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Kalousis Alexandros

Professeur HES ordinaire

Main skills

Professeur HES ordinaire

Desktop: F 124

Haute école de gestion de Genève
Campus Battelle, Rue de la Tambourine 17, 1227 Carouge, CH

Faculty
Economie et services

Main Degree Programme
Informatique de gestion

BSc HES-SO en Informatique de gestion - Haute école de gestion de Genève

Data Mining

BSc HES-SO en Economie d'entreprise - Haute école de gestion de Genève

Machine Learning
Deep Learning

Ongoing

SimGait, Modeling pathological gait resulting from motor impairments: compare and combine neuromechanical simulation and machine learning approaches

Role: Co-applicant

Requérant(e)s: Armand Stéphane, Laboratoire de Cinésiologie Willy Taillard Hôpitaux Universitaires de Genève

Financement: SNSF

Description du projet :

The aim ofSimGait is to create a musculoskeletal model of the human with neural control to be able to model healthy and impaired gait, for example due to cerebral palsy. The model consista of a dynamics model that models the motion of the legs and trunk, and is operated by muscle forces. Machine learning methods will be used to predict a patient’s gait from their clinical data using a data-driven model as well as to learn controllers that will imitate the gait of individual patients. The overarching goal is to create models that will allow medical doctors to explore the effect that different treatments will have on the gait of any given patient.

Research team within HES-SO: Kalousis Alexandros

Partenaires académiques: Ijspeert Auke Jan, Laboratoire de biorobotique EPFL - STI - IBI - BIOROB, Lausanne; Armand Stéphane, Laboratoire de Cinésiologie Willy Taillard Hôpitaux Universitaires de Genève

Durée du projet: 01.09.2018 - 31.08.2022

Montant global du projet: 2'115'000 CHF

Statut: Ongoing

Completed

Road-, Air- and Water-based Future Internet Experimentation H2020-ICT-2014-1

AGP

Role: Main Applicant

Financement: Commission européenne

Description du projet : The purpose of the RAWFIE initiative is to create a federation of different network testbeds that will work together to make their resources available under a common framework. Specifically, it aims at delivering a unique, mixed experimentation environment across the space and technology dimensions. RAWFIE will integrate numerous testbeds for experimenting in vehicular (road), aerial and maritime environments. A Vehicular Testbed (VT) will deal with Unmanned Ground Vehicles (UGVs) while an Aerial Testbed (AT) and a Maritime Testbed (MT) will deal with Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) respectively. The RAWFIE consortium includes all the possible actors of this highly challenging experimentation domain, from technology creators to integrators and facility owners. The basic idea behind the RAWFIE effort is the automated, remote operation of a large number of robotic devices (UGVs, UAVs, USVs) for the purpose of assessing the performance of different technologies in the networking, sensing and mobile/autonomic application domains. RAWFIE will feature a significant number of UxV nodes for exposing to the experimenter a vast test infrastructure. All these items will be managed by a central controlling entity which will be programmed per case and fully overview/drive the operation of the respective mechanisms (e.g., auto-pilots, remote controlled ground vehicles). Internet connectivity will be extended to the mobile units to enable the remote programming (over-the-air), control and data collection. Support software for experiment management, data collection and post-analysis will be virtualized to enable experimentation from everywhere in the world. The vision of Experimentation-as-a-Service (EaaS) will be promoted through RAWFIE. The IoT paradigm will be fully adopted and further refined for support of highly dynamic node architectures.

Research team within HES-SO: Kalousis Alexandros , Blonde Lionel , Ramapuram Jason Emmanuel

Partenaires académiques: 539,Informatique de gestion; Kalousis Alexandros, 539,Informatique de gestion

Durée du projet: 01.01.2015 - 31.03.2019

Montant global du projet: 623'271 CHF

Statut: Completed

Learning olfactory models to support the parfum creation process 19291.1 PFES-ES

AGP

Role: Main Applicant

Financement: CTI; Firmenich

Description du projet : We aim at developing rational solutions for improving product performance and differentiation through data-driven and computational approaches. Statistical learning algorithms embeding side information are studied with the objective to design reliable models assessing product properties and qualities.

Research team within HES-SO: Kalousis Alexandros , Strasser Pablo , Aminanmu Maolaaisha , Lavda Frantzeska

Partenaires académiques: 539,Informatique de gestion

Durée du projet: 01.01.2017 - 28.02.2019

Statut: Completed

Machine learning tools exploiting hidden structures for forecasting multivariate time series (Hidden Structures in Time Series)

AGP

Role: Main Applicant

Financement: HES-SO Rectorat

Description du projet : We will develop new methods and tools for forecasting multivariate time series that exploit hidden structures in the data to improve the accuracy of the forecasts. These shall be able to cope with time series from various application areas, such as financial and economic, transport, or electricity supply and demand, where there are large numbers of indicators developing in parallel along non-trivial and possibly unstable structures in their relationships.

Research team within HES-SO: Kalousis Alexandros , Gregorova Magda

Partenaires académiques: 539,Informatique de gestion

Durée du projet: 01.11.2016 - 01.05.2018

Montant global du projet: 158'030 CHF

Statut: Completed

Novel forecasting tools for very large scale time series systems

AGP

Role: Main Applicant

Financement: HES-SO Rectorat

Description du projet : We will use a specific application area of air trafic forecasting to direct our research in learning multivariate time series models regularized by data-driven yet meaningful constraints. The objective is to develop algorithms that reduce dimensionality and improve the predictive performance of the models by exploiting additional knowledge from domain-theory, the specific spatial structure of the data (low network), or learned in a multi-task setting.

Research team within HES-SO: Kalousis Alexandros , Gregorova Magda

Partenaires académiques: 539,Informatique de gestion; Kalousis Alexandros, 539,Informatique de gestion

Durée du projet: 01.11.2013 - 30.04.2015

Montant global du projet: 157'000 CHF

Statut: Completed

Combining metric and kernel learning

AGP

Role: Main Applicant

Financement: HES-SO Rectorat; 539,Informatique de gestion; FNS - Fonds national suisse

Description du projet : Kernel and metric learning have become very active research fields in machine learning over the last years. Although they have developed as distinct research fields they share common elements. One of the most popular approaches to kernel learning is learning a linear combination of a set of kernels, usually identified as Multiple Kernel Learning (MKL), this essentially corresponds to learning a block diagonal transformation of the concatenation feature space induced by the concatenation of the feature spaces that correspond to the basis kernels. On the metric learning side, probably the most prominent approach is learning a Mahalanobis distance in some feature space which in fact corresponds to learning a linear transformation in that feature space. So both methods learn linear feature transformations, where in the case of the MKL the learned transformation has a specific structure. Many of the metric learning methods are kernelized which raises the issue of which kernel should one use for a given problem, nevertheless there is no work so far that tries to combine metric learning with MKL. On the other hand since MKL is learning a special form of linear transformation over the concatenation feature space one could use metric learning techniques in order to learn such linear transformations or more general forms of them. In fact one can use metric learning techniques to learn linear transformations over the feature space induced by any kernel. On the same time, and despite the increasing popularity of metric learning methods, there exist so far no such method that will scale well with increasing problem sizes, i.e. large feature space dimensionality and large number of instances, and on the same time retain a good generalization performance. In the present proposal we want to take a step to address the issues briefly described above. More precisely the work described in the present proposal is divided into two workpackages. In the first workpackage we link metric learning and kernel learning methods, by using tools from one domain in the other and vice versa. In the second workpackage we will propose metric learning methods that can scale well with large datasets. The work of the first workpackage is divided into two main tasks. In the first task we will combine metric learning with MKL, i.e. learning metrics over kernels learned by MKL. We will explore different metric parametrizations which will lead to different learning problems. In the second task we will go on the opposite direction and use metric learning ideas for kernel learning. More precisely we will learn linear transformations of the feature space induced by some kernel, whether this is a kernel that is learned or it is a standard single kernel. By learning a linear transformation of the feature space we are in 31.03.2011 20:30:52 Page - 6 - fact learning in the general case a new non-linear, quadratic, kernel. We will experiment with different objective functions in order to learn the linear transformations. In the second workpackage we will also have two main tasks. In the first we will explore the use of stochastic gradient descent methods in order to improve the scalability of metric learning. In the second task we will go to the extreme case of metric learning and we will learn a linear transformation of rank one in order to make metric learning possible for very large datasets. At first we might think that learning a rank one metric might be too restrictive limiting its application only to simple learning problems. Nevertheless, by kernelizing it, we can apply it on learning problems of any complexity.

Research team within HES-SO: Kalousis Alexandros

Durée du projet: 01.10.2011 - 31.08.2013

Montant global du projet: 56'820 CHF

Statut: Completed

2023

Semi-supervised variational autoencoders for out-of-distribution generation

Scientific paper ArODES

Frantzeska Lavda, Alexandros Kalousis

Entropy, 25, 12, 1659

Link to the publication

Summary:

Humans are able to quickly adapt to new situations, learn effectively with limited data, and create unique combinations of basic concepts. In contrast, generalizing out-of-distribution (OOD) data and achieving combinatorial generalizations are fundamental challenges for machine learning models. Moreover, obtaining high-quality labeled examples can be very time-consuming and expensive, particularly when specialized skills are required for labeling. To address these issues, we propose BtVAE, a method that utilizes conditional VAE models to achieve combinatorial generalization in certain scenarios and consequently to generate out-of-distribution (OOD) data in a semi-supervised manner. Unlike previous approaches that use new factors of variation during testing, our method uses only existing attributes from the training data but in ways that were not seen during training (e.g., small objects of a specific shape during training and large objects of the same shape during testing).

2022

Lipschitzness is all you need to tame off-policy generative adversarial imitation learning

Scientific paper ArODES

Lionel Blondé, Pablo Strasser, Alexandros Kalousis

Machine Learning, 2022, Vol. 111, pp. 1431–1521

Link to the publication

Summary:

Despite the recent success of reinforcement learning in various domains, these approaches remain, for the most part, deterringly sensitive to hyper-parameters and are often riddled with essential engineering feats allowing their success. We consider the case of off-policy generative adversarial imitation learning, and perform an in-depth review, qualitative and quantitative, of the method. We show that forcing the learned reward function to be local Lipschitz-continuous is a sine qua non condition for the method to perform well. We then study the effects of this necessary condition and provide several theoretical results involving the local Lipschitzness of the state-value function. We complement these guarantees with empirical evidence attesting to the strong positive effect that the consistent satisfaction of the Lipschitzness constraint on the reward has on imitation performance. Finally, we tackle a generic pessimistic reward preconditioning add-on spawning a large class of reward shaping methods, which makes the base method it is plugged into provably more robust, as shown in several additional theoretical guarantees. We then discuss these through a fine-grained lens and share our insights. Crucially, the guarantees derived and reported in this work are valid for any reward satisfying the Lipschitzness condition, nothing is specific to imitation. As such, these may be of independent interest.

Can I trust this location estimate ? :

Scientific paper ArODES

reproducibly benchmarking the methods of dynamic accuracy estimation of localization

Grigorios G. Anagnostopoulos, Alexandros Kalousis

Sensors, 2022, Vol. 22, no. 3, article no. 1088

Link to the publication

Summary:

Despite the great attention that the research community has paid to the creation of novel indoor positioning methods, a rather limited volume of works has focused on the confidence that Indoor Positioning Systems (IPS) assign to the position estimates that they produce. The concept of estimating, dynamically, the accuracy of the position estimates provided by an IPS has been sporadically studied in the literature of the field. Recently, this concept has started being studied as well in the context of outdoor positioning systems of Internet of Things (IoT) based on Low-Power Wide-Area Networks (LPWANs). What is problematic is that the consistent comparison of the proposed methods is quasi nonexistent: new methods rarely use previous ones as baselines; often, a small number of evaluation metrics are reported while different metrics are reported among different relevant publications, the use of open data is rare, and the publication of open code is absent. In this work, we present an open-source, reproducible benchmarking framework for evaluating and consistently comparing various methods of Dynamic Accuracy Estimation (DAE). This work reviews the relevant literature, presenting in a consistent terminology commonalities and differences and discussing baselines and evaluation metrics. Moreover, it evaluates multiple methods of DAE using open data, open code, and a rich set of relevant evaluation metrics. This is the first work aiming to establish the state of the art of methods of DAE determination in IPS and in LPWAN positioning systems, through an open, transparent, holistic, reproducible, and consistent evaluation of the methods proposed in the relevant literature.

2020

Lifelong generative modeling

Scientific paper ArODES

Jason Ramapuram, Magda Gregorova, Alexandros Kalousis

Neurocomputing, 2020, vol. 404, pp. 381-400

Link to the publication

Summary:

Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner, where knowledge gained from previous tasks is retained and used to aid future learning over the lifetime of the learner. It is essential towards the development of intelligent machines that can adapt to their surroundings. In this work we focus on a lifelong learning approach to unsupervised generative modeling, where we continuously incorporate newly observed distributions into a learned model. We do so through a student-teacher Variational Autoencoder architecture which allows us to learn and preserve all the distributions seen so far, without the need to retain the past data nor the past models. Through the introduction of a novel cross-model regularizer, inspired by a Bayesian update rule, the student model leverages the information learned by the teacher, which acts as a probabilistic knowledge store. The regularizer reduces the effect of catastrophic interference that appears when we learn over sequences of distributions. We validate our model’s performance on sequential variants of MNIST, FashionMNIST, PermutedMNIST, SVHN and Celeb-A and demonstrate that our model mitigates the effects of catastrophic interference faced by neural networks in sequential learning scenarios.

Data-dependent conditional priors for unsupervised learning of multimodal data

Scientific paper ArODES

Frantzeska Lavda, Magda Gregorová, Alexandros Kalousis

Entropy, 2020, vol. 22, no 8, pp. 1-34

Link to the publication

Summary:

One of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. This is primarily due to the use of a simple isotropic Gaussian as the prior for the latent code in the ancestral sampling procedure for data generations. In this paper, we propose a novel formulation of variational autoencoders, conditional prior VAE (CP-VAE), with a two-level generative process for the observed data where continuous z and a discrete c variables are introduced in addition to the observed variables x. By learning data-dependent conditional priors, the new variational objective naturally encourages a better match between the posterior and prior conditionals, and the learning of the latent categories encoding the major source of variation of the original data in an unsupervised manner. Through sampling continuous latent code from the data-dependent conditional priors, we are able to generate new samples from the individual mixture components corresponding, to the multimodal structure over the original data. Moreover, we unify and analyse our objective under different independence assumptions for the joint distribution of the continuous and discrete latent variables. We provide an empirical evaluation on one synthetic dataset and three image datasets, FashionMNIST, MNIST, and Omniglot, illustrating the generative performance of our new model comparing to multiple baselines.

2018

Biomedical ontology alignment :

Scientific paper ArODES

an approach based on representation learning

Prodromos Kolyvakis, Alexandros Kalousis, Barry Smith, Dimitris Kiritsis

Journal of biomedical semantics, 2018, vol. 9, no. 21, pp. 1-20

Link to the publication

Summary:

Background: While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. Results: An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results. Conclusions: Our proposed representation learning approach leverages terminological embeddings to capture semantic similarity. Our results provide evidence that the approach produces embeddings that are especially well tailored to the ontology matching task, demonstrating a novel pathway for the problem.

2016

Factorizing LambdaMART for cold start recommendations

Scientific paper ArODES

Phong Nguyen, Jun Wang, Alexandros Kalousis

Machine Learning, 2016, Vol. 104, no. 2, pp. 223–242

Link to the publication

Summary:

Recommendation systems often rely on point-wise loss metrics such as the mean squared error. However, in real recommendation settings only few items are presented to a user. This observation has recently encouraged the use of rank-based metrics. LambdaMART is the state-of-the-art algorithm in learning to rank which relies on such a metric. Motivated by the fact that very often the users’ and items’ descriptions as well as the preference behavior can be well summarized by a small number of hidden factors, we propose a novel algorithm, LambdaMART matrix factorization (LambdaMART-MF), that learns latent representations of users and items using gradient boosted trees. The algorithm factorizes LambdaMART by defining relevance scores as the inner product of the learned representations of the users and items. We regularise the learned latent representations so that they reflect the user and item manifolds as these are defined by their original feature based descriptors and the preference behavior. We also propose to use a weighted variant of NDCG to reduce the penalty for similar items with large rating discrepancy. We experiment on two very different recommendation datasets, meta-mining and movies-users, and evaluate the performance of LambdaMART-MF, with and without regularization, in the cold start setting as well as in the simpler matrix completion setting. The experiments show that the factorization of LambdaMart brings significant performance improvements both in the cold start and the matrix completion settings. The incorporation of regularisation seems to have a smaller performance impact.

2015

The Data Mining OPtimization Ontology

Scientific paper ArODES

C. Maria Keeta, Agnieszka Lawrynowiczb, Claudia d’Amato, Alexandros Kalousis, Phong Nguyen, Raul Palma, Robert Stevens, Melanie Hilario

Journal of web semantics, 2015, vol. 32, pp. 43-53

Link to the publication

Summary:

The Data Mining OPtimization Ontology (DMOP) has been developed to support informed decision-making at various choice points of the data mining process. The ontology can be used by data miners and deployed in ontology-driven information systems. The primary purpose for which DMOP has been developed is the automation of algorithm and model selection through semantic meta-mining that makes use of an ontology-based meta-analysis of complete data mining processes in view of extracting patterns associated with mining performance. To this end, DMOP contains detailed descriptions of data mining tasks (e.g., learning, feature selection), data, algorithms, hypotheses such as mined models or patterns, and workflows. A development methodology was used for DMOP, including items such as competency questions and foundational ontology reuse. Several non-trivial modeling problems were encountered and due to the complexity of the data mining details, the ontology requires the use of the OWL 2 DL profile. DMOP was successfully evaluated for semantic meta-mining and used in constructing the Intelligent Discovery Assistant, deployed at the popular data mining environment RapidMiner

2014

Using meta-mining to support data mining workflow planning and optimization

Scientific paper ArODES

Alexandros Kalousis, Hilario Mélanie, Phong Nguyen

Journal of Artificial Intelligence Research. Novembre 2014. Vol.?51, pp.?605-644,

Link to the publication

2011

Ontology-based meta-mining of knowledge discovery workflows

Book chapter ArODES

Mélanie Hilario, Phong Nguyen, Huyen Do, Adam Woznika, Alexandros Kalousis

In : Jankowski, Norbert (eds). Meta-learning in computational intelligence. Berlin, Springer, 2011. P. 273-315. 2011

Link to the publication

2023

Back translation variational autoencoders for OOD generation

Conference ArODES

Frantzeska Lavda, Alexandros Kalousis

ICLR 2023 Workshop on Domain Generalization

PEOPLE@HES-SO Directory and Skills inventory

Kalousis Alexandros

Professeur HES ordinaire

Main skills

Machine Learning

Data mining

Artificial Intelligence (AI)

Biomedical applications

predictive maintenance

Professeur HES ordinaire

PEOPLE@HES-SO
Directory and Skills inventory