Accepted papers
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Learning state machines via efficient hashing of future traces
by
Robert Baumgartner and Sicco Verwer
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Towards Efficient Active Learning of PDFA
by
Franz Mayr, Sergio Yovine, Federico Pan, Nicolas Basset and Thao Dang
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An Algebraic Approach to Learning and Grounding
by
Johanna Björklund, Adam Dahlgren Lindström and Frank Drewes
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Spectral Regularization: an Inductive Bias for Sequence Modeling
by
Kaiwen Hou and Guillaume Rabusseau
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Marginal Inference queries in Hidden Markov Models under context-free grammar constraints
by
Mohamed Reda Marzouk and Colin de la Higuera
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Robust Attack Graph Generation
by
Dennis Mouwen, Sicco Verwer and Azqa Nadeem
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Analyzing Büchi Automata with Graph Neural Networks
by
Christophe Stammet, Prisca Dotti, Ulrich Ultes-Nitsche and Andreas Fischer
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Spectral Initialization of Recurrent Neural Networks: Proof of Concept
by
Maude Lizaire, Simon Verret and Guillaume Rabusseau
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Sequential Density Estimation via Nonlinear Continuous Weighted Finite Automata
by
Tianyu Li, Bogdan Mazoure and Guillaume Rabusseau
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Extending Shinohara's Algorithm for Computing Descriptive (Angluin-Style) Patterns to Subsequence Patterns
by
Markus L. Schmid
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Towards an AAK Theory Approach to Approximate Minimization in the Multi-Letter Case
by
Clara Lacroce, Prakash Panangaden and Guillaume Rabusseau
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On the limit of gradient descent for Simple Recurrent Neural Networks with finite precision
by
Rémi Eyraud and Volodimir Mitarchuk
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Learning regular non-deterministic distributions via non-linear optimization methods
by
Wenjing Chu, Shuo Chen and Marcello Bonsangue
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Learning from Positive and Negative Examples: New Proof for Binary Alphabets
by
Jonas Lingg, Mateus de Oliveira Oliveira and Petra Wolf