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Structure Learning via Parameter Learning [PDF]
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014 A key challenge in information and knowledge management is to automatically discover the underlying structures and patterns from large collections of extracted information. This paper presents a novel structure-learning method for a new, scalable probabilistic logic called ProPPR.
William Yang Wang +2 more
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Learning the Structure of Event Sequences [PDF]
Journal of Experimental Psychology: General, 1990 How is complex sequential material acquired, processed, and represented when there is no intention to learn? Two experiments exploring a choice reaction time task are reported. Unknown to Ss, successive stimuli followed a sequence derived from a "noisy" finite-state grammar.
Cleeremans, Axel, McClelland, James L.
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Inducing structure in reward learning by learning features
The International Journal of Robotics Research, 2022 Reward learning enables robots to learn adaptable behaviors from human input. Traditional methods model the reward as a linear function of hand-crafted features, but that requires specifying all the relevant features a priori, which is impossible for real-world tasks.
Andreea Bobu +3 more
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Multiscale Causal Structure Learning
Trans. Mach. Learn. Res., 2022 The inference of causal structures from observed data plays a key role in unveiling the underlying dynamics of the system. This paper exposes a novel method, named Multiscale-Causal Structure Learning (MS-CASTLE), to estimate the structure of linear causal relationships occurring at different time scales. Differently from existing approaches, MS-CASTLE
Gabriele D'Acunto +2 more
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Journal of Statistical Software, 2017 It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: Its computational complexity is super-exponential in the number of nodes in the worst case and polynomial in most real-world ...
Marco Scutari
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Tractable Uncertainty for Structure Learning
CoRR, 2022 ICML 2022 (long talk); 20 ...
Benjie Wang 0001 +2 more
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IEEE Access, 2021 At present, in the application of Bayesian network (BN) structure learning algorithm for structure learning, the network scale increases with the increase of number of nodes, resulting in a large scale of structure search space, which is difficult to ...
Kun Liu, Yani Cui, Jia Ren, Peiran Li
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, 2020 Very recently, the unexpected combination of data structures and machine learning has led to the development of a new area of research, called learned data structures. Their distinguishing trait is the ability to reveal and exploit patterns and trends in the input data for achieving more efficiency in time and space, compared to previously known data ...
Paolo Ferragina, Giorgio Vinciguerra
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Structured Apprenticeship Learning [PDF]
, 2012 We propose a graph-based algorithm for apprenticeship learning when the reward features are noisy. Previous apprenticeship learning techniques learn a reward function by using only local state features. This can be a limitation in practice, as often some features are misspecified or subject to measurement noise.
Abdeslam Boularias +2 more
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Temporal context and latent state inference in the hippocampal splitter signal
eLife, 2023 The hippocampus is thought to enable the encoding and retrieval of ongoing experience, the organization of that experience into structured representations like contexts, maps, and schemas, and the use of these structures to plan for the future. A central
Éléonore Duvelle +2 more
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