| 1. |
- Ahlberg, Ernst, et al.
(författare)
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Using conformal prediction to prioritize compound synthesis in drug discovery
- 2017
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Ingår i: Proceedings of Machine Learning Research. - Stockholm : Machine Learning Research. ; , s. 174-184
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Konferensbidrag (refereegranskat)abstract
- The choice of how much money and resources to spend to understand certain problems is of high interest in many areas. This work illustrates how computational models can be more tightly coupled with experiments to generate decision data at lower cost without reducing the quality of the decision. Several different strategies are explored to illustrate the trade off between lowering costs and quality in decisions.AUC is used as a performance metric and the number of objects that can be learnt from is constrained. Some of the strategies described reach AUC values over 0.9 and outperforms strategies that are more random. The strategies that use conformal predictor p-values show varying results, although some are top performing.The application studied is taken from the drug discovery process. In the early stages of this process compounds, that potentially could become marketed drugs, are being routinely tested in experimental assays to understand the distribution and interactions in humans.
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| 2. |
- Alkhatib, Amr, et al.
(författare)
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Approximating Score-based Explanation Techniques Using Conformal Regression
- 2023
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Ingår i: Proceedings of Machine Learning Research. - : ML Research Press. ; , s. 450-469, s. 450-469
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Konferensbidrag (refereegranskat)abstract
- Score-based explainable machine-learning techniques are often used to understand the logic behind black-box models. However, such explanation techniques are often computationally expensive, which limits their application in time-critical contexts. Therefore, we propose and investigate the use of computationally less costly regression models for approximating the output of score-based explanation techniques, such as SHAP. Moreover, validity guarantees for the approximated values are provided by the employed inductive conformal prediction framework. We propose several non-conformity measures designed to take the difficulty of approximating the explanations into account while keeping the computational cost low. We present results from a large-scale empirical investigation, in which the approximate explanations generated by our proposed models are evaluated with respect to efficiency (interval size). The results indicate that the proposed method can significantly improve execution time compared to the fast version of SHAP, TreeSHAP. The results also suggest that the proposed method can produce tight intervals, while providing validity guarantees. Moreover, the proposed approach allows for comparing explanations of different approximation methods and selecting a method based on how informative (tight) are the predicted intervals.
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| 3. |
- Alkhatib, Amr, et al.
(författare)
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Assessing Explanation Quality by Venn Prediction
- 2022
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Ingår i: Proceedings of the 11th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2022. - : ML Research Press. ; , s. 42-54, s. 42-54
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Konferensbidrag (refereegranskat)abstract
- Rules output by explainable machine learning techniques naturally come with a degree of uncertainty, as the complex functionality of the underlying black-box model often can be difficult to approximate by a single, interpretable rule. However, the uncertainty of these approximations is not properly quantified by current explanatory techniques. The use of Venn prediction is here proposed and investigated as a means to quantify the uncertainty of the explanations and thereby also allow for competing explanation techniques to be evaluated with respect to their relative uncertainty. A number of metrics of rule explanation quality based on uncertainty are proposed and discussed, including metrics that capture the tendency of the explanations to predict the correct outcome of a black-box model on new instances, how informative (tight) the produced intervals are, and how certain a rule is when predicting one class. An empirical investigation is presented, in which explanations produced by the state-of-the-art technique Anchors are compared to explanatory rules obtained from association rule mining. The results suggest that the association rule mining approach may provide explanations with less uncertainty towards the correct label, as predicted by the black-box model, compared to Anchors. The results also show that the explanatory rules obtained through association rule mining result in tighter intervals and are closer to either one or zero compared to Anchors, i.e., they are more certain towards a specific class label.
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| 4. |
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| 5. |
- Arvidsson, Simon, et al.
(författare)
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Texture Mapping of Flags onto Polandball Characters using Convolutional Neural Nets
- 2021
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Ingår i: 2021 International Joint Conference on Neural Networks (IJCNN). - 9781665439008 ; , s. 1-7
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Konferensbidrag (refereegranskat)abstract
- Polandball comics are hand-drawn satirical content that portray personified countries in a unique style. Although certain parts of these comics, such as ball outlines, are easy to draw, some country flags are complex and require time, effort, and skill to depict correctly. Convolutional Neural Networks have shown success in image synthesis tasks but lack the ability to rescale and rotate images for texture mapping. The domain of Virtual Try-On Networks has made great progress in networks that can handle spatially invariant transforms. We show that similar methods can be used in another domain dependent on texture mapping, namely generating valid, rule-abiding Poland-ball characters given an outline and a country flag. To evaluate our method we make use of the Fréchet Inception Distance where we achieved a score of 34.9. Multiple configurations of the model were evaluated to show that all modules used in the model contribute to the achieved performance. The main contributions in this paper are: a model that can be used by Polandball artists to aid in comic creation and a dataset with over 40,000 labeled Polandball characters for computer vision tasks.
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| 6. |
- Batingan Paredes, Karla Marie, et al.
(författare)
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Organizational climate for innovation and creativity—a study in Swedish retail organizations
- 2018
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Konferensbidrag (refereegranskat)abstract
- Innovation in retailing is under-investigated in academia and yet highly relevant in practice given the current changes in the retail landscape. Although retailing is often characterised by a dynamic and highly competitive environment, retail organizations are not often considered as “innovative,” at least when compared with manufacturing industries or when using existing innovation frameworks in academic literature. There is a need to consider other ways of looking into retailing’s innovativeness—such as looking into the organizational climate— which may help explain how innovation is enabled in service organizations such as retailers. We applied a mixed-methods approach using an organizational climate survey based on Ekvall’s (1996) work, and semi-structured, one-on-one interviews regarding the creative climate and other aspects of innovation management in the companies. The study shows that despite retail organizations still struggling to incorporate innovation on a strategic level and move beyond incremental developments in their operations, retail organizations score positively on being innovative regarding certain dimensions of the creative climate survey. This indicates that retailers (especially conventional ones) could benefit from challenging current practices and moving towards becoming more active innovators, since the creative climate to a certain extent seems to allow for it. Respondents within the organisations also express a need for better innovation support, whether it is through integrated, formalised structures and processes, or an improvement in the current conditions of the organizational climate. How retailers could enable themselves to become more active innovators—based on what we know that retailers look more towards entrepreneurship and continuous development as a driving force rather than institutionalized innovation practices per se—is a potential avenue for further research.
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| 7. |
- Batingan Paredes, Karla Marie, et al.
(författare)
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Retail Innovation: perceptions. management, and challenges of a systematic approach
- 2018
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Ingår i: The ISPIM Innovation Conference. - Åkersberga.
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Konferensbidrag (refereegranskat)abstract
- The retail industry is looking for new ways to compete as digitalization and non-traditional actors are changing the game. In an industry where constant improvements are key and studies of innovation management are scarce - this qualitative study of four retail firms addresses how innovation is currently perceived and managed. As a result, we have found that innovation is an uncommon theme and there is no systematic approach to innovation management. Furthermore, despite retailers{\textquoteright} proximity to end users, the customer perspective is surprisingly limited, idea development is present but without clear criteria for selection and prioritization from an innovation point of view. For future research, we point to an interesting avenue for retailers as a “hub” for innovation, given their position in the value chain, acknowledging the need for a more strategic and systematic approach.
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| 8. |
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| 9. |
- Boström, Henrik, et al.
(författare)
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Evaluation of a variance-based nonconformity measure for regression forests
- 2016
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Ingår i: 5th International Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2016. - Cham : Springer. - 9783319333946 - 9783319333953 ; , s. 75-89
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Konferensbidrag (refereegranskat)abstract
- In a previous large-scale empirical evaluation of conformal regression approaches, random forests using out-of-bag instances for calibration together with a k-nearest neighbor-based nonconformity measure, was shown to obtain state-of-the-art performance with respect to efficiency, i.e., average size of prediction regions. However, the use of the nearest-neighbor procedure not only requires that all training data have to be retained in conjunction with the underlying model, but also that a significant computational overhead is incurred, during both training and testing. In this study, a more straightforward nonconformity measure is investigated, where the difficulty estimate employed for normalization is based on the variance of the predictions made by the trees in a forest. A large-scale empirical evaluation is presented, showing that both the nearest-neighbor-based and the variance-based measures significantly outperform a standard (non-normalized) nonconformity measure, while no significant difference in efficiency between the two normalized approaches is observed. Moreover, the evaluation shows that state-of-theart performance is achieved by the variance-based measure at a computational cost that is several orders of magnitude lower than when employing the nearest-neighbor-based nonconformity measure.
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| 10. |
- Boström, Henrik, et al.
(författare)
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Mondrian Conformal Predictive Distributions
- 2021
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Ingår i: Proceedings of the 10th Symposium on Conformal and Probabilistic Prediction and Applications, COPA 2021. - : ML Research Press. ; , s. 24-38
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Konferensbidrag (refereegranskat)abstract
- The distributions output by a standard (non-normalized) conformal predictive system all have the same shape but differ in location, while a normalized conformal predictive system outputs distributions that differ also in shape, through rescaling. An approach to further increasing the flexibility of the framework is proposed, called Mondrian conformal predictive distributions, which are (standard or normalized) conformal predictive distributions formed from multiple Mondrian categories. The effectiveness of the approach is demonstrated with an application to regression forests. By forming categories through binning of the predictions, it is shown that for this model class, the use of Mondrian conformal predictive distributions significantly outperforms the use of both standard and normalized conformal predictive distributions with respect to the continuous-ranked probability score. It is further shown that the use of Mondrian conformal predictive distributions results in as tight prediction intervals as produced by normalized conformal regressors, while improving upon the point predictions of the underlying regression forest.
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