1,721,031 research outputs found
Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning
No full textHyperparameter optimization (HPO) is important to leverage the full potential of machine learning (ML).
In practice, users are often interested in multi-objective (MO) problems, i.e., optimizing potentially conflicting objectives, like accuracy and energy consumption.
To tackle this, the vast majority of MO-ML algorithms return a Pareto front of non-dominated machine learning models to the user.
Optimizing the hyperparameters of such algorithms is non-trivial as evaluating a hyperparameter configuration entails evaluating the quality of the resulting Pareto front.
In literature, there are known indicators that assess the quality of a Pareto front (e.g., hypervolume, R2) by quantifying different properties (e.g., volume, proximity to a reference point). However, choosing the indicator that leads to the desired Pareto front might be a hard task for a user. In this paper, we propose a human-centered interactive HPO approach tailored towards multi-objective ML leveraging preference learning to extract desiderata from users that guide the optimization.
Instead of relying on the user guessing the most suitable indicator for their needs, our approach automatically learns an appropriate indicator.
Concretely, we leverage pairwise comparisons of distinct Pareto fronts to learn such an appropriate quality indicator.
Then, we optimize the hyperparameters of the underlying MO-ML algorithm towards this learned indicator using a state-of-the-art HPO approach.
In an experimental study targeting the environmental impact of ML, we demonstrate that our approach leads to substantially better Pareto fronts compared to optimizing based on a wrong indicator pre-selected by the user, and performs comparable in the case of an advanced user knowing which indicator to pick
Reinforcing automated machine learning - bridging AutoML and reinforcement learning
Full text linkReinforcement learning is a machine learning paradigm that allows learning through
interaction. It intertwines data collection and model training into a single problem
statement, enabling the solution of complex sequential decision making problems
in domains like robotics, biology or physics. Not included in this list is the domain
of automated machine learning, which aims to automatically configure machine
learning algorithms for optimal performance on a given task - even though we
have long known that sequential decision making is important in many facets of
automated machine learning. This lack of adoption of reinforcement learning is
potentially due to the fact that the entanglement of data collection and learning in
reinforcement learning makes for a challenging machine learning setting; since the
distribution of data seen during training shifts substantially as the agent improves,
the optimal solution strategy - including the choice of algorithm, algorithm compo-
nents, hyperparameters and even task variation - can shift as well. Thus applying
reinforcement learning directly to an automated machine learning task might not
be possible without considerable effort and expertise. This thesis bridges the gap
between the fields by motivating the use of reinforcement learning in automated
machine learning for dynamic algorithm configuration, a novel paradigm for config-
uring algorithms during their runtime. In turn, applying reinforcement learning in
automated machine learning leads us to a closer examination of how to configure
reinforcement learning itself to be efficient, reliable and generalizable when applied
to new domains. We accomplish this in three parts: i. extending the algorithm con-
figuration paradigm to allow the dynamic configuration and analysis of algorithms;
ii. a principled investigation of the landscape of design decisions in reinforcement
learning and; iii. laying the groundwork for generalization of reinforcement learning
configuration approaches through contextual reinforcement learning. An important
focus throughout is providing insights into the inner workings of reinforcement
learning with respect to its design decisions, as of yet underexplored territory. Thus
we are able to provide actionable recommendations for reinforcement learning
practitioners as well as a broad base for future work on automated reinforcement
learning. Overall, this thesis provides an in-depth look into the intersection of
automated machine learning and reinforcement learning. We believe it will serve as
a foundation for a closer connection between the fields by demonstrating the great
potential of reinforcement learning for automated machine learning and vice versa
Interpreting Text Classification with Human-Understandable Counterfactual Instances
Full text linkAs the omnipresent machine learning models play increasingly important roles in our society, powerful interpretation tools to uncover their black boxes are needed.
On the other hand, proven by psychological study, we humans are more likely to learn new concepts presented with contrastive instances.
Therefore, interpreting ML models using the contrast between the original data instance and its counterfactuals has become a popular problem.
Traditional counterfactual interpretation approaches tend to generate counterfactuals faithful to the ML model.
However, they have little or no constraint on the meaningfulness of generated counterfactuals.
This thesis proposes an approach generating a meaningful counterfactual interpretation of text classification models constrained with cosine similarity and POS (part-of-speech) properties of tokens.
In this thesis, I use the text CNN model based on Kims Cnn\cite{KimsCnn} with fine-tuned Word2Vec embedding layer as the model to interpret.
Then for the counterfactual generation, I leverage token-level HotFlip\cite{hotflip} and replace tokens under several constraints.
Lastly, I will present that my approach results in more meaningful counterfactual interpretations compared with the vanilla HotFlip approaches using several examples
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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