Publications associées à Bayesian optimization

Mesh-based topology, shape and sizing optimization of ribbed plates

In this paper, we present a new parameterization and optimization procedure for minimizing the weight of ribbed plates. The primary goal is to reduce embodied CO2 in concrete floors as part of the effort to diminish the carbon footprint of the construction ...

Springer2024

Machine learning-aided generative molecular design

Philippe Schwaller, Jeff Guo

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

Bayesian Optimization for Chemical Reactions

Philippe Schwaller, Jeff Guo, Bojana Rankovic

Reaction optimization is challenging and traditionally delegated to domain experts who iteratively pro-pose increasingly optimal experiments. Problematically, the reaction landscape is complex and often requires hundreds of experiments to reach convergence ...

Swiss Chemical Soc2023

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

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

Martin Jaggi, Thijs Vogels, Hadrien Hendrikx

In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In the decentralized setting, in which workers commu ...

Microtome Publishing2023

Improving Generalization of Pretrained Language Models

Rabeeh Karimi Mahabadi

In this dissertation, we propose multiple methods to improve transfer learning for pretrained language models (PLMs). Broadly, transfer learning is a powerful technique in natural language processing, where a language model is first pre-trained on a data-r ...

EPFL2023

Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti

We address the problem of designing stabilizing control policies for nonlinear systems in discrete-time, while minimizing an arbitrary cost function. When the system is linear and the cost is convex, the System Level Synthesis (SLS) approach offers an effe ...

IEEE2022

Bayesian Inversion for Nonlinear Imaging Models Using Deep Generative Priors

Michaël Unser, Thanh-An Michel Pham, Pakshal Narendra Bohra, Jonathan Yuelin Dong

Most modern imaging systems incorporate a computational pipeline to infer the image of interest from acquired measurements. The Bayesian approach to solve such ill-posed inverse problems involves the characterization of the posterior distribution of the im ...

2022

Optimizing Latent Space Directions For GAN-based Local Image Editing

Sabine Süsstrunk, Ehsan Pajouheshgar, Tong Zhang

Generative Adversarial Network (GAN) based localized image editing can suffer from ambiguity between semantic attributes. We thus present a novel objective function to evaluate the locality of an image edit. By introducing the supervision from a pre-traine ...

IEEE2021

Optimization methods for collaborative learning

Sai Praneeth Reddy Karimireddy

A traditional machine learning pipeline involves collecting massive amounts of data centrally on a server and training models to fit the data. However, increasing concerns about the privacy and security of user's data, combined with the sheer growth in the ...

EPFL2021

Learning Self-Exciting Temporal Point Processes Under Noisy Observations

William Trouleau

Understanding the diffusion patterns of sequences of interdependent events is a central question for a variety of disciplines. Temporal point processes are a class of elegant and powerful models of such sequences; these processes have become popular across ...

EPFL2021

FEM Based Statistical Data-Driven Modeling Approach for MFT Design Optimization

Drazen Dujic, Marko Mogorovic

This paper proposes a novel class of neural-network inspired statistical data-driven models, especially derived for the purpose of design optimization of medium frequency transformers. These models allow for an efficient (3-5 orders of magnitude faster com ...

2020

Introducing reinforcement learning to the energy system design process

Jean-Louis Scartezzini, Amarasinghage Tharindu Dasun Perera, Pamuditha Udaranga Wickramasinghe, Vahid Moussavi Nik

Design optimization of distributed energy systems has become an interest of a wider group of researchers due the capability of these systems to integrate non-dispatchable renewable energy technologies such as solar PV and wind. White box models, using line ...

2020

Robust Adaptive Decision Making: Bayesian Optimization and Beyond

Ilija Bogunovic

The central task in many interactive machine learning systems can be formalized as the sequential optimization of a black-box function. Bayesian optimization (BO) is a powerful model-based framework for \emph{adaptive} experimentation, where the primary go ...

EPFL2019

A Regularization Framework for Learning Over Multitask Graphs

Ali H. Sayed, Stefan Vlaski, Roula Nassif

This letter proposes a general regularization framework for inference over multitask networks. The optimization approach relies on minimizing a global cost consisting of the aggregate sum of individual costs regularized by a term that allows to incorporate ...

IEEE Institute of Electrical and Electronics Engineers2019

Efficiently Learning Fourier Sparse Set Functions

Amir Zandieh, Andreas Krause, Mikhail Kapralov

Learning set functions is a key challenge arising in many domains, ranging from sketching graphs to black-box optimization with discrete parameters. In this paper we consider the problem of efficiently learning set functions that are defined over a ground ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Dual Generator Generative Adversarial Networks for Multi-domain Image-to-Image Translation

Yan Yan, Wei Wang

State-of-the-art methods for image-to-image translation with Generative Adversarial Networks (GANs) can learn a mapping from one domain to another domain using unpaired image data. However, these methods require the training of one specific model for every ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient

Mohammad Emtiyaz Khan

Uncertainty estimation in large deep-learning models is a computationally challenging task, where it is difficult to form even a Gaussian approximation to the posterior distribution. In such situations, existing methods usually resort to a diagonal approxi ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Discovering Interaction Patterns in Online Learning Environments : A Learning Analytics Research

Mina Shirvani Boroujeni

The increasing amount of data collected in online learning environments provides unique opportunities to better understand the learning processes in different educational settings. Learning analytics research aims at understanding and optimizing learning a ...

EPFL2018

Mean-Field methods for Structured Deep-Learning in Computer Vision

Pierre Bruno Baqué

In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predi ...

EPFL2018