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- research-article
Time series of satellite imagery improve deep learning estimates of neighborhood-level poverty in africa
IJCAI '23: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence•August 2023, Article No.: 684, pp 6165-6173• https://doi.org/10.24963/ijcai.2023/684To combat poor health and living conditions, policymakers in Africa require temporally and geographically granular data measuring economic well-being. Machine learning (ML) offers a promising alternative to expensive and time-consuming survey measurements ...
- research-article
Sharing pattern submodels for prediction with missing values
AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence•February 2023, Article No.: 1110, pp 9882-9890• https://doi.org/10.1609/aaai.v37i8.26179Missing values are unavoidable in many applications of machine learning and present challenges both during training and at test time. When variables are missing in recurring patterns, fitting separate pattern submodels have been proposed as a solution. ...
- research-article
Generalization bounds and representation learning for estimation of potential outcomes and causal effects
Practitioners in diverse fields such as healthcare, economics and education are eager to apply machine learning to improve decision making. The cost and impracticality of performing experiments and a recent monumental increase in electronic record keeping ...
- research-article
Learning to search efficiently for causally near-optimal treatments
NIPS '20: Proceedings of the 34th International Conference on Neural Information Processing Systems•December 2020, Article No.: 113, pp 1333-1344Finding an effective medical treatment often requires a search by trial and error. Making this search more efficient by minimizing the number of unnecessary trials could lower both costs and patient suffering. We formalize this problem as learning a ...
- research-article
Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters: Using Clustering and Rule-Based Machine Learning
IMMS '20: Proceedings of the 3rd International Conference on Information Management and Management Science•August 2020, pp 13-22• https://doi.org/10.1145/3416028.3417215Trucks are designed, configured and marketed for various working environments. There lies a concern whether trucks are used as intended by the manufacturer, as usage may impact the longevity, efficiency and productivity of the trucks. In this paper we ...
- research-article
Estimation of bounds on potential outcomes for decision making
ICML'20: Proceedings of the 37th International Conference on Machine Learning•July 2020, Article No.: 618, pp 6661-6671Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics. However, it is often sufficient for the decision maker to have estimates of upper and lower ...
- Article
Why is my classifier discriminatory?
NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing Systems•December 2018, pp 3543-3554Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy. In sensitive applications such as healthcare or criminal justice, this trade-off is often undesirable as any increase in prediction error could ...
- Article
Estimating individual treatment effect: generalization bounds and algorithms
ICML'17: Proceedings of the 34th International Conference on Machine Learning - Volume 70•August 2017, pp 3076-3085There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In particular, individual-level causal inference has important applications such as precision medicine. We give ...
- Article
Clustering by sum of norms: stochastic incremental algorithm, convergence and cluster recovery
ICML'17: Proceedings of the 34th International Conference on Machine Learning - Volume 70•August 2017, pp 2769-2777Standard clustering methods such as K-means, Gaussian mixture models, and hierarchical clustering, are beset by local minima, which are sometimes drastically suboptimal. Moreover the number of clusters K must be known in advance. The recently introduced ...
- Article
Learning representations for counterfactual inference
ICML'16: Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48•June 2016, pp 3020-3029Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. We consider the task of answering counterfactual questions such as, "Would this patient have lower ...
- Article
Weighted theta functions and embeddings with applications to Max-Cut, clustering and summarization
NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1•December 2015, pp 1018-1026We introduce a unifying generalization of the Lovasz theta function, and the associated geometric embedding, for graphs with weights on both nodes and edges. We show how it can be computed exactly by semidefinite programming, and how to approximate it ...
- research-article
Learning with Similarity Functions on Graphs using Matchings of Geometric Embeddings
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining•August 2015, pp 467-476• https://doi.org/10.1145/2783258.2783341We develop and apply the Balcan-Blum-Srebro (BBS) theory of classification via similarity functions (which are not necessarily kernels) to the problem of graph classification. First we place the BBS theory into the unifying framework of optimal ...
- article
Visions and open challenges for a knowledge-based culturomics
- Nina Tahmasebi,
- Lars Borin,
- Gabriele Capannini,
- Devdatt Dubhashi,
- Peter Exner,
- Markus Forsberg,
- Gerhard Gossen,
- Fredrik D. Johansson,
- Richard Johansson,
- Mikael Kågebäck,
- Olof Mogren,
- Pierre Nugues,
- Thomas Risse
International Journal on Digital Libraries, Volume 15, Issue 2-4•April 2015, pp 169-187 • https://doi.org/10.1007/s00799-015-0139-1The concept of culturomics was born out of the availability of massive amounts of textual data and the interest to make sense of cultural and language phenomena over time. Thus far however, culturomics has only made use of, and shown the great potential ...
- Article
Global graph kernels using geometric embeddings
ICML'14: Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32•June 2014, pp II-694-II-702Applications of machine learning methods increasingly deal with graph structured data through kernels. Most existing graph kernels compare graphs in terms of features defined on small subgraphs such as walks, paths or graphlets, adopting an inherently ...
- Doctoral Theses
Global Geometric Graph Kernels and Applications
AbstractThis thesis explores the topics of graph kernels and classification of graphs. Graph kernels have received considerable attention in the last decade, in part because of their value in many practical applications, such as chemo informatics and ...
- Article
DLOREAN: Dynamic Location-Aware Reconstruction of Multiway Networks
ICDMW '13: Proceedings of the 2013 IEEE 13th International Conference on Data Mining Workshops•December 2013, pp 1012-1019• https://doi.org/10.1109/ICDMW.2013.57This paper presents a method for learning time-varying higher-order interactions based on node observations, with application to short-term traffic forecasting based on traffic flow sensor measurements. We incorporate domain knowledge into the design of ...
- research-article
Entity disambiguation in anonymized graphs using graph kernels
CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management•October 2013, pp 1037-1046• https://doi.org/10.1145/2505515.2505565This paper presents a novel method for entity disambiguation in anonymized graphs using local neighborhood structure. Most existing approaches leverage node information, which might not be available in several contexts due to privacy concerns, or ...
- research-article
Intent-aware temporal query modeling for keyword suggestion
PIKM '12: Proceedings of the 5th Ph.D. workshop on Information and knowledge•November 2012, pp 83-86• https://doi.org/10.1145/2389686.2389703This paper presents a data-driven approach for capturing the temporal variations in user search behaviour by modeling the dynamic query relationships using query-log data. The dependence between different queries (in terms of the query words and latent ...
