Publications associées à Modèle graphique

Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons

Sadegh Farhadkhani, Oscar Jean Olivier Villemaud, Julien René Pierre Fageot, Le Nguyen Hoang

Many applications, e.g. in content recommendation, sports, or recruitment, leverage the comparisons of alternatives to score those alternatives. The classical Bradley-Terry model and its variants have been widely used to do so. The historical model conside ...

AAAI Press2024

An Ordinal Latent Variable Model of Conflict Intensity

Robert West

Measuring the intensity of events is crucial for monitoring and tracking armed conflict. Advances in automated event extraction have yielded massive data sets of '' who did what to whom '' micro-records that enable datadriven approaches to monitoring confl ...

Assoc Computational Linguistics-Acl2023

Robust Outlier Rejection for 3D Registration with Variational Bayes

Mathieu Salzmann, Zheng Dang, Jiancheng Yang, Zhen Wei, Haobo Jiang

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier f ...

Ieee Computer Soc2023

Low-Rank Tensor Approximations for Solving Multimarginal Optimal Transport Problems\ast

Daniel Kressner, Christoph Max Strössner

By the addition of entropic regularization, multimarginal optimal transport problems can be trans-formed into tensor scaling problems, which can be solved numerically using the multimarginal Sinkhorn algorithm. The main computational bottleneck of this alg ...

SIAM PUBLICATIONS2023

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

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

Nature vs. Nurture: Feature vs. Structure for Graph Neural Networks

Karl Aberer, Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Chi Thang Duong

Graph neural networks take node features and graph structure as input to build representations for nodes and graphs. While there are a lot of focus on GNN models, understanding the impact of node features and graph structure to GNN performance has received ...

ELSEVIER2022

HybridSDF: Combining Deep Implicit Shapes and Geometric Primitives for 3D Shape Representation and Manipulation

Pascal Fua, Pierre Bruno Baqué, Nicolas Talabot, Subeesh Vasu, Artem Lukoianov

Deep implicit surfaces excel at modeling generic shapes but do not always capture the regularities present in manufactured objects, which is something simple geometric primitives are particularly good at. In this paper, we propose a representation combinin ...

2022

Representing graphs through data with learning and optimal transport

Hermina Petric Maretic

Graphs offer a simple yet meaningful representation of relationships between data. This representation is often used in machine learning algorithms in order to incorporate structural or geometric information about data. However, it can also be used in an i ...

EPFL2021

Atomic subgraphs and the statistical mechanics of networks

Sofia Charlotta Olhede

We develop random graph models where graphs are generated by connecting not only pairs of vertices by edges, but also larger subsets of vertices by copies of small atomic subgraphs of arbitrary topology. This allows for the generation of graphs with extens ...

2021

Discrete-Choice Mining of Social Processes

Victor Kristof

Poor decisions and selfish behaviors give rise to seemingly intractable global problems, such as the lack of transparency in democratic processes, the spread of conspiracy theories, and the rise in greenhouse gas emissions. However, people are more predict ...

EPFL2021

Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection

Mahsa Shoaran, Bingzhao Zhu

Modern machine learning tools have shown promise in detecting symptoms of neurological disorders. However, current approaches typically train a unique classifier for each subject. This subject-specific training scheme requires long labeled recordings from ...

IEEE2021

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Sylvain Calinon, Emmanuel Pignat

Probability distributions are key components of many learning from demonstration (LfD) approaches, with the spaces chosen to represent tasks playing a central role. Although the robot configuration is defined by its joint angles, end-effector poses are oft ...

2021

Probabilistic Adaptive Control for Robust Behavior Imitation

Sylvain Calinon, Julius Maximilian Jankowski, Hakan Girgin

In the context of learning from demonstration (LfD), trajectory policy representations such as probabilistic movement primitives (ProMPs) allow for rich modeling of demonstrated skills. To reproduce a learned skill with a real robot, a feedback controller ...

IEEE2021

Re-evaluating natural resource investments under uncertainty: An alternative to limited traditional approaches

Sebastian Maier

The need to evaluate natural resource investments under uncertainty has given rise to the development of real options valuation; however, the analysis of such investments has been restricted by the capabilities of existing valuation approaches. We re-visit ...

2021

Distributed Coupled Multiagent Stochastic Optimization

Ali H. Sayed, Sulaiman A S A E Alghunaim

This paper develops an effective distributed strategy for the solution of constrained multiagent stochastic optimization problems with coupled parameters across the agents. In this formulation, each agent is influenced by only a subset of the entries of a ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Pairwise Comparisons with Flexible Time-Dynamics

Matthias Grossglauser, Victor Kristof, Lucas Maystre

Inspired by applications in sports where the skill of players or teams competing against each other varies over time, we propose a probabilistic model of pairwise-comparison outcomes that can capture a wide range of time dynamics. We achieve this by replac ...

ASSOC COMPUTING MACHINERY2019

Deep Unfolding for Communications Systems: A Survey and Some New Directions

Alexios Konstantinos Balatsoukas Stimming, Christoph Studer

Deep unfolding is a method of growing popularity that fuses iterative optimization algorithms with tools from neural networks to efficiently solve a range of tasks in machine learning, signal and image processing, and communication systems. This survey sum ...

IEEE2019

Reconciling Matching Networks of Conceptual Models

Karl Aberer, Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Zoltán Miklós

Conceptual models such as database schemas, ontologies or process models have been established as a means for effective engineering of information systems. Yet, for complex systems, conceptual models are created by a variety of stakeholders, which calls fo ...

2019

Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation

Boi Faltings, Vincent Jean Fabrice Schickel, Stéphane Bernard Martin

We describe the selection, implementation and online evaluation of two e-commerce recommender systems developed with our partner company, Prediggo. The first one is based on the novel method of Bayesian Variable-order Markov Modeling (BVMM). The second, SS ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2019