2022

• Sebastian Berndt, Maciej Liskiewic, Matthias Lutter, Rüdiger Reischuk:
  Learning residual alternating automata.
  Inf. Comput., 289(Part):861-878, 2022.
• Sebastian Berndt, Maciej Liskiewicz, Matthias Lutter, Rüdiger Reischuk:
  Learning residual alternating automata.
  Information and Computation, (289):104981, 2022.
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2019

• Maciej Liskiewicz, Matthias Lutter, Rüdiger Reischuk:
  Proper learning of k-term DNF formulas from satisfying assignments.
  Journal of Computer and System Sciences, 106:129-144, 2019.
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  In certain applications there may be only positive examples available to learn concepts of a class of interest. Furthermore, learning has to be done properly, i.e. the hypothesis space has to coincide with the concept class, and without false positives, i.e. the hypothesis always has to be a subset of the real concept (one-sided error). For the well studied class of k-term DNF formulas it has been known that learning is difficult. Unless RP=NP, it is not feasible to learn k-term DNF formulas properly in a distribution-free sense even if both positive and negative examples are available and even if false positives are allowed. This paper constructs an efficient algorithm that, for fixed but arbitrary k and q, if examples are drawn from q-bounded distributions, it properly learns the class of k-term DNFs without false positives from positive examples alone with arbitrarily small relative error.

2017

• Sebastian Berndt, Maciej Liskiewicz, Matthias Lutter, Rüdiger Reischuk:
  Learning Residual Alternating Automata.
  Electronic Colloquium on Computational Complexity (ECCC), 24(46)2017.
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  Residuality plays an essential role for learning finite automata. While residual deterministic and nondeterministic automata have been understood quite well, fundamental questions concerning alternating automata (AFA) remain open. Recently, Angluin, Eisenstat, and Fisman have initiated a systematic study of residual AFAs and proposed an algorithm called AL* -an extension of the popular L* algorithm - to learn AFAs. Based on computer experiments they conjectured that AL* produces residual AFAs, but have not been able to give a proof. In this paper we disprove this conjecture by constructing a counterexample. As our main positive result we design an efficient learning algorithm, named AL**, and give a proof that it outputs residual AFAs only. In addition, we investigate the succinctness of these different FA types in more detail.
• Sebastian Berndt, Maciej Liskiewicz, Matthias Lutter, Rüdiger Reischuk:
  Learning Residual Alternating Automata.
  Proc. 31st AAAI Conference on Artificial Intelligence (AAAI 2017), S. 1749-1755. AAAI Press, 2017.
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  Residuality plays an essential role for learning finite automata. While residual deterministic and non-deterministic automata have been understood quite well, fundamental questions concerning alternating automata (AFA) remain open. Recently, Angluin, Eisenstat, and Fisman (2015) have initiated a systematic study of residual AFAs and proposed an algorithm called AL* – an extension of the popular L* algorithm – to learn AFAs. Based on computer experiments they have conjectured that AL* produces residual AFAs, but have not been able to give a proof. In this paper we disprove this conjecture by constructing a counterexample. As our main positive result we design an efficient learning algorithm, named AL**, and give a proof that it outputs residual AFAs only. In addition, we investigate the succinctness of these different FA types in more detail.
• Maciej Liskiewicz, Matthias Lutter, Rüdiger Reischuk:
  Proper Learning of k-term DNF Formulas from Satisfying Assignments.
  Electronic Colloquium on Computational Complexity (ECCC), 24(114)2017.
  Website anzeigen | Zusammenfassung anzeigen
  
  In certain applications there may only be positive samples available to to learn concepts of a class of interest, and this has to be done properly, i.e. the hypothesis space has to coincide with the concept class, and without false positives, i.e. the hypothesis always has be a subset of the real concept (one-sided error). For the well studied class of k-term DNF formulas it has been known that learning is difficult. Unless RP = NP, it is not feasible to learn k-term DNF formulas properly in a distribution-free sense even if both positive and negative samples are available and even if false positives are allowed. This paper constructs an efficient algorithm that for arbitrary fixed k, if samples are drawn from distributions like uniform or q-bounded ones, properly learns the class of k-term DNFs without false positives from positive samples alone with arbitrarily small relative error.

2015

• Matthias Ernst, Maciej Liskiewicz, Rüdiger Reischuk:
  Algorithmic Learning for Steganography: Proper Learning of k-term DNF Formulas from Positive Samples.
  In Proc. International Symposium on Algorithms and Computation (ISAAC 2015), Band 9472 von Lecture Notes in Computer Science, S. 151-162. Springer, 2015.
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  Proper learning from positive samples is a basic ingredient for designing secure steganographic systems for unknown covertext channels. In addition, security requirements imply that the hypothesis should not contain false positives. We present such a learner for k-term DNF formulas for the uniform distribution and a generalization to q-bounded distributions. We briefly also describe how these results can be used to design a secure stegosystem.