Jan. 9

Unsupervised Machine Learning for Matrix Decomposition

Abstract: Unsupervised learning is a classical approach in pattern recognition and data analysis. Its importance is growing today, due to the increasing data volumes and the difficulty of obtaining statistically sufficient amounts of labelled training data. Typical analysis techniques using unsupervised learning are principal component analysis, independent component analysis, and cluster analysis. They can all be presented as decompositions of the data matrix containing the unlabeled  samples. Starting from the classical results, the author reviews some advances in the field up to the present day.

Speaker: Erkki Oja

Affiliation: Professor Emeritus, Aalto University

Place of Seminar: Aalto University


Jan. 16

Probabilistic Programming: Bayesian Modeling Made Easy

Abstract: Probabilistic models are principled tools for understanding data, but difficulty of inference limits the complexity of models we can actually use. Often we need to develop specific inference algorithms for new models (which might take months), and need to restrict ourselves to tractable model families that might not match our beliefs about the data. Probabilistic programming promises to fix this, by separating the model description from the inference: With probabilistic programming languages we can specify complex models using a high-level programming language, letting a black-box inference engine take care of the tricky details. This talk covers the basic idea of probabilistic programming and discusses how well its promises hold now and in the future.

Speaker: Arto Klami

Affiliation: Academy Research Fellow, University of Helsinki

Place of Seminar: University of Helsinki


Jan. 23

Metabolite Identification Through Machine Learning

Abstract: Identification of small molecules from biological samples remains a major bottleneck in understanding the inner working of biological cells and their environment. Machine learning on data from large public databases of tandem mass spectrometric data has transformed this field in recent years, witnessing an increase of identification rates by 150%. In this presentation, I will outline the key machine learning methods behind this development: kernel-based learning of molecular fingerprints, multiple kernel learning, structured prediction as well as some recent advances.

Speaker: Juho Rousu

Affiliation: Associate Professor, Aalto University

Place of Seminar: Aalto University


Jan. 30

Likelihood-free Inference and Predictions for Computational Epidemiology

Abstract: Simulator-based models often allow inference and predictions under more realistic assumptions than those employed in standard statistical models. For example, the observation model for an underlying stochastic process can be more freely chosen to reflect the characteristics of the data gathering procedure. A major obstacle for such models is the intractability of the likelihood, which has to a large extent hampered their practical applicability. I will discuss recent advances in likelihood-free inference that greatly accelerate the model fitting process by exploiting a combination of machine learning techniques. Applications to several novel models in infectious disease epidemiology are used to illustrate the potential offered by this approach.

Speaker: Jukka Corander

Affiliation: Professor, University of Helsinki and University of Oslo

Place of Seminar: University of Helsinki


Feb. 6

Towards Perfect Density Estimation

Abstract: We start by addressing a most simple problem, estimation of a one dimensional density function, and argue that despite of the apparent simplicity of the problem, it is surprisingly difficult to solve it in a holistic manner that is both computationally feasible and theoretically justifiable without strong distributional or other assumptions. We demonstrate how the information-theoretic MDL framework can be used for reaching this goal (almost) perfectly, and show how this simple setup gives interesting perspectives on the fundamental concepts in probabilistic modelling and statistical inference. We also discuss ideas for extending the framework to more complex models with additional practical applications.

Speaker: Petri Myllymäki

Affiliation: Professor, University of Helsinki

Place of Seminar: Aalto University


Feb. 13

Variable Selection From Summary Statistics

Abstract: With increasing capabilities to measure a massive number of variables, efficient variable selection methods are needed to improve our understanding of the underlying data generating processes. This is evident, for example, in human genomics, where genomic regions showing association to a disease may contain thousands of highly correlated variants, while we expect that only a small number of them are truly involved in the disease process. I outline recent ideas that have made variable selection practical in human genomics and demonstrate them through our experiences with the FINEMAP algorithm (Benner et al. 2016, Bioinformatics).

(1) Compressing data to light-weight summaries to avoid logistics and privacy concerns related to complete data sharing and to minimize the computational overhead.

(2) Efficient implementation of sparsity assumptions.

(3) Efficient stochastic search algorithms.

(4) Use of public reference databases to complement the available summary statistics.

Speaker: Matti Pirinen

Affiliation: Academy Research Fellow, Institute for Molecular Medicine Finland, University of Helsinki

Place of Seminar: University of Helsinki


Feb. 20

Compressed Sensing for Semi-Supervised Learning From Big Data Over Networks

Abstract: In this talk I will present some of our most recent work on the application of compressed sensing to semi-supervised learning from massive network-structured datasets, i.e., big data over networks. We expect the user of compressed sensing ideas to be game-changing for machine learning from big data in a similar manner as it was for digital signal processing. In particular, I will present a sparse label propagation algorithm which efficiently learn from large amounts of network-structured unlabeled data by leveraging the information provided by a few initially labelled training data points. This algorithm is inspired by compressed sensing recovery methods and allows for a simple sufficient condition on the network structure which guarantees accurate learning.

Speaker: Alexander Jung

Affiliation: Assistant Professor, Aalto University

Place of Seminar: Aalto University


Feb. 27

Inverse Modeling in Behavioral Sciences and HCI

Abstract: Can one make deep inferences about a person based only on observations of how she acts? I discuss methodology for inverse modeling in behavioral sciences, where the goal is to estimate a cognitive model from limited behavioral data. Given substantial diversity in people's intentions, strategies and abilities, this is a difficult problem and previously unaddressed. I discuss advances achieved with an approach that combines (1) computational rationality, to predict how a person adapts to a task when her capabilities are known, and (2) Approximate Bayesian Computation (ABC) to estimate those capabilities. The benefit is that model parameters are conditioned on both prior knowledge and observations, which improves model validity and helps identify causes for observations. Inverse modeling methods can advance theory-formation by bringing complex behavior within reach of modeling. This talk is based on on-going collaborations with Antti Kangasraasio, Samuel Kaski, Jukka Corander, Andrew Howes, Kumaripaba Athukorala, Jussi Jokinen, Sayan Sarcar, and Xiangshi Ren.

Speaker: Antti Oulasvirta

Affiliation: Associate Professor, Aalto University

Place of Seminar: University of Helsinki


March. 6

Differentially Private Bayesian Learning

Abstract: Many applications of machine learning for example in health care would benefit from methods that can guarantee data subject privacy. Differential privacy has recently emerged as a leading framework for private data analysis. Differenctial privacy guarantees privacy by requiring that the results of an algorithm should not change much even if one data point is changed, thus providing plausible deniability for the data subjects.

In this talk I will present methods for efficient differentially private Bayesian learning. In addition to asymptotic efficiency, we will focus on how to make the methods efficient for moderately-sized data sets. The methods are based on perturbation of sufficient statistics for exponential family models and perturbation of gradients for variational inference. Unlike previous state-of-the-art, our methods can predict drug sensitivity of cancer cell lines using differentially private linear regression with better accuracy than using a very small non-private data set.

Speaker: Antti Honkela

Affiliation: Assistant Professor, University of Helsinki

Place of Seminar: Aalto University


March. 13

Small Data AUC Estimation of Machine Learning Methods: Pitfalls and Remedies

Abstract: Asking whether two populations can be distinguished from each other is one of the most fundamental questions in data analysis and area under ROC curve (AUC) is one of the simplest and most practical tools for answering it. Also known as the Wilcoxon-Mann-Whitney U statistic, it can be associated with a p-value indicating how likely one would obtain as good AUC value if the two populations would not be stochastically different. Estimating AUC of a predictive model and its statistical significance has a huge practical importance in fields like medicine, where one often has access to only small amounts of labeled data but large number of features. Leave-pair-out cross-validation (LPOCV) is an almost unbiased AUC estimator of machine learning methods that has also been empirically shown to be the most reliable of the cross-validation (CV) based estimators. We further study the properties of LPOCV and show some serious pitfalls one can encounter when estimating AUC with CV and how to avoid them. In particular, we show how one can produce very promising results with high AUC values even if there is no signal in the data. Finally, we show how to counter these risks with new Wilcoxon–Mann–Whitney U type of permutation tests adjusted for LPOCV, thus upgrading one of the classical statistical tools for CV estimates.

Speaker: Tapio Pahikkala

Affiliation: Assistant Professor, University of Turku

Place of Seminar: University of Helsinki


March. 20

Future AI: Autonomous machine learning and beyond

Abstract: Many researchers have identified autonomous machine learning (unsupervised, semi-supervised and reinforcement learning) as an important cornerstone of advanced artificial intelligence. The Curious AI Company is developing such autonomous learning systems. We already have state-of-the-art results in several semi-supervised classification tasks but we are also working on bringing autonomy to learning segmentation and hierarchical control, both of them tasks that take a lot of human work when developing for instance self-driving cars. However, we believe there's an even more important blocker on the way to advanced AI: the fundamental inability of currently used parallel distributed neural coding to properly represent objects and their interactions. We are working on deep learning networks whose neuro-symbolic representations will hopefully allow neural networks to understand the world not only in terms of a collection of features but in terms of objects and their interactions, too. This is necessary for many tasks such as communication, reasoning and complex decision making.

Speaker: Harri Valpola

Affiliation: CEO of the Curious AI Company

Place of Seminar: Aalto University

March. 27

Learning to Rank: Applications to Bioinformatics

Abstract: Learning To Rank (LTR) has been developed in information retrieval for ranking documents regarding the relevance to a given query. Typically LTR builds a ranking model from given relevant (or irrelevant) query-document pairs. Generally, in some respect, LTR can be thought as an attempt to solve a multilabel classification problem, where queries are labels. A lot of settings in bioinformatics can be turned into multilabel classification problems having relatively similar properties. One typical example is biomedical document annotation. Currently PubMed, a database of 26 million biomedical citations, has around 30,000 keywords, called MeSH (Medical Subject Headings) terms, i.e. labels in multilabel classification, where the number of articles per MeSH term is extremely diverse, ranging from only 20 to more than eight million. This large, biased dataset already goes beyond the general sense of settings expected by regular multilabel classifiers. In this talk, I will start with introduction and a brief review of LTR. I then raise three bioinformatics multilabel classification problems that share real data-derived, practical properties, which hamper the application of regular multilabel classifiers. Finally I will show that LTR nicely addresses such large-scale, challenging bioinformatics multilabel classification problems.

A large portion of this talk appeared in ISMB in 2015 and 2016.

Speaker: Hiroshi Mamitsuka

Affiliation: Professor, Kyoto University

Place of Seminar: University of Helsinki



Last updated on 27 Mar 2017 by Homayun Afrabandpey - Page created on 12 Dec 2016 by Homayun Afrabandpey