25 research outputs found
Graph Representations for Higher-Order Logic and Theorem Proving
This paper presents the first use of graph neural networks (GNNs) for
higher-order proof search and demonstrates that GNNs can improve upon
state-of-the-art results in this domain. Interactive, higher-order theorem
provers allow for the formalization of most mathematical theories and have been
shown to pose a significant challenge for deep learning. Higher-order logic is
highly expressive and, even though it is well-structured with a clearly defined
grammar and semantics, there still remains no well-established method to
convert formulas into graph-based representations. In this paper, we consider
several graphical representations of higher-order logic and evaluate them
against the HOList benchmark for higher-order theorem proving
Lassie: HOL4 Tactics by Example
Proof engineering efforts using interactive theorem proving have yielded
several impressive projects in software systems and mathematics. A key obstacle
to such efforts is the requirement that the domain expert is also an expert in
the low-level details in constructing the proof in a theorem prover. In
particular, the user needs to select a sequence of tactics that lead to a
successful proof, a task that in general requires knowledge of the exact names
and use of a large set of tactics.
We present Lassie, a tactic framework for the HOL4 theorem prover that allows
individual users to define their own tactic language by example and give
frequently used tactics or tactic combinations easier-to-remember names. The
core of Lassie is an extensible semantic parser, which allows the user to
interactively extend the tactic language through a process of definitional
generalization. Defining tactics in Lassie thus does not require any knowledge
in implementing custom tactics, while proofs written in Lassie retain the
correctness guarantees provided by the HOL4 system. We show through case
studies how Lassie can be used in small and larger proofs by novice and more
experienced interactive theorem prover users, and how we envision it to ease
the learning curve in a HOL4 tutorial
A Formal Proof of PAC Learnability for Decision Stumps
We present a formal proof in Lean of probably approximately correct (PAC)
learnability of the concept class of decision stumps. This classic result in
machine learning theory derives a bound on error probabilities for a simple
type of classifier. Though such a proof appears simple on paper, analytic and
measure-theoretic subtleties arise when carrying it out fully formally. Our
proof is structured so as to separate reasoning about deterministic properties
of a learning function from proofs of measurability and analysis of
probabilities.Comment: 13 pages, appeared in Certified Programs and Proofs (CPP) 202
Large expert-curated database for benchmarking document similarity detection in biomedical literature search
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe