8 research outputs found
Recent Advances in Optimal Transport for Machine Learning
Recently, Optimal Transport has been proposed as a probabilistic framework in
Machine Learning for comparing and manipulating probability distributions. This
is rooted in its rich history and theory, and has offered new solutions to
different problems in machine learning, such as generative modeling and
transfer learning. In this survey we explore contributions of Optimal Transport
for Machine Learning over the period 2012 -- 2022, focusing on four sub-fields
of Machine Learning: supervised, unsupervised, transfer and reinforcement
learning. We further highlight the recent development in computational Optimal
Transport, and its interplay with Machine Learning practice.Comment: 20 pages,5 figures,under revie
Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Space
This paper seeks to solve Multi-Source Domain Adaptation (MSDA), which aims
to mitigate data distribution shifts when transferring knowledge from multiple
labeled source domains to an unlabeled target domain. We propose a novel MSDA
framework based on dictionary learning and optimal transport. We interpret each
domain in MSDA as an empirical distribution. As such, we express each domain as
a Wasserstein barycenter of dictionary atoms, which are empirical
distributions. We propose a novel algorithm, DaDiL, for learning via
mini-batches: (i) atom distributions; (ii) a matrix of barycentric coordinates.
Based on our dictionary, we propose two novel methods for MSDA: DaDil-R, based
on the reconstruction of labeled samples in the target domain, and DaDiL-E,
based on the ensembling of classifiers learned on atom distributions. We
evaluate our methods in 3 benchmarks: Caltech-Office, Office 31, and CRWU,
where we improved previous state-of-the-art by 3.15%, 2.29%, and 7.71% in
classification performance. Finally, we show that interpolations in the
Wasserstein hull of learned atoms provide data that can generalize to the
target domain.Comment: 13 pages,8 figures,Accepted as a conference paper at the 26th
European Conference on Artificial Intelligenc
Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning
In this paper, we consider the intersection of two problems in machine
learning: Multi-Source Domain Adaptation (MSDA) and Dataset Distillation (DD).
On the one hand, the first considers adapting multiple heterogeneous labeled
source domains to an unlabeled target domain. On the other hand, the second
attacks the problem of synthesizing a small summary containing all the
information about the datasets. We thus consider a new problem called MSDA-DD.
To solve it, we adapt previous works in the MSDA literature, such as
Wasserstein Barycenter Transport and Dataset Dictionary Learning, as well as DD
method Distribution Matching. We thoroughly experiment with this novel problem
on four benchmarks (Caltech-Office 10, Tennessee-Eastman Process, Continuous
Stirred Tank Reactor, and Case Western Reserve University), where we show that,
even with as little as 1 sample per class, one achieves state-of-the-art
adaptation performance.Comment: 7 pages,4 figure
Multi-Source Domain Adaptation for Cross-Domain Fault Diagnosis of Chemical Processes
Fault diagnosis is an essential component in process supervision. Indeed, it
determines which kind of fault has occurred, given that it has been previously
detected, allowing for appropriate intervention. Automatic fault diagnosis
systems use machine learning for predicting the fault type from sensor
readings. Nonetheless, these models are sensible to changes in the data
distributions, which may be caused by changes in the monitored process, such as
changes in the mode of operation. This scenario is known as Cross-Domain Fault
Diagnosis (CDFD). We provide an extensive comparison of single and multi-source
unsupervised domain adaptation (SSDA and MSDA respectively) algorithms for
CDFD. We study these methods in the context of the Tennessee-Eastmann Process,
a widely used benchmark in the chemical industry. We show that using multiple
domains during training has a positive effect, even when no adaptation is
employed. As such, the MSDA baseline improves over the SSDA baseline
classification accuracy by 23% on average. In addition, under the
multiple-sources scenario, we improve classification accuracy of the no
adaptation setting by 8.4% on average.Comment: 18 pages,15 figure
Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation
In this article, we propose an approach for federated domain adaptation, a
setting where distributional shift exists among clients and some have unlabeled
data. The proposed framework, FedDaDiL, tackles the resulting challenge through
dictionary learning of empirical distributions. In our setting, clients'
distributions represent particular domains, and FedDaDiL collectively trains a
federated dictionary of empirical distributions. In particular, we build upon
the Dataset Dictionary Learning framework by designing collaborative
communication protocols and aggregation operations. The chosen protocols keep
clients' data private, thus enhancing overall privacy compared to its
centralized counterpart. We empirically demonstrate that our approach
successfully generates labeled data on the target domain with extensive
experiments on (i) Caltech-Office, (ii) TEP, and (iii) CWRU benchmarks.
Furthermore, we compare our method to its centralized counterpart and other
benchmarks in federated domain adaptation.Comment: 7 pages,2 figure
OPENDENOISING: AN EXTENSIBLE BENCHMARK FOR BUILDING COMPARATIVE STUDIES OF IMAGE DENOISERS
International audienc
Multi-source domain adaptation through dataset dictionary learning in wasserstein space
International audienceThis paper seeks to solve Multi-Source Domain Adaptation (MSDA), which aims to mitigate data distribution shifts when transferring knowledge from multiple labeled source domains to an unlabeled target domain. We propose a novel MSDA framework based on dictionary learning and optimal transport. We interpret each domain in MSDA as an empirical distribution. As such, we express each domain as a Wasserstein barycenter of dictionary atoms, which are empirical distributions. We propose a novel algorithm, DaDiL, for learning via mini-batches: (i) atom distributions; (ii) a matrix of barycentric coordinates. Based on our dictionary, we propose two novel methods for MSDA: DaDil-R, based on the reconstruction of labeled samples in the target domain, and DaDiL-E, based on the ensembling of classifiers learned on atom distributions. We evaluate our methods in 3 benchmarks: Caltech-Office, Office 31, and CRWU, where we improved previous state-of-the-art by 3.15%, 2.29%, and 7.71% in classification performance. Finally, we show that interpolations in the Wasserstein hull of learned atoms provide data that can generalize to the target domain
Cross-domain fault diagnosis through optimal transport for a CSTR process
Publisher Copyright: © 2022 Elsevier B.V.. All rights reserved.Fault diagnosis is a key task for developing safer control systems, especially in chemical plants. Nonetheless, acquiring good labeled fault data involves sampling from dangerous system conditions. A possible workaround to this limitation is to use simulation data for training data-driven fault diagnosis systems. However, due to modelling errors or unknown factors, simulation data may differ in distribution from real-world data. This setting is known as cross-domain fault diagnosis (CDFD). We use optimal transport for: (i) exploring how modelling errors relate to the distance between simulation (source) and real-world (target) data distributions, and (ii) matching source and target distributions through the framework of optimal transport for domain adaptation (OTDA), resulting in new training data that follows the target distribution. Comparisons show that OTDA outperforms other CDFD methods.Peer reviewe