Machine Learning Coffee Seminars

Machine Learning Coffee Seminars 2018-05-14T13:07:34+00:00

About Us

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Seminars will be held weekly on Mondays at 9 am – 10 am. The location alternates between Aalto University and the University of Helsinki. At Aalto University, talks will be held in Konemiehentie 2, seminar room T6 and at the University of Helsinki in Kumpula, seminar room Exactum D122 (Gustaf Hällströmin katu 2b), unless otherwise noted. Talks will begin at 9:15 am and coffee will be served from 9:00 am.

Subscribe to our mailing list where seminar topics are announced beforehand.

Upcoming Talks (2018)

Finnish Center for Artificial Intelligence (FCAI) is proud to present a series of longer machine learning coffee sessions on themes of the research programmes of FCAI. These minisymposiums will last for almost 2 hours and consist of some longer talks and a set of 5-min flash talks. The purpose is both for the researchers already working on the theme to get to know what the others are working on, and for those interested in the theme to get to know who to talk to. Suggestions of flash talk topics are welcome – contact the organizers of the specific symposium.

May 2018

Minisymposium on Privacy-Preserving and Secure AI

Minisymposium on Interactive AI

Date: May 28, 2018

Organizers: N. Asokan and Antti Honkela

Machine Learning in the Presence of Adversaries

Abstract: Machine Learning in the presence of adversaries AI, and machine learning in particular, is being applied in a wide range of problem domains. Security/privacy problems are no exception. Typically, effectiveness of ML applications is evaluated in terms of considerations like accuracy and cost. An equally important consideration is how adversaries can interfere with ML applications. Considering the adversary at different stages of a ML pipeline can help us understand different security and privacy problems of ML applications.
Speaker: N. Asokan

Android Malware Classification: How to Deal With Extremely Sparse Features

Abstract: In this talk I provide some insights about the specifics of working with high-dimensional features from Android files. Since each package to be classified can be described based on strings extracted from its files, the overall feature size grows drastically with the size of training set. To deal with sparse feature sets, we experimented with various approaches including log-odds ratio, random projections, feature clustering and non-random matrix factorization. In this talk, I describe the framework for Android Malware Classification with a focus on the proposed dimensionality reduction approaches.
Speaker: Luiza Sayfullina

Reducing False Positives in Intrusion Detection

Abstract: The F-Secure Rapid Detection and Response Service is an intrusion detection service provided by F-secure to companies. In this solution, we analyze the events generated by the clients, and raise an alarm when suspicious behavior occurs. These alarms are further analyzed by experts, and if needed, a client is contacted. Some of these alarms are false positives, resulting in unnecessary analysis work by the experts, and in this talk we describe the challenges, and the approach to reduce such false positives.
Speaker: Nikolaj Tatti

Stealing DNN Models: Attacks and Defenses

Abstract: Today machine learning models constitute business advantages to several companies. Companies want to leverage ML models to provide prediction services to clients. However, direct (i.e. white-box) access to models has been shown to be vulnerable to adversarial machine learning, where a malicious client may craft adversarial examples — samples that that by design are misclassified by the model. This has serious consequences for several business sectors, including autonomous driving and malware detection. Model confidentiality is paramount in these scenarios.

Consequently, model owners do not want to reveal the model to the client, but may provide black-box predictions via well-defined APIs to them. Nevertheless, prediction APIs still leak information (predictions) that make it possible to mount model extraction attacks by repeatedly querying the model via the prediction API. Model extraction attacks threaten the confidentiality of the model, as well as the integrity, since the stolen model can be used to create transferable adversarial examples.

We analyze model extraction attacks on DNNs via structured tests and present a new way of generating synthetic queries, which outperforms state-of-the-art. We then propose a generic approach to effectively detect model extraction attacks: PRADA. It analyzes the distribution of successive queries to the model evolves and detects abrupt deviations. We show that PRADA can detect all known model extraction attacks with a 100% success rate and no false positives.

Speaker: Mika Juuti

Differential Privacy and Machine Learning

Abstract: Differential privacy provides a flexible framework for privacy-aware computation. It provides strong privacy guarantees through requiring that the results of the computation should not depend too strongly on any single individual’s data. In my talk I will introduce differentially private machine learning, with an emphasis on Bayesian methods. I will also present an application of differentially private machine learning to personalised cancer drug sensitivity prediction using gene expression data.
Speaker: Antti Honkela

Differentially private Bayesian learning on distributed data

Abstract: Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become established as a standard for protecting learning results. The standard DP algorithms require a single trusted party to have access to the entire data, which is a clear weakness, or add prohibitive amounts of noise to learning. I discuss a novel method for DP Bayesian learning in a distributed setting, where each party only holds a single sample or a few samples of the data. The method relies on secure multi-party computation combined with the well-established Gaussian mechanism for DP. The talk is based on our recent paper.
Speaker: Mikko Heikkilä

Privacy Preservation with Federated Learning in Personalized Recommendation Systems

Abstract: Recent events have brought public attention to how companies capture, store and exploit user’s personal data in their various services.

The EU’s GDPR enforcement starts in May 2018 regulating how companies access, store and process user data. Companies can now suffer reputational damage and large financial penalties if they fail to respect the rights of users and how they manage their data. At Huawei we are looking at different approaches to enhancing Huawei user privacy while at the same time providing an optimal user experience. In this talk we discuss one approach to generating personalized recommendations for use in different Huawei mobile services based on Federated Learning. The target of the research has been to generate high quality personalized recommendations on mobile devices without moving the user data from the user’s own device.
Speaker: Adrian Flanagan

Place of Seminar: Aalto University


Samuel Kaski  Professor of Computer Science, Aalto University
Teemu Roos Associate Professor of Computer Science, University of Helsinki
Homayun Afrabandpey PhD Student, Aalto University