Degenerate Feedback Loops in Recommender Systems

Machine learning is used extensively in recommender systems deployed in products. The decisions made by these systems can influence user beliefs and preferences which in turn affect the feedback the learning system receives - thus creating a feedback loop. This phenomenon can give rise to the so-called "echo chambers" or "filter bubbles" that have user and societal implications. In this paper, we provide a novel theoretical analysis that examines both the role of user dynamics and the behavior of recommender systems, disentangling the echo chamber from the filter bubble effect. In addition, we offer practical solutions to slow down system degeneracy. Our study contributes toward understanding and developing solutions to commonly cited issues in the complex temporal scenario, an area that is still largely unexplored

Functional Causal Bayesian Optimization

We propose functional causal Bayesian optimization (fCBO), a method for finding interventions that optimize a target variable in a known causal graph. fCBO extends the CBO family of methods to enable functional interventions, which set a variable to be a deterministic function of other variables in the graph. fCBO models the unknown objectives with Gaussian processes whose inputs are defined in a reproducing kernel Hilbert space, thus allowing to compute distances among vector-valued functions. In turn, this enables to sequentially select functions to explore by maximizing an expected improvement acquisition functional while keeping the typical computational tractability of standard BO settings. We introduce graphical criteria that establish when considering functional interventions allows attaining better target effects, and conditions under which selected interventions are also optimal for conditional target effects. We demonstrate the benefits of the method in a synthetic and in a real-world causal graph

Generative Independent Component Analysis for EEG Classification

We present an application of Independent Component Analysis (ICA) to the discrimination of mental tasks for EEG-based Brain Computer Interface systems. ICA is most commonly used with EEG for artifact identification with little work on the use of ICA for direct discrimination of different types of EEG signals. By viewing ICA as a generative model, we can use Bayes' rule to form a classifier. This enables us also to investigate whether simple spatial information is sufficiently informative to produce state-of-the-art results when compared to more traditional methods based on using temporal features as inputs to off-the-shelf classifiers. Experiments conducted on two subjects suggest that knowing `where' activity is happening alone gives encouraging results

Unified Inference for Variational Bayesian Linear Gaussian State-Space Models

Linear Gaussian State-Space Models are widely used and a Bayesian treatment of parameters is therefore of considerable interest. The approximate Variational Bayesian method applied to these models is an attractive approach, used successfully in applications ranging from acoustics to bioinformatics. The most challenging aspect of implementing the method is in performing inference on the hidden state sequence of the model. We show how to convert the inference problem so that standard and stable Kalman Filtering/Smoothing recursions from the literature may be applied. This is in contrast to previously published approaches based on Belief Propagation. Our framework both simplifies and unifies the inference problem, so that future applications may be easily developed. We demonstrate the elegance of the approach on Bayesian temporal ICA, with an application to finding independent components in noisy EEG signals

Generative Temporal ICA for Classification in Asynchronous BCI Systems

In this paper we investigate the use of a temporal extension of Independent Component Analysis (ICA) for the discrimination of three mental tasks for asynchronous EEG-based Brain Computer Interface systems. ICA is most commonly used with EEG for artifact identification with little work on the use of I CA for direct discrimination of different types of EEG signals. In a recent work we have shown that, by viewing ICA as a generative model, we can use Bayes' rule to form a classifier obtaining state-of-the-art results when compared to more traditional methods based on using temporal features as inputs to off-th e-shelf classifiers. However, in that model no assumption on the temporal nature of the independent components was made. In this work we model the hidden co mponents with an autoregressive process in order to investigate whether temporal information can bring any advantage in terms of discrimination of spontaneo us mental tasks

Generative Temporal ICA for Classification in Asynchronous BCI Systems

In this paper we investigate the use of a temporal extension of Independent Component Analysis (ICA) for the discrimination of three mental tasks for asynchronous EEG-based Brain Computer Interface systems. ICA is most commonly used with EEG for artifact identification with little work on the use of I CA for direct discrimination of different types of EEG signals. In a recent work we have shown that, by viewing ICA as a generative model, we can use Bayes' rule to form a classifier obtaining state-of-the-art results when compared to more traditional methods based on using temporal features as inputs to off-th e-shelf classifiers. However, in that model no assumption on the temporal nature of the independent components was made. In this work we model the hidden co mponents with an autoregressive process in order to investigate whether temporal information can bring any advantage in terms of discrimination of spontaneo us mental tasks

EEG classification using generative independent component analysis

We present an application of Independent Component Analysis (ICA) to the discrimination of mental tasks for EEG-based Brain Computer Interface systems. ICA is most commonly used with EEG for artifact identification with little work on the use of ICA for direct discrimination of different types of EEG signals. By viewing ICA as a generative model, we can use Bayes' rule to form a classifier. We fit spatial filters and source distribution parameters simultaneously and investigate whether these are sufficiently informative to produce good results when compared to more traditional methods based on using temporal features as inputs to off-the-shelf classifiers. Experiments suggest that state-of-the-art results may indeed be found without explicitly using temporal features. We extend the method to using a mixture of ICA models, consistent with the assumption that subjects may have more than one approach to thinking about a specific mental task