Action Matching: Learning Stochastic Dynamics from Samples

Learning the continuous dynamics of a system from snapshots of its temporal marginals is a problem which appears throughout natural sciences and machine learning, including in quantum systems, single-cell biological data, and generative modeling. In these settings, we assume access to cross-sectional samples that are uncorrelated over time, rather than full trajectories of samples. In order to better understand the systems under observation, we would like to learn a model of the underlying process that allows us to propagate samples in time and thereby simulate entire individual trajectories. In this work, we propose Action Matching, a method for learning a rich family of dynamics using only independent samples from its time evolution. We derive a tractable training objective, which does not rely on explicit assumptions about the underlying dynamics and does not require back-propagation through differential equations or optimal transport solvers. Inspired by connections with optimal transport, we derive extensions of Action Matching to learn stochastic differential equations and dynamics involving creation and destruction of probability mass. Finally, we showcase applications of Action Matching by achieving competitive performance in a diverse set of experiments from biology, physics, and generative modeling.Comment: Published in ICML 202

L’IMPACT DE LA QUALITE DE SERVICE SUR LES INTENTIONS COMPORTEMENTALES DE LA CLIENTELE DES BANQUES DE DETAIL AU MAROC

La concurrence dans le secteur de la banque de détail a augmenté de façon spectaculaire ces dernières années. De ce fait, les banques de détail se retrouvent aujourd’hui confrontées à des défis de taille, notamment à l’accroissement de la compétitivité mondiale, l’acharnement de la concurrence, l’évolution constante des attentes des clients, l’impact des nouvelles technologies, ou encore l’augmentation des réclamations des clients. Il en résulte la nécessité d’une compréhension approfondie des intentions comportementales et de la satisfaction de la clientèle, et ce, afin de rester compétitif dans un contexte économique marqué par une compétition accrue entre les différents acteurs économiques. Eu égard à la croissance exponentielle et à la nature des réclamations de la clientèle des banques de détail au Maroc qui revendiquent de plus en plus l’amélioration de certains attributs du service bancaire, cet article se propose d’étudier la relation entre la qualité perçue du service bancaire et les intentions comportementales des clients bancaires. L’objet étant d’inciter les prestataires de services bancaires de détail à réévaluer le niveau de qualité de leurs services et à reconnaître les facteurs importants qui influencent la satisfaction des clients, les intentions comportementales et la fidélité de la clientèle. La collecte des données a été réalisée à partir de la méthodologie de recherche documentaire, qui s’est articulée autour de quatre étapes clés, à savoir la préparation de la recherche, la sélection des sources d’information, la recherche et la localisation des documents, et enfin, l’évaluation de la qualité et de la pertinence des sources. L’échantillon s’est composé de ressources bibliographiques pluridisciplinaires, en plus de ressources bibliographiques spécialisées. Les principaux résultats ont démontré que la satisfaction et la qualité de service ont été identifiées comme ayant un impact sur la fidélité par le biais des intentions de rachat et que la qualité de service a un impact positif et significatif sur la rentabilité des banques. Ainsi, les conclusions ont affirmé que le succès d’un établissement bancaire repose sur le fait que les clients lui associent une qualité supérieure, ce qui établit la satisfaction et la fidélité de la clientèle. Par conséquent, fournir une excellente qualité aux clients bancaires est indispensable afin de survivre et prospérer dans un paysage bancaire hautement concurrentiel. Due to a significant surge in competition in the retail banking sector in recent years, nowadays retail banks are facing major challenges such as increasing global competitiveness, ever-changing customer expectations and soaring customer complaints. As a result, banks need to deeply understand their customers’ behavioral intentions in order to remain competitive in a global economic environment characterized by a ruthless competition among aggressive economic players. This article aims to evaluate the relationship between the behavioral intentions of retail banking customers and the perceived service quality in light of the nature and growth of customer complaints, which continue to claim the improvement of certain attributes of banking services. The purpose is to incite retail banking service providers to reassess the level of their service quality and recognize the important factors that influence customer satisfaction, behavioral intentions and loyalty of their customers. Data collection was carried out using a documentary research method, which was based on four main steps, namely analyzing the subject, selecting the right sources, collecting data and evaluating the authenticity, credibility, representativeness and meaning of results. The research sample consisted of multidisciplinary and specialized bibliographic resources. The main results of the study showed that showed that satisfaction and perceived service quality have an impact on loyalty through repurchase intentions and that perceived service quality has a positive and significant impact on banks' profitability. The findings also asserted that the success of a banking institution is based on the fact that customers associate it with superior quality, which results in customer satisfaction and loyalty. Therefore, providing excellent quality to banking customers is fundamental to thrive in a cut-throat banking environment

A Computational Framework for Solving Wasserstein Lagrangian Flows

The dynamical formulation of the optimal transport can be extended through various choices of the underlying geometry ($\textit{kinetic energy}$), and the regularization of density paths ($\textit{potential energy}$). These combinations yield different variational problems ($\textit{Lagrangians}$), encompassing many variations of the optimal transport problem such as the Schr\"odinger bridge, unbalanced optimal transport, and optimal transport with physical constraints, among others. In general, the optimal density path is unknown, and solving these variational problems can be computationally challenging. Leveraging the dual formulation of the Lagrangians, we propose a novel deep learning based framework approaching all of these problems from a unified perspective. Our method does not require simulating or backpropagating through the trajectories of the learned dynamics, and does not need access to optimal couplings. We showcase the versatility of the proposed framework by outperforming previous approaches for the single-cell trajectory inference, where incorporating prior knowledge into the dynamics is crucial for correct predictions

Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schr\"odinger Equation

Solving the quantum many-body Schr\"odinger equation is a fundamental and challenging problem in the fields of quantum physics, quantum chemistry, and material sciences. One of the common computational approaches to this problem is Quantum Variational Monte Carlo (QVMC), in which ground-state solutions are obtained by minimizing the energy of the system within a restricted family of parameterized wave functions. Deep learning methods partially address the limitations of traditional QVMC by representing a rich family of wave functions in terms of neural networks. However, the optimization objective in QVMC remains notoriously hard to minimize and requires second-order optimization methods such as natural gradient. In this paper, we first reformulate energy functional minimization in the space of Born distributions corresponding to particle-permutation (anti-)symmetric wave functions, rather than the space of wave functions. We then interpret QVMC as the Fisher--Rao gradient flow in this distributional space, followed by a projection step onto the variational manifold. This perspective provides us with a principled framework to derive new QMC algorithms, by endowing the distributional space with better metrics, and following the projected gradient flow induced by those metrics. More specifically, we propose "Wasserstein Quantum Monte Carlo" (WQMC), which uses the gradient flow induced by the Wasserstein metric, rather than Fisher--Rao metric, and corresponds to transporting the probability mass, rather than teleporting it. We demonstrate empirically that the dynamics of WQMC results in faster convergence to the ground state of molecular systems