Learning Non-Parametric Models with Guarantees: A Smooth Lipschitz Regression Approach

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Laser material processing (LMP) has revolutionized modern manufacturing, emerging as a cornerstone technology valued for its precision, speed, and versatility, especially in high-stakes sectors like aerospace, automotive, and medical device production. Des ...

Demographic fairness or equity is a crucial aspect of machine learning models, particularly in critical domains such as healthcare, where errors can have severe consequences. A fair model avoids making distinctions between groups with different sensitive o ...

A payment channel network is a blockchain-based overlay mechanism that allows parties to transact more efficiently than directly using the blockchain. These networks are composed of payment channels that carry transactions between pairs of users. Due to it ...

Our understanding of the generalization capabilities of neural networks (NNs) is still incomplete. Prevailing explanations are based on implicit biases of gradient descent (GD) but they cannot account for the capabilities of models from gradient-free metho ...

The sum-of-squares (SOS) approximation method is a technique used in optimization problems to derive lower bounds on the optimal value of an objective function. By representing the objective function as a sum of squares in a feature space, the SOS method t ...

This paper analyzes a class of Stackelberg games where different actors compete for shared resources and a central authority tries to balance the demand through a pricing mechanism. Situations like this can for instance occur when fleet owners of electric ...

In this article, we study the problem of Byzantine fault-tolerance in a federated optimization setting, where there is a group of agents communicating with a centralized coordinator. We allow up to

f

Byzantine-faulty agents, which may not follow a prescr ...

We address the numerical solution of the Dirichlet problem for the two-dimensional elliptic Monge-Ampere equation using a least-squares/relaxation approach. The relaxation algorithm allows the decoupling of the differential operators from the nonlinearitie ...

Distributionally robust chance constrained programs minimize a deterministic cost function subject to the satisfaction of one or more safety conditions with high probability, given that the probability distribution of the uncertain problem parameters affec ...

Deep neural networks trained on physical losses are emerging as promising surrogates for nonlinear numerical solvers. These tools can predict solutions to Maxwell's equations and compute gradients of output fields with respect to the material and geometric ...

Recent advances in Model Predictive Control (MPC) algorithms and methodologies, combined with the surge of computational power of available embedded platforms, allows the use of real-time optimization-based control of fast mechatronic systems. This paper p ...

Despite the high number of investments for data-based models in the expansion of Industry 4.0, too little effort has been made to ensure the maintenance of those models. In a data-streaming environment, data-based models are subject to concept drifts. A co ...

Soft Magnetic Composites (SMCs) demonstrate excellent potentials for electromagnetic applications. The isotropy is one critical feature in designing the composite. This work presents a shape optimization algorithm for SMCs subject to isotropy and volume co ...

Optimal control is often used in robotics for planning a trajectory to achieve some desired behavior, as expressed by the cost function. Most works in optimal control focus on finding a single optimal trajectory, which is then typically tracked by another ...

Effective caching is crucial for performance of modern-day computing systems. A key optimization problem arising in caching – which item to evict to make room for a new item – cannot be optimally solved without knowing the future. There are many classical ...

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology unde ...

Learning Non-Parametric Models with Guarantees: A Smooth Lipschitz Regression Approach

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Toward Autonomous Laser Manufacturing: Solving Key Challenges in Optimization, Monitoring, and Control

Optimizing fairness and utility in healthcare machine learning models

Pricing for Routing and Flow-control in Payment Channel Networks

Neural Redshift: Random Networks are not Random Functions

Theory and applications of the sum-of-squares technique

On Finding the Leader's Strategy in Quadratic Aggregative Stackelberg Pricing Games

Byzantine Fault-Tolerance in Federated Local SGD Under 2f-Redundancy

An Adaptive Least-squares Algorithm for the Elliptic Monge-ampere Equation

On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

Predicting nonlinear optical scattering with physics-driven neural networks

Optimal Thrust Vector Control of an Electric Small-Scale Rocket Prototype

Data-based model maintenance in the era of industry 4.0: A methodology

Level-set-based Shape Optimization on Soft Magnetic Composites with Isotropy Constraint

Probabilistic Iterative LQR for Short Time Horizon MPC

Robust Learning-Augmented Caching: An Experimental Study

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging