Publications related to Data Heterogeneity in Linear Distributed Estimation and Learning

Related publications (14)

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity

Geovani Rizk, Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot, Youssef Allouah

The theory underlying robust distributed learning algorithms, designed to resist adversarial machines, matches empirical observations when data is homogeneous. Under data heterogeneity however, which is the norm in practical scenarios, established lower bo ...

2023

Privatized graph federated learning

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-m ...

SPRINGER2023

Artificial Intelligence Based Methods for Smart and Sustainable Urban Planning: A Systematic Survey

Jérôme Chenal

The world's cities are facing various challenges such as rapid urbanization, poverty, climate change, pollution, sustainable and inclusive development. Building futuristic smart and sustainable cities is proving to be a response to these challenges. Recent ...

SPRINGER2022

Closed-Form Expression of the Fourier Ring-Correlation for Single-Molecule Localization Microscopy

Michaël Unser, Daniel Sage, Emmanuel Emilien Louis Soubies, Thanh-An Michel Pham

Single-molecule localization microscopy (SMLM) is a popular microscopic technique that achieves super resolution imaging by localizing individual blinking molecules in thousands of frames. Therefore, the reconstructed high-resolution image is a combination ...

IEEE2019

Asian option pricing with orthogonal polynomials

Sander Félix M Willems

In this paper we derive a series expansion for the price of a continuously sampled arithmetic Asian option in the Black-Scholes setting. The expansion is based on polynomials that are orthogonal with respect to the log-normal distribution. All terms in the ...

ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD2019

Collecting Experience Data from Remotely Hosted Learning Applications

Denis Gillet, Christophe Salzmann

The ability to integrate multiple learning applications from different organizations allows sharing resources and reducing costs in the deployment of learning systems. In this sense, Learning Tools Interoperability (LTI) is the main current leading technol ...

SPRINGER2018

Learning Data Triage: Linear Decoding Works for Compressive MRI

Volkan Cevher, Yen-Huan Li

The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure. This approach requires looking for ...

Ieee2016

Model selection for partial least squares calibration and implications for analysis of atmospheric organic aerosol samples with mid-infrared spectroscopy

Satoshi Takahama

In developing partial least squares calibration models, selecting the number of latent variables used for their construction to minimize both model bias and model variance remains a challenge. Several metrics exist for incorporating these trade-offs, but t ...

Wiley-Blackwell2015

Non-linear Recovery of Sparse Signal Representations with Applications to Temporal and Spatial Localization

Zafer Dogan

Foundations of signal processing are heavily based on Shannon's sampling theorem for acquisition, representation and reconstruction. This theorem states that signals should not contain frequency components higher than the Nyquist rate, which is half of the ...

EPFL2015

Enhancing statistical performance of data-driven controller tuning via L2-regularization

Alireza Karimi

Noniterative data-driven techniques are design methods that allow optimal feedback control laws to be derived from input-output (I/O) data only, without the need of a model of the process. A drawback of these methods is that, in their standard formulation, ...

Pergamon-Elsevier Science Ltd2014

Adaptive Information-Sharing for Privacy-Aware Mobile Social Networks

Jean-Pierre Hubaux, Murtuza Jadliwala, Kévin Clément Huguenin, Igor Bilogrevic, Berker Agir

Personal and contextual information are increasingly shared via mobile social networks. Users' locations, activities and their co-presence can be shared easily with online ``friends'', as their smartphones already access such information from embedded sens ...

2013

OFDM Channel Estimation Based on Impulse Response Decimation: Analysis and Novel Algorithms

Stefano Rosati

In this paper, OFDM data-aided channel estimation based on the decimation of the Channel Impulse Response (CIR) through the selection of the Most Significant Samples (MSS) is addressed. Our aim is to approach the Minimum Mean Square Error (MMSE) channel es ...

Institute of Electrical and Electronics Engineers2012

Statistical methods for insect choice experiments

Ingrid Ricard

Olfactometer experiments are used to determine the effect of odours on the behaviour of organisms such as insects or nematodes, and typically result in data comprising many groups of small counts, overdispersed relative to the multinomial distribution. Ove ...

EPFL2008

Vision-Aided Inertial Navigation Using a Geomatics Approach

Fadi Atef Bayoud

This paper proposes and tests a method for bridging GPS outages during short and long periods with a vision-based inertial navigation. This method is similar to the Simultaneous Lacalisation And Mapping (SLAM), which is the problem of mapping the environme ...

2005