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| 2. |
- Borin, Lars, 1957, et al.
(författare)
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Mining semantics for culturomics: towards a knowledge-based approach
- 2013
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Ingår i: 2013 ACM International Workshop on Mining Unstructured Big Data Using Natural Language Processing, UnstructureNLP 2013, Held at 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013; San Francisco, CA; United States; 28 October 2013 through 28 October 2013. - New York, NY, USA : ACM. - 9781450324151 ; , s. 3-10
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Konferensbidrag (refereegranskat)abstract
- The massive amounts of text data made available through the Google Books digitization project have inspired a new field of big-data textual research. Named culturomics, this field has attracted the attention of a growing number of scholars over recent years. However, initial studies based on these data have been criticized for not referring to relevant work in linguistics and language technology. This paper provides some ideas, thoughts and first steps towards a new culturomics initiative, based this time on Swedish data, which pursues a more knowledge-based approach than previous work in this emerging field. The amount of new Swedish text produced daily and older texts being digitized in cultural heritage projects grows at an accelerating rate. These volumes of text being available in digital form have grown far beyond the capacity of human readers, leaving automated semantic processing of the texts as the only realistic option for accessing and using the information contained in them. The aim of our recently initiated research program is to advance the state of the art in language technology resources and methods for semantic processing of Big Swedish text and focus on the theoretical and methodological advancement of the state of the art in extracting and correlating information from large volumes of Swedish text using a combination of knowledge-based and statistical methods.
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| 3. |
- Adiga, Aniruddha, et al.
(författare)
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Mathematical Models for COVID-19 Pandemic: A Comparative Analysis
- 2020
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Ingår i: Journal of the Indian Institute of Science. - : Springer Science and Business Media LLC. - 0970-4140 .- 0019-4964. ; 100:4, s. 793-807
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Forskningsöversikt (refereegranskat)abstract
- COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years. Its economic, social and health impact continues to grow and is likely to end up as one of the worst global disasters since the 1918 pandemic and the World Wars. Mathematical models have played an important role in the ongoing crisis; they have been used to inform public policies and have been instrumental in many of the social distancing measures that were instituted worldwide. In this article, we review some of the important mathematical models used to support the ongoing planning and response efforts. These models differ in their use, their mathematical form and their scope.
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| 4. |
- Baltz, A., et al.
(författare)
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Probabilistic analysis for a multiple depot vehicle routing problem
- 2007
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Ingår i: Random Structures and Algorithms. - : Wiley. - 1098-2418 .- 1042-9832. ; 30:1-2, s. 206-225
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Tidskriftsartikel (refereegranskat)abstract
- We give a probabilistic analysis of the Multiple Depot Vehicle Routing Problem (MDVRP) where k depots and it customers are given by i.i.d. random variables in [0, 1](d), d >= 2. The tour length divided by n((d-1)/d) tends to a integral([0,1]d) f(x)((d-1)/d) dx, where,f is the density of the absolutely continuous part of the law of the random variables giving the depots and customers and where the constant alpha depends on the number of depots. If k = o(n), alpha is the constant of the TSP problem. For k = lambda n, lambda > 0, we prove lower and upper bounds on alpha, which decrease as fast as (1 + lambda)(-1/d).
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| 5. |
- Bosch, David, 1997, et al.
(författare)
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Random Features Model with General Convex Regularization: A Fine Grained Analysis with Precise Asymptotic Learning Curves
- 2023
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Ingår i: Proceedings of Machine Learning Research. - 2640-3498. ; 206, s. 11371-11414
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Konferensbidrag (refereegranskat)abstract
- We compute precise asymptotic expressions for the learning curves of least squares random feature (RF) models with either a separable strongly convex regularization or the ℓ1 regularization. We propose a novel multi-level application of the convex Gaussian min max theorem (CGMT) to overcome the traditional difficulty of finding computable expressions for random features models with correlated data. Our result takes the form of a computable 4-dimensional scalar optimization. In contrast to previous results, our approach does not require solving an often intractable proximal operator, which scales with the number of model parameters. Furthermore, we extend the universality results for the training and generalization errors for RF models to ℓ1 regularization. In particular, we demonstrate that under mild conditions, random feature models with elastic net or ℓ1 regularization are asymptotically equivalent to a surrogate Gaussian model with the same first and second moments. We numerically demonstrate the predictive capacity of our results, and show experimentally that the predicted test error is accurate even in the nonasymptotic regime.
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| 6. |
- Carlsson, Emil, 1995, et al.
(författare)
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Learning Approximate and Exact Numeral Systems via Reinforcement Learning
- 2021
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Ingår i: Proceedings of the 43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021. ; 43
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Konferensbidrag (refereegranskat)abstract
- Recent work (Xu et al., 2020) has suggested that numeral systems in different languages are shaped by a functional need for efficient communication in an information-theoretic sense. Here we take a learning-theoretic approach and show how efficient communication emerges via reinforcement learning. In our framework, two artificial agents play a Lewis signaling game where the goal is to convey a numeral concept. The agents gradually learn to communicate using reinforcement learning and the resulting numeral systems are shown to be efficient in the information-theoretic framework of Regier et al.(2015); Gibson et al. (2017). They are also shown to be similar to human numeral systems of same type. Our results thus provide a mechanistic explanation via reinforcement learning of the recent results in Xu et al. (2020) and can potentially be generalized to other semantic domains.
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| 7. |
- Carlsson, Emil, 1995, et al.
(författare)
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Pragmatic Reasoning in Structured Signaling Games
- 2022
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Ingår i: Proceedings of the 44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022. ; , s. 2831-2837
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Konferensbidrag (refereegranskat)abstract
- In this work we introduce a structured signaling game, an extension of the classical signaling game with a similarity structure between meanings in the context, along with a variant of the Rational Speech Act (RSA) framework which we call structured-RSA (sRSA) for pragmatic reasoning in structured domains. We explore the behavior of the sRSA in the domain of color and show that pragmatic agents using sRSA on top of semantic representations, derived from the World Color Survey, attain efficiency very close to the information theoretic limit after only 1 or 2 levels of recursion. We also explore the interaction between pragmatic reasoning and learning in multi-agent reinforcement learning framework. Our results illustrate that artificial agents using sRSA develop communication closer to the information theoretic frontier compared to agents using RSA and just reinforcement learning. We also find that the ambiguity of the semantic representation increases as the pragmatic agents are allowed to perform deeper reasoning about each other during learning.
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| 8. |
- Carlsson, Emil, 1995, et al.
(författare)
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Pure Exploration in Bandits with Linear Constraints
- 2024
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Ingår i: Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022. - 2640-3498. ; 238, s. 334-342
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Konferensbidrag (refereegranskat)abstract
- We address the problem of identifying the optimal policy with a fixed confidence level in a multi-armed bandit setup, when the arms are subject to linear constraints. Unlike the standard best-arm identification problem which is well studied, the optimal policy in this case may not be deterministic and could mix between several arms. This changes the geometry of the problem which we characterize via an information-theoretic lower bound. We introduce two asymptotically optimal algorithms for this setting, one based on the Track-and-Stop method and the other based on a game-theoretic approach. Both these algorithms try to track an optimal allocation based on the lower bound and computed by a weighted projection onto the boundary of a normal cone. Finally, we provide empirical results that validate our bounds and visualize how constraints change the hardness of the problem.
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| 9. |
- Carlsson, Emil, 1995, et al.
(författare)
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Thompson Sampling for Bandits with Clustered Arms
- 2021
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Ingår i: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. - California : International Joint Conferences on Artificial Intelligence Organization. - 9780999241196
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Konferensbidrag (refereegranskat)abstract
- We propose algorithms based on a multi-level Thompson sampling scheme, for the stochastic multi-armed bandit and its contextual variant with linear expected rewards, in the setting where arms are clustered. We show, both theoretically and empirically, how exploiting a given cluster structure can significantly improve the regret and computational cost compared to using standard Thompson sampling. In the case of the stochastic multi-armed bandit we give upper bounds on the expected cumulative regret showing how it depends on the quality of the clustering. Finally, we perform an empirical evaluation showing that our algorithms perform well compared to previously proposed algorithms for bandits with clustered arms.
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| 10. |
- Clermont, Gilles, et al.
(författare)
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Bridging the gap between systems biology and medicine
- 2009
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Ingår i: Genome Medicine. - : Springer Science and Business Media LLC. - 1756-994X. ; 1:9
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Tidskriftsartikel (refereegranskat)abstract
- ABSTRACT : Systems biology has matured considerably as a discipline over the last decade, yet some of the key challenges separating current research efforts in systems biology and clinically useful results are only now becoming apparent. As these gaps are better defined, the new discipline of systems medicine is emerging as a translational extension of systems biology. How is systems medicine defined? What are relevant ontologies for systems medicine? What are the key theoretic and methodologic challenges facing computational disease modeling? How are inaccurate and incomplete data, and uncertain biologic knowledge best synthesized in useful computational models? Does network analysis provide clinically useful insight? We discuss the outstanding difficulties in translating a rapidly growing body of data into knowledge usable at the bedside. Although core-specific challenges are best met by specialized groups, it appears fundamental that such efforts should be guided by a roadmap for systems medicine drafted by a coalition of scientists from the clinical, experimental, computational, and theoretic domains.
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