Publications related to Nearly-Tight and Oblivious Algorithms for Explainable Clustering

Localization errors of the stochastic heat equation

In this thesis, we study the stochastic heat equation (SHE) on bounded domains and on the whole Euclidean space

\R^d.

We confirm the intuition that as the bounded domain increases to the whole space, both solutions become arbitrarily close to one another ...

EPFL2022

Shrinking FPGA Static Power via Machine Learning-Based Power Gating and Enhanced Routing

Mirjana Stojilovic, Hossein Asadi, Zeinab Seifoori

Despite FPGAs rapidly evolving to support the requirements of the most demanding emerging applications, their high static power consumption, concentrated within the routing resources, still presents a major hurdle for low-power applications. Augmenting the ...

2021

Sublinear Algorithms for Spectral Graph Clustering

Aidasadat Mousavifar

This thesis focuses on designing spectral tools for graph clustering in sublinear time. With the emergence of big data, many traditional polynomial time, and even linear time algorithms have become prohibitively expensive. Processing modern datasets requir ...

EPFL2021

Size and duration of COVID-19 clusters go along with a high SARS-CoV-2 viral load: A spatio-temporal investigation in Vaud state, Switzerland

Stéphane Joost, Séverine Vuilleumier Varisco, Idris Guessous, Onya Opota, Anaïs Laurence Ladoy

To understand the geographical and temporal spread of SARS-CoV-2 during the first documented wave of infection in the state of Vaud, Switzerland, we analyzed clusters of positive cases using the precise residential location of 33,651 individuals tested (RT ...

2021

Plunge and rebound of a taxi market through COVID-19 lockdown: Lessons learned from Shenzhen, China

Kenan Zhang

This paper traces the plunge and rebound of the taxi market in Shenzhen, China through the COVID-19 lockdown. A four-week taxi GPS trajectory data set is collected in the first quarter of 2020, which covers the period of lockdown and phased reopening in th ...

2021

Data reduction for mixed integer linear programming in complex energy systems

François Maréchal, Luc Girardin, Julia Granacher, Luise Middelhauve, Natalija Ljubic

The task of optimally designing and scheduling energy systems with a high share of renewable energies is complex and computationally demanding. A widespread method for tackling this task is to apply mixed integer linear programming (MILP). Even though the ...

2021

On Perfect Clustering of High Dimension, Low Sample Size Data

Soham Sarkar

Popular clustering algorithms based on usual distance functions (e.g., the Euclidean distance) often suffer in high dimension, low sample size (HDLSS) situations, where concentration of pairwise distances and violation of neighborhood structure have advers ...

IEEE COMPUTER SOC2020

Modeling and Analyzing Inquiry Strategies in Open-Ended Learning Environments

Modeling and predicting student learning in computer-based environments often relies solely on sequences of accuracy data. Previous research suggests that it does not only matter what we learn, but also how we learn. The detection and analysis of learning ...

2020

Model-Class Selection Using Clustering and Classification for Structural Identification and Prediction

Ian Smith, Sai Ganesh Sarvotham Pai, Masoud Sanayei

Structural identification using physics-based models and subsequent prediction have much potential to enhance civil infrastructure asset-management decision-making. Interpreting monitoring information in the presence of multiple uncertainty sources and sys ...

2020

Removing imaging systematics from galaxy clustering measurements with Obiwan: application to the SDSS-IV extended Baryon Oscillation Spectroscopic Survey emission-line galaxy sample

Cheng Zhao, Johan Comparat, Anand Stéphane Raichoor

This work presents the application of a new tool, Obiwan, which uses image simulations to determine the selection function of a galaxy redshift survey and calculate three-dimensional (3D) clustering statistics. Obiwan relies on a forward model of the proce ...

OXFORD UNIV PRESS2020

Nearly-Tight and Oblivious Algorithms for Explainable Clustering

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Localization errors of the stochastic heat equation

Shrinking FPGA Static Power via Machine Learning-Based Power Gating and Enhanced Routing

Sublinear Algorithms for Spectral Graph Clustering

Size and duration of COVID-19 clusters go along with a high SARS-CoV-2 viral load: A spatio-temporal investigation in Vaud state, Switzerland

Plunge and rebound of a taxi market through COVID-19 lockdown: Lessons learned from Shenzhen, China

Data reduction for mixed integer linear programming in complex energy systems

On Perfect Clustering of High Dimension, Low Sample Size Data

Modeling and Analyzing Inquiry Strategies in Open-Ended Learning Environments

Model-Class Selection Using Clustering and Classification for Structural Identification and Prediction

Removing imaging systematics from galaxy clustering measurements with Obiwan: application to the SDSS-IV extended Baryon Oscillation Spectroscopic Survey emission-line galaxy sample

Size and duration of COVID-19 clusters go along with a high SARS-CoV-2 viral load: A spatio-temporal investigation in Vaud state, Switzerland

Data reduction for mixed integer linear programming in complex energy systems

Sublinear Algorithms for Spectral Graph Clustering

Localization errors of the stochastic heat equation

Shrinking FPGA Static Power via Machine Learning-Based Power Gating and Enhanced Routing

Model-Class Selection Using Clustering and Classification for Structural Identification and Prediction

Plunge and rebound of a taxi market through COVID-19 lockdown: Lessons learned from Shenzhen, China

On Perfect Clustering of High Dimension, Low Sample Size Data

Modeling and Analyzing Inquiry Strategies in Open-Ended Learning Environments

Removing imaging systematics from galaxy clustering measurements with Obiwan: application to the SDSS-IV extended Baryon Oscillation Spectroscopic Survey emission-line galaxy sample