A Linear-Quadratic Gaussian Approach to Dynamic Information Acquisition

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Path-following control is a critical technology for autonomous vehicles. However, time-varying parameters, parametric uncertainties, external disturbances, and complicated environments significantly challenge autonomous driving. We propose an iterative rob ...

We prove global in time well-posedness for perturbations of the 2D stochastic Navier-Stokes equations partial derivative( t)u + u center dot del u = Delta u - del p + sigma + xi, u(0, center dot ) = u(0),div (u) = 0, driven by additive space-time white noi ...

Macroscopic fundamental diagrams (MFDs) have been widely adopted to model the traffic flow of large-scale urban networks. Coupling perimeter control and regional route guidance (PCRG) is a promising strategy to decrease congestion heterogeneity and reduce ...

In this thesis we study stability from several viewpoints. After covering the practical importance, the rich history and the ever-growing list of manifestations of stability, we study the following. (i) (Statistical identification of stable dynamical syste ...

Bayesian Optimization (BO) is typically used to optimize an unknown function f that is noisy and costly to evaluate, by exploiting an acquisition function that must be maximized at each optimization step. Even if provably asymptotically optimal BO algorith ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

We consider optimal regimes for algorithm-assisted human decision-making. Such regimes are decision functions of measured pre-treatment variables and, by leveraging natural treatment values, enjoy a superoptimality property whereby they are guaranteed to o ...

This paper introduces a novel method for data-driven robust control of nonlinear systems based on the Koopman operator, utilizing Integral Quadratic Constraints (IQCs). The Koopman operator theory facilitates the linear representation of nonlinear system d ...

There is a bias in the inference pipeline of most diffusion models. This bias arises from a signal leak whose distribution deviates from the noise distribution, creating a discrepancy between training and inference processes. We demonstrate that this signa ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Recently, there has been an arms race of pose forecasting methods aimed at solving the spatio-temporal task of predicting a sequence of future 3D poses of a person given a sequence of past observed ones. However, the lack of unified benchmarks and limited ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

Noise measurements in light water reactor systems aid in generating validation data for integral point kinetic parameter predictions and generating monitoring parameters for reactor safety and safeguards. The CROCUS zero-power reactor has been used to supp ...

Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations ...

This paper introduces Wireless IoT-based Noise Cancellation (WINC) which defines a framework for leveraging a wireless network of IoT microphones to enhance active noise cancellation in noise-canceling headphones. The IoT microphones forward ambient noise ...

In this thesis we address various factors that contribute both theoretically and practically to mitigating supply demand mismatches. The thesis is composed of three chapters, where each chapter is an independent scientific paper. In the first paper, we dev ...

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

Air traffic noise emissions are responsible for a significant part of the overall environmental noise, especially in the vicinity of airports. Exposure to environmental noise is known to negatively impact health and to be associated with cardiovascular dis ...

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems". The corresponding scripts can be accessed on GitHub (https://github.com/axgoujon/convex_ridge_regularizers). The data is organized as follows: - ct_data_sets.tar.gz: contains prepr ...

A Linear-Quadratic Gaussian Approach to Dynamic Information Acquisition

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Less Conservatism, Stronger Robustness: Iterative Robust Gain-Scheduled Path Following Control of Autonomous Bus With Unstructured and Changing Dynamics

Global existence for perturbations of the 2D stochastic Navier-Stokes equations with space-time white noise

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Stability: a search for structure

Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimization

Reliable data-driven decision-making through optimal transport

Optimal regimes for algorithm-assisted human decision-making

Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

Exploiting the Signal-Leak Bias in Diffusion Models

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Toward Reliable Human Pose Forecasting With Uncertainty

Spectral Estimators for High-Dimensional Matrix Inference

Neutron-Gamma Noise Measurements in a Zero-Power Reactor Using Organic Scintillators

Distributionally Robust Linear Quadratic Control

WINC: A Wireless IoT Network for Multi-Noise Source Cancellation

Optimizing Sales Forecasting, Inventory, Pricing and Sourcing Decisions

Multi-agent Learning with Privacy Guarantees

Robust tunable acoustic impedance control on electroacoustic resonators for aircraft noise reduction

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems"