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

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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 ...

The unifying theme of this thesis are decision trees. Given a boolean function

f

and an input

x

, a decision tree computes the value

f(x)

by repeatedly querying bits of

x

. Our work is two-fold. On one hand, we investigate direct sums and other relat ...

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 ...

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 ...

The real-time optimization scheme “modifier adaptation” (MA) has been developed to enforce steady-state plant optimality in the presence of model uncertainty. The key feature of MA is its ability to locally modify the model by adding bias and gradient corr ...

Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when navigating ...

We introduce an algorithm to reconstruct a mesh from discrete samples of a shape's Signed Distance Function (SDF). A simple geometric reinterpretation of the SDF lets us formulate the problem through a point cloud, from which a surface can be extracted wit ...

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 ...

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query proc ...

A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods typically used i ...

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 ...

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 ...

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 ...

Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the observed ...

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 ...

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 ...

This thesis aims to explore and exploit trade-offs in integrated circuits and systems to overcome the fundamental bottlenecks faced by future data acquisition and communication systems. Specifically, we target the emerging implantable neurotechnology and t ...

We propose a non-parametric regression methodology that enforces the regressor to be fully consistent with the sample set and the ground-truth regularity assumptions. As opposed to the Nonlinear Set Membership technique, this constraint guarantees the atta ...

Removal of noise from fluorescence microscopy images is an important first step in many biological analysis pipelines. Current state-of-the-art supervised methods employ convolutional neural networks that are trained with clean (ground-truth) images. Recen ...

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

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Optimizing fairness and utility in healthcare machine learning models

The Power of Decision Trees for Composition and Search Problems

Pricing for Routing and Flow-control in Payment Channel Networks

Theory and applications of the sum-of-squares technique

Statistical mechanics of deep learning

On speeding-up modifier-adaptation schemes for real-time optimization

Social-Transmotion: Promptable Human Trajectory Prediction

Reach For the Arcs: Reconstructing Surfaces from SDFs via Tangent Points

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

Validating functional redundancy with mixed generative adversarial networks

Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector

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

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

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

Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models

Robust Learning-Augmented Caching: An Experimental Study

Probabilistic Iterative LQR for Short Time Horizon MPC

Resource Trade-Offs in Circuits and Systems: from Neurotechnology to Communications

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

Removing Structured Noise With Self-Supervised Blind-Spot Networks