Concept

Occam's razor

Related publications (32)

Network-based kinetic models: Emergence of a statistical description of the graph topology

In this paper, we propose a novel approach that employs kinetic equations to describe the collective dynamics emerging from graph-mediated pairwise interactions in multi-agent systems. We formally show that for large graphs and specific classes of interact ...

Cambridge2024

Spatiotemporal wildfire modeling through point processes with moderate and extreme marks

Jonathan Koh Boon Han

Accurate spatiotemporal modeling of conditions leading to moderate and large wildfires provides better understanding of mechanisms driving fire-prone ecosystems and improves risk management. Here, we develop a joint model for the occurrence intensity and t ...

2023

Sparse Attacks for Manipulating Explanations in Deep Neural Network Models

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Michail Vlachos, Ahmad Ajalloeian

We investigate methods for manipulating classifier explanations while keeping the predictions unchanged. Our focus is on using a sparse attack, which seeks to alter only a minimal number of input features. We present an efficient and novel algorithm for co ...

Los Alamitos2023

DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications

Pascal Frossard, Ádám Dániel Ivánkay

Along with the successful deployment of deep neural networks in several application domains, the need to unravel the black-box nature of these networks has seen a significant increase recently. Several methods have been introduced to provide insight into t ...

Assoc Computational Linguistics-Acl2023

CauseOccam: Learning Interpretable Abstract Representations in Reinforcement Learning Environments via Model Sparsity

Sergei Volodin

"I choose this restaurant because they have vegan sandwiches" could be a typical explanation we would expect from a human. However, current Reinforcement Learning (RL) techniques are not able to provide such explanations, when trained on raw pixels. RL alg ...

2021

A Statistical Test for Probabilistic Fairness

Daniel Kuhn, Viet Anh Nguyen, Bahar Taskesen

Algorithms are now routinely used to make consequential decisions that affect human lives. Examples include college admissions, medical interventions or law enforcement. While algorithms empower us to harness all information hidden in vast amounts of data, ...

2021

Bayesian Monte Carlo assimilation for the PETALE experimental programme using inter-dosimeter correlation

Andreas Pautz, Vincent Pierre Lamirand, Dimitri Rochman, Axel Guy Marie Laureau

This article presents the methodology developed to generate and use dosimeter covariances and to estimate nuisance parameters for the PETALE experimental programme. In anticipation of the final experimental results, this work investigates the consideration ...

2020

Regularization via Mass Transportation

Daniel Kuhn, Soroosh Shafieezadeh Abadeh, Peyman Mohajerin Esfahani

The goal of regression and classification methods in supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical distribution. When facing scarce training data, ...

2019

A conceptual framework to study the dynamics of routine practice transitions

Franziska Meinherz

Actors from academia, politics, and the civil society increasingly acknowledge that to address our current unsustainable regimes of resource and energy demand, we need to redefine our ways of being and doing. Everyday practices are a key driver of energy a ...

2018

Models and Algorithms in Biological Network Evolution with Modularity

Min Ye

Networks are commonly used to represent key processes in biology; examples include transcriptional regulatory networks, protein-protein interaction (PPI) networks, metabolic networks, etc. Databases store many such networks, as graphs, observed or inferred ...

EPFL2017

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