Partner: Swisscom Partner contact: Dan-Cristian Tomozei EPFL laboratory: Signal Processing Laboratory (LTS4) EPFL contact: Prof. Pascal Frossard, Nikolaos Dimitriadis Customer understanding is a ubiquitous and multifaceted business application whose mission lies in providing better experiences to customers by recognising their needs. A multitude of tasks, ranging from churn prediction to accepting upselling recommendations, fall under this umbrella. Common approaches model each task separately and neglect the common structure some tasks may share. The purpose of this project is to leverage multi-task learning to better understand the behaviour of customers by modeling similar tasks into a single model. This multi-objective approach utilises the information of all involved tasks to generate a common embedding that can be beneficial to all and provide insights into the connection between different user behaviours, i.e. tasks. The project will provide data-driven insights into customer needs leading to retention as well as revenue maximisation while providing a better user experience.View
Partner: Swissquote Partner contact: Serge Kassibrakis EPFL laboratory: Swiss Finance Institute @ EPFL EPFL contact: Prof. Damir Filipovic, Alexis Marchal I propose a new tool to characterize the resolution of uncertainty around FOMC press conferences. It relies on the construction of a measure capturing the level of discussion complexity between the Fed Chair and reporters during the Q&A sessions. I show that complex discussions are associated with higher equity returns and a drop in realized volatility. The method creates an attention score by quantifying how much the Chair needs to rely on reading internal documents to be able to answer a question. This is accomplished by building a novel dataset of video images of the press conferences and leveraging recent deep learning algorithms from computer vision. This alternative data provides new information on nonverbal communication that cannot be extracted from the widely analyzed FOMC transcripts. This paper can be seen as a proof of concept that certain videos contain valuable information for the study of financial markets.View
Partner: armasuisse Partner contact: Alain Mermoud EPFL laboratory: Laboratory for Data Security (LDS) EPFL contact: Prof. Jean-Pierre Hubaux, Juan Troncoso, Romain Bouyé Cyber security information is often extremely sensitive and confidential, it introduces a tradeoff between the benefits of improved threat-response capabilities and the drawbacks of disclosing national-security-related information to foreign agencies or institutions. This results in the retention of valuable information (a.k.a. as the free-rider problem), which considerably limits the efficacy of data sharing. The purpose of this project is to resolve the cybersecurity information-sharing tradeoff by enabling more accurate insights on larger amounts of more relevant collective threat-intelligence data. This project will have the benefit of enabling institutions to build better models by securely collaborating with valuable sensitive data that is not normally shared. This will expand the range of available intelligence, thus leading to new and better threat analyses and predictions.View
Partner: armasuisse Partner contact: Alain Mermoud EPFL laboratory: Distributed Information Systems Laboratory (LSIR) EPFL contact: Prof. Karl Aberer, Chi Thang Duong The objective of the TMM project is to identify, at an early stage, the risks associated with new technologies and develop solutions to ward off such threats. It also aims to assess existing products and applications to pinpoint vulnerabilities. In that process, artificial intelligence and machine learning will play an important part. The main goal of this project is to automatically identify technology offerings of Swiss companies especially in the cyber security domain. This also includes identifying key stakeholders in these companies, possible patents, published scientific papers.
Topics • Machine LearningView
Partner: armasuisse Partner contact: Gérôme Bovet EPFL laboratory: Signal Processing Laboratory (LTS4) EPFL contact: Prof. Pascal Frossard In communication systems, there are many tasks, like modulation recognition, for which Deep Neural Networks (DNNs) have obtained promising performance. However, these models have been shown to be susceptible to adversarial perturbations, namely imperceptible additive noise crafted to induce misclassification. This raises questions about the security but also the general trust in model predictions. In this project, we propose to use adversarial training, which consists of fine-tuning the model with adversarial perturbations, to increase the robustness of automatic modulation recognition (AMC) models. We show that current state-of-the-art models benefit from adversarial training, which mitigates the robustness issues for some families of modulations. We use adversarial perturbations to visualize the features learned, and we found that in robust models the signal symbols are shifted towards the nearest classes in constellation space, like maximum likelihood methods. This confirms that robust models not only are more secure, but also more interpretable, building their decisions on signal statistics that are relevant to modulation recognition.View
Partner: Swissquote Partner contact: Serge Kassibrakis EPFL laboratory: Swiss Finance Institute @ EPFL EPFL contact: Prof. Damir Filipovic, Alexis Marchal We develop a methodology for detecting asset bubbles using a neural network. We rely on the theory of local martingales in continuous-time and use a deep network to estimate the diffusion coefficient of the price process more accurately than the current estimator, obtaining an improved detection of bubbles. We show the outperformance of our algorithm over the existing statistical method in a laboratory created with simulated data. We then apply the network classification to real data and build a zero net exposure trading strategy that exploits the risky arbitrage emanating from the presence of bubbles in the US equity market from 2006 to 2008. The profitability of the strategy provides an estimation of the economical magnitude of bubbles as well as support for the theoretical assumptions relied on.View
Partner: Swissquote Partner contact: Serge Kassibrakis EPFL laboratory: Swiss Finance Institute @ EPFL EPFL contact: Prof. Damir Filipovic, Alexis Marchal We develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the current benchmark on simulated data. We use a long short- term memory (LSTM) neural network that is trained on labelled data generated by a process that experiences both jumps and volatility bursts. As a result, the network learns how to disentangle the two. Then it is applied to out-of-sample simulated data and delivers results that considerably differ from the benchmark: we obtain fewer spurious detection and identify a larger number of true jumps. When applied to real data, our approach for jump screening allows to extract a more precise signal about future volatility.View