970 research outputs found
Label Propagation for Learning with Label Proportions
Learning with Label Proportions (LLP) is the problem of recovering the
underlying true labels given a dataset when the data is presented in the form
of bags. This paradigm is particularly suitable in contexts where providing
individual labels is expensive and label aggregates are more easily obtained.
In the healthcare domain, it is a burden for a patient to keep a detailed diary
of their daily routines, but often they will be amenable to provide higher
level summaries of daily behavior. We present a novel and efficient graph-based
algorithm that encourages local smoothness and exploits the global structure of
the data, while preserving the `mass' of each bag.Comment: Accepted to MLSP 201
Interpretable multiclass classification by MDL-based rule lists
Interpretable classifiers have recently witnessed an increase in attention
from the data mining community because they are inherently easier to understand
and explain than their more complex counterparts. Examples of interpretable
classification models include decision trees, rule sets, and rule lists.
Learning such models often involves optimizing hyperparameters, which typically
requires substantial amounts of data and may result in relatively large models.
In this paper, we consider the problem of learning compact yet accurate
probabilistic rule lists for multiclass classification. Specifically, we
propose a novel formalization based on probabilistic rule lists and the minimum
description length (MDL) principle. This results in virtually parameter-free
model selection that naturally allows to trade-off model complexity with
goodness of fit, by which overfitting and the need for hyperparameter tuning
are effectively avoided. Finally, we introduce the Classy algorithm, which
greedily finds rule lists according to the proposed criterion. We empirically
demonstrate that Classy selects small probabilistic rule lists that outperform
state-of-the-art classifiers when it comes to the combination of predictive
performance and interpretability. We show that Classy is insensitive to its
only parameter, i.e., the candidate set, and that compression on the training
set correlates with classification performance, validating our MDL-based
selection criterion
Crosslingual Document Embedding as Reduced-Rank Ridge Regression
There has recently been much interest in extending vector-based word
representations to multiple languages, such that words can be compared across
languages. In this paper, we shift the focus from words to documents and
introduce a method for embedding documents written in any language into a
single, language-independent vector space. For training, our approach leverages
a multilingual corpus where the same concept is covered in multiple languages
(but not necessarily via exact translations), such as Wikipedia. Our method,
Cr5 (Crosslingual reduced-rank ridge regression), starts by training a
ridge-regression-based classifier that uses language-specific bag-of-word
features in order to predict the concept that a given document is about. We
show that, when constraining the learned weight matrix to be of low rank, it
can be factored to obtain the desired mappings from language-specific
bags-of-words to language-independent embeddings. As opposed to most prior
methods, which use pretrained monolingual word vectors, postprocess them to
make them crosslingual, and finally average word vectors to obtain document
vectors, Cr5 is trained end-to-end and is thus natively crosslingual as well as
document-level. Moreover, since our algorithm uses the singular value
decomposition as its core operation, it is highly scalable. Experiments show
that our method achieves state-of-the-art performance on a crosslingual
document retrieval task. Finally, although not trained for embedding sentences
and words, it also achieves competitive performance on crosslingual sentence
and word retrieval tasks.Comment: In The Twelfth ACM International Conference on Web Search and Data
Mining (WSDM '19
GPstruct: Bayesian structured prediction using Gaussian processes
We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M ^3 N), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inference procedure based on Markov Chain Monte Carlo. The framework can be instantiated for a wide range of structured objects such as linear chains, trees, grids, and other general graphs. As a proof of concept, the model is benchmarked on several natural language processing tasks and a video gesture segmentation task involving a linear chain structure. We show prediction accuracies for GPstruct which are comparable to or exceeding those of CRFs and SVMstruct
The ABACOC Algorithm: a Novel Approach for Nonparametric Classification of Data Streams
Stream mining poses unique challenges to machine learning: predictive models
are required to be scalable, incrementally trainable, must remain bounded in
size (even when the data stream is arbitrarily long), and be nonparametric in
order to achieve high accuracy even in complex and dynamic environments.
Moreover, the learning system must be parameterless ---traditional tuning
methods are problematic in streaming settings--- and avoid requiring prior
knowledge of the number of distinct class labels occurring in the stream. In
this paper, we introduce a new algorithmic approach for nonparametric learning
in data streams. Our approach addresses all above mentioned challenges by
learning a model that covers the input space using simple local classifiers.
The distribution of these classifiers dynamically adapts to the local (unknown)
complexity of the classification problem, thus achieving a good balance between
model complexity and predictive accuracy. We design four variants of our
approach of increasing adaptivity. By means of an extensive empirical
evaluation against standard nonparametric baselines, we show state-of-the-art
results in terms of accuracy versus model size. For the variant that imposes a
strict bound on the model size, we show better performance against all other
methods measured at the same model size value. Our empirical analysis is
complemented by a theoretical performance guarantee which does not rely on any
stochastic assumption on the source generating the stream
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