38,389 research outputs found
Statistical Learning of Arbitrary Computable Classifiers
Statistical learning theory chiefly studies restricted hypothesis classes,
particularly those with finite Vapnik-Chervonenkis (VC) dimension. The
fundamental quantity of interest is the sample complexity: the number of
samples required to learn to a specified level of accuracy. Here we consider
learning over the set of all computable labeling functions. Since the
VC-dimension is infinite and a priori (uniform) bounds on the number of samples
are impossible, we let the learning algorithm decide when it has seen
sufficient samples to have learned. We first show that learning in this setting
is indeed possible, and develop a learning algorithm. We then show, however,
that bounding sample complexity independently of the distribution is
impossible. Notably, this impossibility is entirely due to the requirement that
the learning algorithm be computable, and not due to the statistical nature of
the problem.Comment: Expanded the section on prior work and added reference
Programmability of Chemical Reaction Networks
Motivated by the intriguing complexity of biochemical circuitry within individual cells we study Stochastic Chemical Reaction Networks (SCRNs), a formal model that considers a set of chemical reactions acting on a finite number of molecules in a well-stirred solution according to standard chemical kinetics equations. SCRNs have been widely used for describing naturally occurring (bio)chemical systems, and with the advent of synthetic biology they become a promising language for the design of artificial biochemical circuits. Our interest here is the computational power of SCRNs and how they relate to more conventional models of computation. We survey known connections and give new connections between SCRNs and Boolean Logic Circuits, Vector Addition Systems, Petri Nets, Gate Implementability, Primitive Recursive Functions, Register Machines, Fractran, and Turing Machines. A theme to these investigations is the thin line between decidable and undecidable questions about SCRN behavior
Data Definitions in the ACL2 Sedan
We present a data definition framework that enables the convenient
specification of data types in ACL2s, the ACL2 Sedan. Our primary motivation
for developing the data definition framework was pedagogical. We were teaching
undergraduate students how to reason about programs using ACL2s and wanted to
provide them with an effective method for defining, testing, and reasoning
about data types in the context of an untyped theorem prover. Our framework is
now routinely used not only for pedagogical purposes, but also by advanced
users.
Our framework concisely supports common data definition patterns, e.g. list
types, map types, and record types. It also provides support for polymorphic
functions. A distinguishing feature of our approach is that we maintain both a
predicative and an enumerative characterization of data definitions.
In this paper we present our data definition framework via a sequence of
examples. We give a complete characterization in terms of tau rules of the
inclusion/exclusion relations a data definition induces, under suitable
restrictions. The data definition framework is a key component of
counterexample generation support in ACL2s, but can be independently used in
ACL2, and is available as a community book.Comment: In Proceedings ACL2 2014, arXiv:1406.123
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