Document Relations from Search Corpus by Marginalizing over User Queries
Yuki Iwamoto, Kaoru Tsunoda, Ken Kaneiwa
https://arxiv.org/abs/2507.10726

Extracting Document Relations from Search Corpus by Marginalizing over User Queries
Understanding relationships between documents in large-scale corpora is essential for knowledge discovery and information organization. However, existing approaches rely heavily on manual annotation or predefined relationship taxonomies. We propose EDR-MQ (Extracting Document Relations by Marginalizing over User Queries), a novel framework that discovers document relationships through query marginalization. EDR-MQ is based on the insight that strongly related documents often co-occur in results…

Multi-Agent Trust Region Policy Optimisation: A Joint Constraint Approach
Chak Lam Shek, Guangyao Shi, Pratap Tokekar
https://arxiv.org/abs/2508.10340 https://

Multi-Agent Trust Region Policy Optimisation: A Joint Constraint Approach
Multi-agent reinforcement learning (MARL) requires coordinated and stable policy updates among interacting agents. Heterogeneous-Agent Trust Region Policy Optimization (HATRPO) enforces per-agent trust region constraints using Kullback-Leibler (KL) divergence to stabilize training. However, assigning each agent the same KL threshold can lead to slow and locally optimal updates, especially in heterogeneous settings. To address this limitation, we propose two approaches for allocating the KL dive…

Forecast-Driven MPC for Decentralized Multi-Robot Collision Avoidance
Hadush Hailu, Bruk Gebregziabher, Prudhvi Raj
https://arxiv.org/abs/2508.08264 https://

Forecast-Driven MPC for Decentralized Multi-Robot Collision Avoidance
The Iterative Forecast Planner (IFP) is a geometric planning approach that offers lightweight computations, scal- able, and reactive solutions for multi-robot path planning in decentralized, communication-free settings. However, it struggles in symmetric configurations, where mirrored interactions often lead to collisions and deadlocks. We introduce eIFP-MPC, an optimized and extended version of IFP that improves robustness and path consistency in dense, dynamic environments. The method refines…

Computational Methods and Verification Theorem for Portfolio-Consumption Optimization under Exponential O-U Dynamics
Zhaoxiang Zhong, Haiming Song
https://arxiv.org/abs/2508.06491

Computational Methods and Verification Theorem for Portfolio-Consumption Optimization under Exponential O-U Dynamics
In this paper, we focus on the problem of optimal portfolio-consumption policies in a multi-asset financial market, where the n risky assets follow Exponential Ornstein-Uhlenbeck processes, along with one risk-free bond. The investor's preferences are modeled using Constant Relative Risk Aversion utility with state-dependent stochastic discounting. The problem can be formulated as a high-dimensional stochastic optimal control problem, wherein the associated value function satisfies a Hamilton-J…

Estimating Covariance for Global Minimum Variance Portfolio: A Decision-Focused Learning Approach
Juchan Kim, Inwoo Tae, Yongjae Lee
https://arxiv.org/abs/2508.10776 https://

Estimating Covariance for Global Minimum Variance Portfolio: A Decision-Focused Learning Approach
Portfolio optimization constitutes a cornerstone of risk management by quantifying the risk-return trade-off. Since it inherently depends on accurate parameter estimation under conditions of future uncertainty, the selection of appropriate input parameters is critical for effective portfolio construction. However, most conventional statistical estimators and machine learning algorithms determine these parameters by minimizing mean-squared error (MSE), a criterion that can yield suboptimal inves…

Optimal Control of Reserve Asset Portfolios for Pegged Digital Currencies
Alexander Hammerl, Georg Beyschlag
https://arxiv.org/abs/2508.09429 https://arxiv…

Optimal Control of Reserve Asset Portfolios for Pegged Digital Currencies
Stablecoins promise par convertibility, yet issuers must balance immediate liquidity against yield on reserves to keep the peg credible. We study this treasury problem as a continuous-time control task with two instruments: reallocating reserves between cash and short-duration government bills, and setting a spread fee for either minting or burning the coin. Mint and redemption flows follow mutually exciting processes that reproduce clustered order flow; peg deviations arise when redemptions ex…