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Register a new userTowards AutoML in the presence of Drift: first results
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Jorge Gustavo Madrid Perez
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Research progress in AutoML has lead to state of the art solutions that can cope quite wellwith supervised learning task, e.g., classification with AutoSklearn. However, so far thesesystems do not take into account the changing nature of evolving data over time (i.e., theystill assume i.i.d. data); even when this sort of domains are increasingly available in realapplications (e.g., spam filtering, user preferences, etc.). We describe a first attempt to de-velop an AutoML solution for scenarios in which data distribution changes relatively slowlyover time and in which the problem is approached in a lifelong learning setting. We extendAuto-Sklearn with sound and intuitive mechanisms that allow it to cope with this sort ofproblems. The extended Auto-Sklearn is combined with concept drift detection techniquesthat allow it to automatically determine when the initial models have to be adapted. Wereport experimental results in benchmark data from AutoML competitions that adhere tothis scenario. Results demonstrate the effectiveness of the proposed methodology.
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Building models from data is an integral part of the majority of data science workflows. While data scientists are often forced to spend the majority of the time available for a given project on data cleaning and exploratory analysis, the time available to practitioners to build actual models from data is often rather short due to time constraints for a given project. AutoML systems are currently rising in popularity, as they can build powerful models without human oversight. In this position paper, we aim to discuss the impact of the rising popularity of such systems and how a user-centered interface for such systems could look like. More importantly, we also want to point out features that are currently missing in those systems and start to explore better usability of such systems from a data-scientists perspective.
We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participants to develop computer programs capable of solving supervised learning problems where the i.i.d. assumption did not hold. Large data sets were arranged in a lifelong learning and evaluation scenario and CodaLab was used as the challenge platform. The challenge attracted more than 300 participants in its two month duration. This chapter describes the design of the challenge and summarizes its main results.
Latent variables may lead to spurious relationships that can be misinterpreted as causal relationships. In Bayesian Networks (BNs), this challenge is known as learning under causal insufficiency. Structure learning algorithms that assume causal insufficiency tend to reconstruct the ancestral graph of a BN, where bi-directed edges represent confounding and directed edges represent direct or ancestral relationships. This paper describes a hybrid structure learning algorithm, called CCHM, which combines the constraint-based part of cFCI with hill-climbing score-based learning. The score-based process incorporates Pearl s do-calculus to measure causal effects and orientate edges that would otherwise remain undirected, under the assumption the BN is a linear Structure Equation Model where data follow a multivariate Gaussian distribution. Experiments based on both randomised and well-known networks show that CCHM improves the state-of-the-art in terms of reconstructing the true ancestral graph.
We introduce and study knowledge drift (KD), a complex form of drift that occurs in hierarchical classification. Under KD the vocabulary of concepts, their individual distributions, and the is-a relations between them can all change over time. The main challenge is that, since the ground-truth concept hierarchy is unobserved, it is hard to tell apart different forms of KD. For instance, introducing a new is-a relation between two concepts might be confused with individual changes to those concepts, but it is far from equivalent. Failure to identify the right kind of KD compromises the concept hierarchy used by the classifier, leading to systematic prediction errors. Our key observation is that in many human-in-the-loop applications (like smart personal assistants) the user knows whether and what kind of drift occurred recently. Motivated by this, we introduce TRCKD, a novel approach that combines automated drift detection and adaptation with an interactive stage in which the user is asked to disambiguate between different kinds of KD. In addition, TRCKD implements a simple but effective knowledge-aware adaptation strategy. Our simulations show that often a handful of queries to the user are enough to substantially improve prediction performance on both synthetic and realistic data.
We address the problem of uncertainty calibration. While standard deep neural networks typically yield uncalibrated predictions, calibrated confidence scores that are representative of the true likelihood of a prediction can be achieved using post-hoc calibration methods. However, to date the focus of these approaches has been on in-domain calibration. Our contribution is two-fold. First, we show that existing post-hoc calibration methods yield highly over-confident predictions under domain shift. Second, we introduce a simple strategy where perturbations are applied to samples in the validation set before performing the post-hoc calibration step. In extensive experiments, we demonstrate that this perturbation step results in substantially better calibration under domain shift on a wide range of architectures and modelling tasks.
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