Wednesday, February 27, 3.15 pm, 2019

Reliable Semi-Supervised Learning when Labels are Missing at Random
Dave Zachariah, Department of Information Technology, Division of Systems and Control, Uppsala University
Abstract: Semi-supervised learning methods are motivated by the availability of large datasets with unlabeled features in addition to labeled data. Unlabeled data is, however, not guaranteed to improve classification performance and has in fact been reported to impair the performance in certain cases. In this talk we discuss some fundamental limitations to semi-supervised learning and restrictive assumptions which result in unreliable classifiers. We also propose a learning approach that relaxes such assumptions and is capable of providing classifiers that reliably quantify the label uncertainty.
Location: Ada Lovelace (Visionen)
Organizer: Fredrik Lindsten

Wednesday, March 27, 3.15 pm, 2019

Conformal prediction
Henrik Boström, Department of Software and Computer Systems, KTH Royal Institute of Technology
Abstract: Conformal prediction is a framework for quantifying the uncertainty of predictions provided by standard machine learning algorithms. When employing the framework, the probability of making incorrect predictions is bounded by a user-provided confidence threshold. In this talk, we will briefly introduce the framework and illustrate its use in conjunction with both interpretable models, such as decision trees, and highly predictive models, such as random forests.
Location: Ada Lovelace (Visionen)
Organizer: Oleg Sysoev

Wednesday, April 24, 3.15 pm, 2019

Evaluating the Performance of Soccer Players
Jesse Davis, Department of Computer Science, KU Leuven
Abstract: Over the last 25 years, there has been tremendous interest in applying computational techniques to analyze sports. This area has exploded in the past decade as modern data collection techniques have enabled collecting large of amounts of data about games and athletes. From a computer science perspective, sports data are very rich and complicated, which poses a number of interesting analysis challenges such as the lack of ground truth labels, the need to construct relevant features, and changing contexts. I will begin the talk by highlighting some of the most important general challenges. Then I will focus on our efforts to assess the performance of soccer players during a match. First, I will describe our approach for assigning values to all on-ball actions during a match. This goes beyond standard approaches such as expected goals and assists that only value on a small subset of actions. Second, I will describe our recent research on trying to understand how mental pressure affects performance. I will explain our mental pressure model, which assigns a pressure level to each minute of match by considering both the match context as well as the current game state. This enables comparing soccer players' performances across different levels of mental pressure. Finally, I will show our approach’s ability to provide actionable insights for soccer clubs in four relevant use cases: player acquisition, training, tactical decisions, and lineups and substitutions.
Location: Ada Lovelace (Visionen)
Organizer: Patrick Lambrix

Wednesday, May 8, 3.15 pm, 2019

Beyond the mean-field family: Variational inference with implicit distributions
Francisco Ruiz, Department of Computer Science, Columbia University and Dept of Engineering, University of Cambridge.
Abstract: Approximating the posterior of a probabilistic model is the central challenge of Bayesian inference. One of the main approximate inference tools is variational inference (VI), which recasts inference as an optimization problem. Classical VI relies on the mean-field approximation, which constrains the variational family to be a fully factorized distribution. While useful, the mean-field assumption may lead to variational families that are not expressive enough to approximate the posterior. In this talk, I present two different ways to expand the expressiveness of the variational family using implicit distributions. First, I describe unbiased implicit VI (UIVI), a method that obtains an implicit variational distribution in a hierarchical manner using simple but flexible reparameterizable distributions. This construction enables unbiased stochastic gradients of the variational objective, making optimization tractable. Second, I describe a method to improve the variational distribution using Markov chain Monte Carlo (MCMC), leveraging the advantages of both inference techniques. To make inference tractable, we introduce the variational contrastive divergence (VCD), a divergence that replaces the standard variational objective based on the Kullback-Leibler divergence. Both UIVI and the VCD are demonstrated empirically through a set of experiments on several probabilistic models.
Location: Ada Lovelace (Visionen)
Organizer: Fredrik Lindsten

Wednesday, May 15, 3.15 pm, 2019

Topological and Geometric Methods for Reasoning about Data
Florian T. Pokorny, Robotics, Perception and Learning Lab, KTH Royal Institute of Technology.
Abstract: In this talk, I will discuss our recent work on topological and geometric methods for representing and reasoning about data from a variety of application domains ranging from trajectory clustering to classification of image data. I will focus firstly on our most recent approach to extracting information about high-dimensional Voronoi Cell geometry using Monte Carlo sampling which avoids explicit computation of Voronoi representations. I will discuss how the estimation of weighted integrals over Voronoi boundaries can in particular lead to a simple yet effective geometric classification approach. Secondly, I will also discuss our work towards reasoning about motion and robotic configuration spaces based on simplicial complex representations and persistent homology.
Location: Ada Lovelace (Visionen)
Organizer: Mattias Villani