mailitics

Category: cs.SY

  • A Cherry-Picking Approach to Large Load Shaping for More Effective Carbon Reduction

    A Cherry-Picking Approach to Large Load Shaping for More Effective Carbon Reduction arXiv:2601.17990v1 Announce Type: new Abstract: Shaping multi-megawatt loads, such as data centers, impacts generator dispatch on the electric grid, which in turn affects system CO2 emissions and energy cost. Substantiating the effectiveness of prevalent load shaping strategies, such as those based on grid-level…

  • Fine Tuning a Simulation-Driven Estimator

    Fine Tuning a Simulation-Driven Estimator arXiv:2504.04480v2 Announce Type: cross Abstract: Many industries now deploy high-fidelity simulators (digital twins) to represent physical systems, yet their parameters must be calibrated to match the true system. This motivated the construction of simulation-driven parameter estimators, built by generating synthetic observations for sampled parameter values and learning a supervised mapping…

  • Symmetric Linear Dynamical Systems are Learnable from Few Observations

    Symmetric Linear Dynamical Systems are Learnable from Few Observations arXiv:2512.05337v1 Announce Type: new Abstract: We consider the problem of learning the parameters of a $N$-dimensional stochastic linear dynamics under both full and partial observations from a single trajectory of time $T$. We introduce and analyze a new estimator that achieves a small maximum element-wise error…

  • Operator Models for Continuous-Time Offline Reinforcement Learning

    Operator Models for Continuous-Time Offline Reinforcement Learning arXiv:2511.10383v1 Announce Type: new Abstract: Continuous-time stochastic processes underlie many natural and engineered systems. In healthcare, autonomous driving, and industrial control, direct interaction with the environment is often unsafe or impractical, motivating offline reinforcement learning from historical data. However, there is limited statistical understanding of the approximation errors…

  • Friction on Demand: A Generative Framework for the Inverse Design of Metainterfaces

    Friction on Demand: A Generative Framework for the Inverse Design of Metainterfaces arXiv:2511.03735v1 Announce Type: new Abstract: Designing frictional interfaces to exhibit prescribed macroscopic behavior is a challenging inverse problem, made difficult by the non-uniqueness of solutions and the computational cost of contact simulations. Traditional approaches rely on heuristic search over low-dimensional parameterizations, which limits…

  • Personalized Collaborative Learning with Affinity-Based Variance Reduction

    Personalized Collaborative Learning with Affinity-Based Variance Reduction arXiv:2510.16232v1 Announce Type: new Abstract: Multi-agent learning faces a fundamental tension: leveraging distributed collaboration without sacrificing the personalization needed for diverse agents. This tension intensifies when aiming for full personalization while adapting to unknown heterogeneity levels — gaining collaborative speedup when agents are similar, without performance degradation when…

  • Random Walk Learning and the Pac-Man Attack

    Random Walk Learning and the Pac-Man Attack arXiv:2508.05663v1 Announce Type: new Abstract: Random walk (RW)-based algorithms have long been popular in distributed systems due to low overheads and scalability, with recent growing applications in decentralized learning. However, their reliance on local interactions makes them inherently vulnerable to malicious behavior. In this work, we investigate an…

  • Physics constrained learning of stochastic characteristics

    Physics constrained learning of stochastic characteristics arXiv:2507.12661v1 Announce Type: new Abstract: Accurate state estimation requires careful consideration of uncertainty surrounding the process and measurement models; these characteristics are usually not well-known and need an experienced designer to select the covariance matrices. An error in the selection of covariance matrices could impact the accuracy of the…

  • Derandomizing Simultaneous Confidence Regions for Band-Limited Functions by Improved Norm Bounds and Majority-Voting Schemes

    Derandomizing Simultaneous Confidence Regions for Band-Limited Functions by Improved Norm Bounds and Majority-Voting Schemes arXiv:2506.17764v1 Announce Type: new Abstract: Band-limited functions are fundamental objects that are widely used in systems theory and signal processing. In this paper we refine a recent nonparametric, nonasymptotic method for constructing simultaneous confidence regions for band-limited functions from noisy input-output…

  • Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data

    Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data arXiv:2505.04954v1 Announce Type: new Abstract: The identification of a linear system model from data has wide applications in control theory. The existing work that provides finite sample guarantees for linear system identification typically uses data from a single long system trajectory under i.i.d.…

  • Foundations of Safe Online Reinforcement Learning in the Linear Quadratic Regulator: $sqrt{T}$-Regret

    Foundations of Safe Online Reinforcement Learning in the Linear Quadratic Regulator: $sqrt{T}$-Regret arXiv:2504.18657v1 Announce Type: new Abstract: Understanding how to efficiently learn while adhering to safety constraints is essential for using online reinforcement learning in practical applications. However, proving rigorous regret bounds for safety-constrained reinforcement learning is difficult due to the complex interaction between safety,…

  • Learning Enhanced Ensemble Filters

    Learning Enhanced Ensemble Filters arXiv:2504.17836v1 Announce Type: new Abstract: The filtering distribution in hidden Markov models evolves according to the law of a mean-field model in state–observation space. The ensemble Kalman filter (EnKF) approximates this mean-field model with an ensemble of interacting particles, employing a Gaussian ansatz for the joint distribution of the state and…

  • A Metropolis-Adjusted Langevin Algorithm for Sampling Jeffreys Prior

    A Metropolis-Adjusted Langevin Algorithm for Sampling Jeffreys Prior arXiv:2504.06372v1 Announce Type: cross Abstract: Inference and estimation are fundamental aspects of statistics, system identification and machine learning. For most inference problems, prior knowledge is available on the system to be modeled, and Bayesian analysis is a natural framework to impose such prior information in the form…

  • On Model Protection in Federated Learning against Eavesdropping Attacks

    On Model Protection in Federated Learning against Eavesdropping Attacks arXiv:2504.02114v1 Announce Type: cross Abstract: In this study, we investigate the protection offered by federated learning algorithms against eavesdropping adversaries. In our model, the adversary is capable of intercepting model updates transmitted from clients to the server, enabling it to create its own estimate of the…

  • Nuclear Microreactor Control with Deep Reinforcement Learning

    Nuclear Microreactor Control with Deep Reinforcement Learning arXiv:2504.00156v1 Announce Type: cross Abstract: The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for…

  • Bayes and Biased Estimators Without Hyper-parameter Estimation: Comparable Performance to the Empirical-Bayes-Based Regularized Estimator

    Bayes and Biased Estimators Without Hyper-parameter Estimation: Comparable Performance to the Empirical-Bayes-Based Regularized Estimator arXiv:2503.11854v1 Announce Type: new Abstract: Regularized system identification has become a significant complement to more classical system identification. It has been numerically shown that kernel-based regularized estimators often perform better than the maximum likelihood estimator in terms of minimizing mean squared…

  • On the Convergence and Stability of Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning, and Online Decision Transformers

    On the Convergence and Stability of Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning, and Online Decision Transformers arXiv:2502.05672v1 Announce Type: new Abstract: This article provides a rigorous analysis of convergence and stability of Episodic Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning and Online Decision Transformers. These algorithms performed competitively across various benchmarks, from games to robotic tasks,…

  • Nonparametric Sparse Online Learning of the Koopman Operator

    Nonparametric Sparse Online Learning of the Koopman Operator arXiv:2501.16489v1 Announce Type: new Abstract: The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems. Data-driven techniques to learn the Koopman operator typically assume that the chosen function space is closed under system dynamics. In this paper, we study the Koopman…

  • Asymptotics of Linear Regression with Linearly Dependent Data

    Asymptotics of Linear Regression with Linearly Dependent Data arXiv:2412.03702v1 Announce Type: new Abstract: In this paper we study the asymptotics of linear regression in settings where the covariates exhibit a linear dependency structure, departing from the standard assumption of independence. We model the covariates using stochastic processes with spatio-temporal covariance and analyze the performance of…