Programming
Software development: programming languages, coding, libraries and frameworks, software engineering and architecture, source control, developer tools, databases, and infrastructure.
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Analysis of Langevin Monte Carlo via Convex Optimization jmlr.org
Journal of Machine Learning Research, 2019. [All Versions]. This paper provides new insights on the Unadjusted Langevin Algorithm. The authors show that this method can be formulated as the first order optimization algorithm for an objective functional defined on the…
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Biocoder: A programming language for standardizing and automating biology protocols jbioleng.biomedcentral.com
Journal of Biological Engineering, 2010. [All Versions]. [Project]. [Microsoft Page] This paper introduces BioCoder, a C++ library that enables biologists to express the exact steps needed to execute a protocol. In addition to being suitable for automation, BioCoder converts…
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Learning Explanatory Rules from Noisy Data jair.org
Journal of Artificial Intelligence Research, 2018. [All Versions]. The original paper for differential Inductive Logic Programming.
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Turning 30: New Ideas in Inductive Logic Programming ijcai.org
IJCAI'20, 2020. [All Versions].
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Neural Task Programming: Learning to Generalize Across Hierarchical Tasks ieeexplore.ieee.org
ICRA'18, 2018. [All Versions].
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Visual Concept Programming: A Visual Analytics Approach to Injecting Human Intelligence at Scale ieeexplore.ieee.org
IEEE Transactions on Visualization and Computer Graphics, 2023. [All Versions]. This paper presents Visual Concept Programming, a first-of-its-kind visual analytics approach of using visual concepts to program image data at scale while requiring a few human efforts.
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Domain Specific Language for Smart Contract Development ieeexplore.ieee.org
ICBC'20, 2020. [All Versions]. [Preprint]. This research addresses the understanding hardness raised from the conceptual discrepancy between contractual clauses and corresponding code of the Solidity programming language, by the design and study of a domain-specific smart…
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Taking the Human Out of the Loop: A Review of Bayesian Optimization ieeexplore.ieee.org
Proceedings of the IEEE, 2015. [All Versions]. [Preprint]. Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such…
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Usability Evaluation of Domain-Specific Languages ieeexplore.ieee.org
ICQICT'12, 2012. [All Versions]. [Preprint]. The purpose of this proposal is to contribute to the systematic activity of Software Language Engineering by focusing on the issue of the Usability evaluation of DSLs. Usability evaluation is often skipped, relaxed, or at least…
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Product Line Engineering Using Domain-Specific Languages ieeexplore.ieee.org
ISPLC'11, 2011. [All Versions]. [Preprint]. This paper investigates the application of domain-specific languages in product line engineering (PLE). It starts by analyzing the limits of expressivity of feature models. Feature models correspond to context-free grammars without…
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LogSay: An Efficient Comprehension System for Log Numerical Reasoning ieeexplore.ieee.org
IEEE Transactions on Computers, 2024. [All Versions]. With the growth of smart systems and applications, high volume logs are generated that record important data for system maintenance. System developers are usually required to analyze logs to track the status of the system or…
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Comparison of multi-paradigm programming languages en.wikipedia.org
Wikipedia. Programming languages may support multiple programming paradigms. This Wikipedia encyclopedia entry lists a concise reference for the programming paradigms.
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Robotic search for optimal cell culture in regenerative medicine elifesciences.org
eLife, 2022. [All Versions]. This paper develops a robotic AI system with a batch Bayesian optimization algorithm that autonomously induces the differentiation of induced pluripotent stem cell-derived retinal pigment epithelial (iPSC-RPE) cells. From 200 million possible…
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Epigrams on programming dl.acm.org
ACM SIGPLAN Notices, 1982. [All Versions].
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On the Effectiveness of Large Language Models in Domain-Specific Code Generation dl.acm.org
ACM Transactions on Software Engineering and Methodology, 2025. [All Versions]. Large language models (LLMs) such as ChatGPT have shown remarkable capabilities in code generation. Despite significant achievements, they rely on enormous training data to acquire a broad spectrum…
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ACM Transactions on Software Engineering and Methodology, 2024. [All Versions]. Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents to software engineering researchers the six key challenges that a domain…
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"We Need Structured Output": Towards User-centered Constraints on Large Language Model Output dl.acm.org
CHI EA'24, 2024. [All Versions]. [Preprint]. Large language models can produce creative and diverse responses. However, to integrate them into current developer workflows, it is essential to constrain their outputs to follow specific formats or standards. This work surveyed 51…
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CoLadder: Manipulating Code Generation via Multi-Level Blocks dl.acm.org
UIST'24, 2024. [All Versions]. This paper adopted an iterative design process to gain insights into programmers’ strategies when using LLMs for programming. The authors proposed CoLadder, a novel system that supports programmers by facilitating hierarchical task decomposition…
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How Domain Experts Use an Embedded DSL dl.acm.org
OOPSLA'23, 2023. [All Versions]. Programming tools are increasingly integral to research and analysis in myriad domains, including specialized areas with no formal relation to computer science. Embedded domain-specific languages (eDSLs) have the potential to serve these…
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GarmentCode: Programming Parametric Sewing Patterns dl.acm.org
ACM Transactions on Graphics, 2023. [All Versions]. Garment modeling is an essential task of the global apparel industry and a core part of digital human modeling. Realistic representation of garments with valid sewing patterns is key to their accurate digital simulation and…
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A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges dl.acm.org
ICSE'24, 2024. [All Versions]. A survey finding that developers are most motivated to use AI programming assistants because they help developers reduce key-strokes, finish programming tasks quickly, and recall syntax, but resonate less with using them to help brainstorm…
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Combining Functional and Automata Synthesis to Discover Causal Reactive Programs dl.acm.org
POPL'23, 2023. [All Versions]. A new algorithm that synthesizes functional reactive programs from observation data, which iterates between a functional synthesis step, which attempts to generate a transition function over observed states, and an automata synthesis step, which…
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Top-Down Synthesis for Library Learning dl.acm.org
POPL'23, 2023. [All Versions]. This paper introduces corpus-guided top-down synthesis as a mechanism for synthesizing library functions that capture common functionality from a corpus of programs in a domain specific language (DSL). The algorithm builds abstractions directly…
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babble: Learning Better Abstractions with E-Graphs and Anti-unification dl.acm.org
POPL'23, 2023. [All Versions]. This paper proposes library learning modulo theory (LLMT), a new library learning algorithm that additionally takes as input an equational theory for a given problem domain. LLMT uses e-graphs and equality saturation to compactly represent the…
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Domain-specific languages: an annotated bibliography dl.acm.org
ACM SIGPLAN Notices, 2000. [All Versions]. A survey on the topic of domain-specific languages as used for the construction and maintenance of software systems. The survey lists a selection of 75 key publications in the area, and provides a summary for each of the papers…
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PLIERS: A Process that Integrates User-Centered Methods into Programming Language Design dl.acm.org
ACM Transactions on Computer-Human Interaction, 2021. [All Versions]. Programming language design requires making many usability-related design decisions. However, existing HCI methods can be impractical to apply to programming languages: languages have high iteration costs…
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Synthesis of Incremental Linear Algebra Programs dl.acm.org
ACM Transactions on Database Systems, 2020. [All Versions]. This article targets the Incremental View Maintenance (IVM) of sophisticated analytics (such as statistical models, machine learning programs, and graph algorithms) expressed as linear algebra programs. This work…
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A Taxonomy of Domain-Specific Aspect Languages dl.acm.org
ACM Computing Surveys, 2015. [All Versions]. Domain-Specific Aspect Languages (DSALs) are Domain-Specific Languages (DSLs) designed to express crosscutting concerns. Compared to DSLs, their aspectual nature greatly amplifies the language design space. This survey structures…
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When and How to Develop Domain-Specific Languages dl.acm.org
ACM Computing Surveys, 2005. [All Versions]. [Preprint]. Domain-specific languages (DSLs) are languages tailored to a specific application domain. They offer substantial gains in expressiveness and ease of use compared with general-purpose programming languages in their domain…
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Exploring human cognition using large image databases cocosci.princeton.edu
Topics in Cognitive Sciences, 2016. [All Versions].
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A high-level programming language for generative protein design biorxiv.org
2022. [All Versions]. Combining a basic set of building blocks into more complex forms is a universal design principle. Most protein designs have proceeded from a manual bottom-up approach using parts created by nature, but top-down design of proteins is fundamentally hard due…
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Intentonomy: a Dataset and Study towards Human Intent Understanding arxiv.org
CVPR'21, 2021. [All Versions]. A large-scale database on human intentionally-posted images on social media.
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Human Comprehensible Active Learning of Genome-Scale Metabolic Networks arxiv.org
AAAI Spring Symposium Series 2023 on Computational Scientific Discovery, 2023. [All Versions]. [Extended Abstract]. [Slides]. This work introduces a novel machine learning framework ILP-iML1515 based on Inductive Logic Programming (ILP) that performs abductive logical reasoning…
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Large Language Models for Software Engineering: A Systematic Literature Review arxiv.org
2023. [All Versions]. A systematic literature review on LLM4SE, with a particular focus on understanding how LLMs can be exploited to optimize processes and outcomes.
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2023. [All Versions]. [Project]. [Website]. This paper proposes CLAIRIFY, an approach that combines automatic iterative prompting with program verification to ensure programs written in data-scarce domain-specific language are syntactically valid and incorporate environment…
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Program Synthesis with Large Language Models arxiv.org
2021. [All Versions]. This paper explores the limits of the current generation of large language models for program synthesis in general purpose programming languages.
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Inductive logic programming at 30: a new introduction arxiv.org
Journal of Artificial Intelligence Research, 2020. [All Versions]. A 30-year comprehensive review on Inductive Logic Programming.
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DeepProbLog: Neural Probabilistic Logic Programming arxiv.org
NeurIPS'18, 2018. [All Versions]. The original paper on neuro-symbolic probabilistic programming.
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Design Guidelines for Domain Specific Languages arxiv.org
OOPSLA Workshop on Domain-Specific Modeling (DSM' 09), 2009. [All Versions]. Designing a new domain specific language is as any other complex task sometimes error-prone and usually time consuming, especially if the language shall be of high-quality and comfortably usable…
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CodeBERT: A Pre-Trained Model for Programming and Natural Languages aclanthology.org
EMNLP'20, 2020. [All Versions]. Completing code, generating programming documentation, and providing technical support, making programming knowledge more accessible to non-experts.
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Abductive Logic Programming academic.oup.com
Journal of Logic Computation, 1992. [All Versions]. This paper is a survey and critical overview of recent work on the extension of logic programming to perform abductive reasoning (abductive logic programming). The authors outline the general framework of abduction and its…
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Theory of Minds: Understanding Behavior in Groups through Inverse Planning aaai.org
AAAI'19, 2019. [All Versions]. Towards the goal of building machine-learning algorithms with human-like social intelligence, this paper develops a generative model of multiagent action understanding based on a novel representation for these latent relationships called…
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Stealing Machine Learning Models via Prediction APIs usenix.org
(Usenix Security) (Code) Aims at extracting machine learning models in use by remote services.
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Black-Box Ripper: Copying black-box models using generative evolutionary algorithms proceedings.neurips.cc
(NeurIPS) Replicates the functionality of a black-box neural model, yet with no limit on the amount of queries (via a teacher/student scheme and an evolutionary search).
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Active Fairness Auditing proceedings.mlr.press
(ICML) Studies of query-based auditing algorithms that can estimate the demographic parity of ML models in a query-efficient manner.
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Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information proceedings.mlr.press
(ICML) A budget constrained and Bayesian optimization procedure to extract properties out of a black-box algorithm.
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Stealing the Decoding Algorithms of Language Models people.cs.umass.edu
(CCS) Steal the type and hyperparameters of the decoding algorithms of a LLM.
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Opening Up the Black Box:Auditing Google's Top Stories Algorithm par.nsf.gov
(Flairs-32) Audit of the Google's Top stories panel that pro-vides insights into its algorithmic choices for selectingand ranking news publisher
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Confidential-PROFITT: Confidential PROof of FaIr Training of Trees openreview.net
(ICLR) Proposes fair decision tree learning algorithms along with zero-knowledge proof protocols to obtain a proof of fairness on the audited server.
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Two-Face: Adversarial Audit of Commercial Face Recognition Systems ojs.aaai.org
(ICWSM) Performs an adversarial audit on multiple systems APIs and datasets, making a number of concerning observations.
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