Machine learning with kernels for portfolio valuation and risk management

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Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a geant-based simulation of the detect ...

Accurately estimating model performance poses a significant challenge, particularly in scenarios where the source and target domains follow different data distributions. Most existing performance prediction methods heavily rely on the source data in their ...

Orthogonal frequency-division multiplexing (OFDM) is widely used to mitigate inter-symbol interference (ISI) from multipath fading. However, the open nature of wireless OFDM systems makes them vulnerable to jamming attacks. In this context, pilot jamming i ...

Conventional lasers typically support a well‐defined comb of modes. Coupling many resonators together to form larger complex cavities enables the design of the spatial and spectral distribution of modes, for sensitive and controllable on‐chip light sources ...

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 ...

Vision Transformers (ViTs) demonstrate remarkable performance in image classification through visual-token interaction learning, particularly when equipped with local information via region attention or convolutions. Although such architectures improve the ...

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

Distributed learning has become a promising computational parallelism paradigm that enables a wide scope of intelligent applications from the Internet of Things (IoT) to autonomous driving and the healthcare industry. This paper studies distributed learnin ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

The limiting distributions of statistics used to test hypotheses about parameters on the boundary of their domains may provide very poor approximations to the finite-sample behaviour of these statistics, even for very large samples. We review theoretical w ...

Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Tes ...

To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured. The complexity of full measurements of the system scales with its size. When exposed to real-w ...

In this paper, we analyze the recently proposed stochastic primal-dual hybrid gradient (SPDHG) algorithm and provide new theoretical results. In particular, we prove almost sure convergence of the iterates to a solution with convexity and linear convergenc ...

Interactive oracle proofs (IOPs) are a proof system model that combines features of interactive proofs (IPs) and probabilistically checkable proofs (PCPs). IOPs have prominent applications in complexity theory and cryptography, most notably to constructing ...

India is home to a large population of blind children, many with treatable conditions. Project Prakash identifies treatable blind children and provides them with eye surgeries. Once treated, these children are given the opportunity to further their educati ...

This paper presents our approach to predicting future error-related events in a robot-mediated gamified phys- ical training activity for stroke patients. The ability to predict future error under such conditions suggests the existence of distinguishable fe ...

The increasing use of ground-source heat pumps (GSHPs) for heating and cooling of buildings raises questions regarding the technical potential of GSHPs and their impact on the temperature in the shallow subsurface. In this paper, we develop a method using ...

Machine Learning (ML) is on the rise in medicine, promising improved diagnostic, therapeutic and prognostic clinical tools. While these technological innovations are bound to transform health care, they also bring new ethical concerns to the forefront. One ...

Contrastive learning has recently seen tremendous success in self-supervised learning. So far, however, it is largely unclear why the learned representations generalize so effectively to a large variety of downstream tasks. We here prove that feed-forward ...

Machine learning with kernels for portfolio valuation and risk management

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Reweighting simulated events using machine-learning techniques in the CMS experiment

Source-Free Domain-Invariant Performance Prediction

Adversarial Training for Jamming-Robust Channel Estimation in OFDM Systems

Designed Semiconductor Network Random Lasers

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

AdaNCA: Neural Cellular Automata as Adaptors for More Robust Vision Transformer

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

One-Bit Byzantine-Tolerant Distributed Learning via Over-The-Air Computation

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Improved inference for a boundary parameter

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Learning of physical systems: from inference to control

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

A Toolbox for Barriers on Interactive Oracle Proofs

Scholastic status of congenitally blind children following sight surgery

Predictive Analysis of Errors During Robot-Mediated Gamified Training

Using Machine Learning to estimate the technical potential of shallow ground-source heat pumps with thermal interference

Towards a pragmatist dealing with algorithmic bias in medical machine learning

Contrastive Learning Inverts the Data Generating Process