Publications associées à Online and Consistent Correlation Clustering

Publications associées (13)

How data structures affect generalization in Kernel Methods and Deep Learning

Artificial Intelligence has revolutionized numerous fields, driving advancements in healthcare, finance, and autonomous systems. At the core of this revolution lies Deep Learning---algorithms that learn representations from data through computational layer ...

EPFL2025

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

Zhuan Shi, Gang Wang

Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a.k.a. FL clients) to train the same local model. This design is not well-suited for scenarios involving data and/or system ...

IJCAI2024

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

Negar Kiyavash, Ehsan Mokhtarian, Saber Salehkaleybar

We study experiment design for unique identification of the causal graph of a system where the graph may contain cycles. The presence of cycles in the structure introduces major challenges for experiment design as, unlike acyclic graphs, learning the skele ...

2023

Contrastive Class-aware Adaptation for Domain Generalization

Mathieu Salzmann

Domain generalization (DG) tackles the problem of learning a model that generalizes to data drawn from a target domain that was unseen during training. A major trend in this area consists of learning a domain-invariant representation by minimizing the disc ...

IEEE2022

Automatic pathological speech assessment

Parvaneh Janbakhshi

Many pathologies cause impairments in the speech production mechanism resulting in reduced speech intelligibility and communicative ability. To assist the clinical diagnosis, treatment and management of speech disorders, automatic pathological speech asses ...

EPFL2022

Graphic: Graph-Based Hierarchical Clustering For Single-Molecule Localization Microscopy

Michaël Unser, Daniel Sage, Shayan Aziznejad

We propose a novel method for the clustering of point-cloud data that originate from single-molecule localization microscopy (SMLM). Our scheme has the ability to infer a hierarchical structure from the data. It takes a particular relevance when quantitati ...

IEEE2021

Efficient Allocations in Constant Time: Towards Scalable Solutions in the Era of Large Scale Intelligent Systems

Boi Faltings, Panayiotis Danassis

The next technological revolution will be interwoven to the proliferation of intelligent systems. As we bridge the gap between physical and cyber worlds, we will give rise to large-scale, multi-agent based technologies. A key challenge that cities of the f ...

IOS PRESS2020

Enabling Accuracy-Aware Quantum Compilers using Symbolic Resource Estimation

Mathias Soeken, Giulia Meuli

Approximation errors must be taken into account when compiling quantum programs into a low-level gate set. We present a methodology that tracks such errors automatically and then optimizes accuracy parameters to guarantee a specified overall accuracy while ...

ASSOC COMPUTING MACHINERY2020

Education in Process Systems Engineering: Why it matters more than ever and how it can be structured

Dominique Bonvin

This position paper is an outcome of discussions that took place at the third FIPSE Symposium in Rhodes, Greece, between June 20-22, 2016 (http://fi-in-pse.org). The FIPSE objective is to discuss open research challenges in topics of Process Systems Engine ...

PERGAMON-ELSEVIER SCIENCE LTD2019

Quantification of stored red blood cell fluctuations by time-lapse holographic cell imaging

Pierre Marquet, Gerardo Turcatti, Benjamin Rappaz, Michel Prudent, Manon Sandra Bardyn

We propose methods to quantitatively calculate the fluctuation rate of red blood cells with nanometric axial and millisecond temporal sensitivity at the single-cell level by using time-lapse holographic cell imaging. For this quantitative analysis, cell me ...

2018

Geometric robustness of deep networks: analysis and improvement

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli

Deep convolutional neural networks have been shown to be vulnerable to arbitrary geometric transformations. However, there is no systematic method to measure the invariance properties of deep networks to such transformations. We propose ManiFool as a simpl ...

2018

Awareness Towards Industry 4.0: Key Enablers and Applications for Internet of Things and Big Data

Christopher Tucci, Myrna Fatima Flores Pineda

The fourth Industrial revolution promises to increase productivity, flexibility and automation of internal business processes integrating value chains and supporting companies to design and offer novel services based on the availability of data enabled by ...

SPRINGER INTERNATIONAL PUBLISHING AG2018

Subdeterminant Maximization via Nonconvex Relaxations and Anti-Concentration

Javad Ebrahimi Boroojeni, Nisheeth Vishnoi, Damian Mateusz Straszak

Several fundamental problems that arise in optimization and computer science can be cast as follows: Given vectors v(1), ..., v(m) is an element of R-d and a constraint family B subset of 2([m]), find a set S. B that maximizes the squared volume of the sim ...

Ieee2017