# Scientific Track

## Ridge Regression, Hubness, and Zero-Shot Learning

This paper discusses the effect of hubness in zero-shot learning, when ridge regression is used to find a mapping between the example space to the label space. Contrary to the existing approach, which attempts to find a mapping from the example space to the label space, we show that mapping labels into the example space is desirable to suppress the emergence of hubs in the subsequent nearest neighbor search step. Assuming a simple data model, we prove that the proposed approach indeed reduces hubness.

## Regression with Linear Factored Functions

Many applications that use empirically estimated functions face a curse of dimensionality, because integrals over most function classes must be approximated by sampling. This paper introduces a novel regression-algorithm that learns linear factored functions (LFF). This class of functions has structural properties that allow to analytically solve certain integrals and to calculate point-wise products. Applications like belief propagation and reinforcement learning can exploit these properties to break the curse and speed up computation.

## Predicting Unseen Labels using Label Hierarchies in Large-Scale Multi-label Learning

An important problem in multi-label classification is to capture label patterns or underlying structures that have an impact on such patterns.One way of learning underlying structures over labels is to project both instances and labels into the same space where an instance and its relevant labels tend to have similar representations.In this paper, we present a novel method to learn a joint space of instances and labels by leveraging a hierarchy of labels.We also present an efficient method for pretraining vector representations of labels, namely label embeddings, from large amounts of label co

## Parameter Learning of Bayesian Network Classifiers Under Computational Constraints

We consider online learning of Bayesian network classifiers (BNCs) with reduced-precision parameters, i.e. the conditional-probability tables parameterizing the BNCs are represented by low bit-width fixed-point numbers. In contrast to previous work, we analyze the learning of these parameters using reduced-precision arithmetic only which is important for computationally constrained platforms, e.g. embedded- and ambient-systems, as well as power-aware systems.

## Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration

There are several reasons to evaluate a multi-class classifier on other measures than just error rate. Perhaps most importantly, there can be uncertainty about the exact context of classifier deployment, requiring the classifier to perform well with respect to a variety of contexts. This is commonly achieved by creating a scoring classifier which outputs posterior class probability estimates. Proper scoring rules are loss evaluation measures of scoring classifiers which are minimised at the true posterior probabilities.

## Maximum Entropy Linear Manifold for Learning Discriminative Low-dimensional Representation

Representation learning is currently a very hot topic in modern machine learning, mostly due to the great success of the deep learning methods.In particular low-dimensional representation which discriminates classes can not only enhance the classification procedure, but also make it faster, while contrary to the high-dimensional embeddings can be efficiently used for visual based exploratory data analysis.In this paper we propose Maximum Entropy Linear Manifold (MELM), a multidimensional generalization of Multithreshold Entropy Linear Classifier model which is able to find a low-dimensional li

## Fast Label Embeddings via Randomized Linear Algebra

Many modern multiclass and multilabel problems are characterized by increasingly large output spaces. For these problems, label embeddings have been shown to be a useful primitive that can improve computational and statistical efficiency. In this work we utilize a correspondence between rank constrained estimation and low dimensional label embeddings that uncovers a fast label embedding algorithm which works in both the multiclass and multilabel settings. The result is a randomized algorithm whose running time is exponentially faster than naive algorithms.

## Discriminative Interpolation for Classification of Functional Data

The modus operandi for machine learning is to represent data as feature vectors and then proceed with training algorithms that seek to optimally partition the feature space S ⊂ R^n into labeled regions. This holds true even when the original data are functional in nature, i.e. curves or surfaces that are inherently varying over a continuum such as time or space. Functional data are often reduced to summary statistics, locally-sensitive characteristics, and global signatures with the objective of building comprehensive feature vectors that uniquely characterize each function.

## Data split strategies for evolving predictive models

A conventional textbook prescription for building good predictive models is to split the data into three parts: training set (for model fitting), validation set (for model selection), and test set (for final model assessment). Predictive models can potentially evolve over time as developers improve their performance either by acquiring new data or improving the existing model. The main contribution of this paper is to discuss problems encountered and propose workflows to manage the allocation of newly acquired data into different sets in such dynamic model building and updating scenarios.

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