Publications associées à Inductive probability

Keep Sensors in Check: Disentangling Country-Level Generalization Issues in Mobile Sensor-Based Models with Diversity Scores

Daniel Gatica-Perez, Lakmal Buddika Meegahapola

Machine learning models trained with passive sensor data from mobile devices can be used to perform various inferences pertaining to activity recognition, context awareness, and health and well-being. Prior work has improved inference performance through t ...

Assoc Computing Machinery2023

Essays in macro-finance and deep learning

Goutham Gopalakrishna

This thesis studies the origins and consequences of financial crises, and computational techniques to solve continuous-time economic models that explain such crises. The first chapter shows that financial recessions are typically characterised by a large ...

EPFL2023

Scalable Multi-agent Coordination and Resource Sharing

Panayiotis Danassis

A plethora of real world problems consist of a number of agents that interact, learn, cooperate, coordinate, and compete with others in ever more complex environments. Examples include autonomous vehicles, robotic agents, intelligent infrastructure, IoT de ...

EPFL2022

Learning Analytics for Adaptive and Self-Improving Learning Environments for Inductive Teaching

Louis Pierre Faucon

The emergence of digital technology is changing education in many ways. A particularly interesting aspect of this transformation is the development of learning environments that can automatically adapt to individual students and can collect data in order t ...

EPFL2021

Distributed Meta-Learning with Networked Agents

Ali H. Sayed, Mert Kayaalp, Stefan Vlaski

Meta-learning aims to improve efficiency of learning new tasks by exploiting the inductive biases obtained from related tasks. Previous works consider centralized or federated architectures that rely on central processors, whereas, in this paper, we propos ...

EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP2021

Does It Pay to Do Novel Science? The Selectivity Patterns in Science Funding

Fabiana Visentin, Charles Chadi Ayoubi, Michele Pezzoni

Public funding agencies aim to fund novel breakthrough research to promote the radical scientific discoveries of tomorrow. Identifying the profiles of scientists being financed to pursue their research is therefore crucial. This paper shows that the fundin ...

OXFORD UNIV PRESS2021

A Pareto Dominance Principle for Data-Driven Optimization

Daniel Kuhn, Tobias Sutter

We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that maps the available training data to a feasible action. It can always be exp ...

2024

A t-distribution based operator for enhancing out of distribution robustness of neural network classifiers

Philip Neil Garner

Neural Network (NN) classifiers can assign extreme probabilities to samples that have not appeared during training (out-of-distribution samples) resulting in erroneous and unreliable predictions. One of the causes for this unwanted behaviour lies in the us ...

IEEE Institute of Electrical and Electronics Engineers2020

Context effects on probability estimation

Wei-Hsiang Lin

Many decisions rely on how we evaluate potential outcomes and estimate their corresponding probabilities of occurrence. Outcome evaluation is subjective because it requires consulting internal preferences and is sensitive to context. In contrast, probabili ...

PUBLIC LIBRARY SCIENCE2020

Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning

Ali H. Sayed, Stefan Vlaski, Jie Chen, Roula Nassif

The problem of simultaneously learning several related tasks has received considerable attention in several domains, especially in machine learning, with the so-called multitask learning (MTL) problem, or learning to learn problem [1], [2]. MTL is an appro ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020