AI
Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.
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Learning Higher-Order Logic Programs through Abstraction and Invention andrewcropper.com
IJCAI'16, 2016. [All Versions].
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Learning Efficient Logical Robot Strategies Involving Composable Objects andrewcropper.com
IJCAI'15, 2015. [All Versions].
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Forgetting to Learn Logic Programs andrewcropper.com
AAAI'20, 2020. [All Versions].
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Reinforcement learning: An introduction andrew.cmu.edu
MIT Press, 2018. [All Versions]. Richard Sutton's comprehensive book on reinforcement learning.
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SciBERT: A Pretrained Language Model for Scientific Text aclanthology.org
EMNLP'19, 2019. [All Versions]. Searching and synthesizing scientific literature, aiding researchers in hypothesis generation, and assisting with experimental design, making scientific knowledge more accessible.
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Do Llamas Work in English? On the Latent Language of Multilingual Transformers aclanthology.org
ACL'24, 2024. [All Versions]. A preliminary work empirically showing that the intermediate embeddings of multilingual Transformers (1) start far away from output token embeddings; (2) already allow for decoding a semantically correct next token in the middle layers, but give…
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BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in Biology aclanthology.org
EMNLP'23, 2023. [All Versions]. [Project]. This paper presents an automatic evaluation framework for the task of planning experimental protocols, and introduces BioProt: a dataset of biology protocols with corresponding pseudocode representations.
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Large Language Models Meet NL2Code: A Survey aclanthology.org
ACL'23, 2023. [All Versions]. [NL2Code Website]. A paper presenting a comprehensive survey of 27 existing large language models for NL2Code, and also review benchmarks and metrics, suggesting that the key factors contributing to the success of large language models for NL2Code…
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PersLEARN: Research Training through the Lens of Perspective Cultivation aclanthology.org
ACL'23, 2023. [All Versions]. Scientific research is inherently shaped by its authors’ perspectives, influenced by various factors such as their personality, community, or society. Junior researchers often face challenges in identifying the perspectives reflected in the…
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Unit Testing for Concepts in Neural Networks aclanthology.org
Transactions of the Association for Computational Linguistics, 2022. [All Versions]. Testing the concept representation by neural networks through Fodor's theory of concepts.
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ESRA: Explainable Scientific Research Assistant aclanthology.org
ACL'21 Demo Track, 2021. [All Versions]. A tool for constructing and visualizing the knowledge graph of a query keyword in literature retrieving.
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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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Experience Grounds Language aclanthology.org
EMNLP'20, 2020. [All Versions]. A perspective on the furture of computational linguistics research---commonsense-driven and embodied language.
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How large language models need symbolism academic.oup.com
National Science Review, 2025. [All Versions]. Advances in artificial intelligence (AI), particularly large language models (LLMs), have achieved remarkable success. This progress stems from ‘scaling laws’---performance improves with greater computation, data and model size…
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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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An Approach to Abductive Reasoning in Equational Logic aaai.org
IJCAI'13, 2013. [All Versions]. Abduction has been extensively studied in propositional logic because of its many applications in artificial intelligence. However, its intrinsic complexity has been a limitation to the implementation of abductive reasoning tools in more…
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Learning Systems of Concepts with an Infinite Relational Model aaai.org
AAAI'06, 2006. [All Versions].
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The Public Acquisition of Commonsense Knowledge aaai.org
Proceedings of AAAI Spring Symposium on Acquiring (and Using) Linguistic (and World) Knowledge for Information Access, 2002. [All Versions]. The first attempt for acquring commonsense knowlege from humans' activities on the internet.
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Handbook of Knowledge Representation 1lib.net
Elsevier, 2008. [All Versions]. A pragmatical handbook for all kinds of knowledge representation modes.
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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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Remote Explainability faces the bouncer problem rdcu.be
(Nature Machine Intelligence volume 2, pages529–539) (Code) Shows the impossibility (with one request) or the difficulty to spot lies on the explanations of a remote AI decision.
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Identifying the Machine Learning Family from Black-Box Models rd.springer.com
(CAEPIA) Determines which kind of machine learning model is behind the returned predictions.
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Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis proceedings.neurips.cc
(NeurIPS) Sobol indices provide an efficient way to capture higher-order interactions between image regions and their contributions to a (black box) neural network’s prediction through the lens of variance.
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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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Privacy Auditing with One (1) Training Run neurips.cc
(NeurIPS - best paper) A scheme for auditing differentially private machine learning systems with a single training run.
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Membership Inference Attacks Against Machine Learning Models ieeexplore.ieee.org
(Symposium on Security and Privacy) Given a machine learning model and a record, determine whether this record was used as part of the model's training dataset or not.
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Stealing Knowledge from Protected Deep Neural Networks Using Composite Unlabeled Data ieeexplore.ieee.org
(ICNN) Composite method which can be used to attack and extract the knowledge ofa black box model even if it completely conceals its softmaxoutput.
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Under manipulations, are some AI models harder to audit? grodino.github.io
(SATML) Relates the difficulty of black-box audits to the capacity of the targeted models, using the Rademacher complexity.
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Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment dl.acm.org
(CCS) Model inversion approach in the adversary setting based on training an inversion model that acts as aninverse of the original model. With no fullknowledge about the original training data, an accurate inversion is still possible by training the inversion model on…
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Practical Black-Box Attacks against Machine Learning dl.acm.org
(Asia CCS) Understand how vulnerable is a remote service to adversarial classification attacks.
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The Fair Game: Auditing & debiasing AI algorithms overtime cambridge.org
(Cambridge Forum on AI: Law and Governance) Aims to simulate the evolution of ethical and legal frameworks in the society by creating an auditor which sends feedback to a debiasing algorithm deployed around an ML system.
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The Fair Game: Auditing & debiasing AI algorithms over time cambridge.org
Jouarnal of Cambridge Forum on AI: Law and Governance
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Auditing Pay-Per-Token in Large Language Models arxiv.org
(arXiv) Develops an auditing framework based on martingale theory that enables a trusted third-party auditor who sequentially queries a provider to detect token misreporting.
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Hardware and software platform inference arxiv.org
(arXiv) A method for identifying the underlying GPU architecture and software stack of a black-box machine learning model solely based on its input-output behavior.
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FairProof: Confidential and Certifiable Fairness for Neural Networks arxiv.org
(Arxiv) Proposes an alternative paradigm to traditional auditing using crytographic tools like Zero-Knowledge Proofs; gives a system called FairProof for verifying fairness of small neural networks.
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(ICLR) Considers backdoor detection under the black-box setting in machine learning as a service (MLaaS) applications.
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Extracting Training Data from Large Language Models arxiv.org
(USENIX Security) Extract verbatim text sequences from the GPT-2 model’s training data.
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Extracting Training Data from Large Language Models arxiv.org
(arxiv) Performs a training data extraction attack to recover individual training examples by querying the language model.
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Making targeted black-box evasion attacks effective andefficient arxiv.org
(arXiv) Investigates how an adversary can optimally use its query budget for targeted evasion attacks against deep neural networks.
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Neural Network Model Extraction Attacks in Edge Devicesby Hearing Architectural Hints arxiv.org
(arxiv) Through the acquisition of memory access events from bus snooping, layer sequence identification bythe LSTM-CTC model, layer topology connection according to the memory access pattern, and layer dimension estimation under data volume constraints, it demonstrates one can…
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Stealing Neural Networks via Timing Side Channels arxiv.org
(arXiv) Stealing/approximating a model through timing attacks usin queries.
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CALM: Curiosity-Driven Auditing for Large Language Models arxiv.org
(AAAI) Auditing as a black-box optimization problem where the goal is to automatically uncover input-output pairs of the target LLMs that exhibit illegal, immoral, or unsafe behaviors.
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LLMs hallucinate graphs too: a structural perspective arxiv.org
(complex networks) Queries LLMs for known graphs and studies topological hallucinations. Proposes a structural hallucination rank.
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Adversarial Model Extraction on Graph Neural Networks arxiv.org
(AAAI Workshop on Deep Learning on Graphs: Methodologies and Applications) Introduces GNN model extraction and presents a preliminary approach for this.
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TamperNN: Efficient Tampering Detection of Deployed Neural Nets arxiv.org
(ISSRE) Algorithms to craft inputs that can detect the tampering with a remotely executed classifier model.
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Adversarial Frontier Stitching for Remote Neural Network Watermarking arxiv.org
(Neural Computing and Applications) (Alternative implementation) Check if a remote machine learning model is a "leaked" one: through standard API requests to a remote model, extract (or not) a zero-bit watermark, that was inserted to watermark valuable models (eg, large deep…
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Towards Reverse-Engineering Black-Box Neural Networks arxiv.org
(ICLR) (Code) Infer inner hyperparameters (eg number of layers, non-linear activation type) of a remote neural network model by analysing its response patterns to certain inputs.
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StatQuest youtube.com
Introductory video explanations of statistics, probability, and machine learning.
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Mathematics and AI sites.math.washington.edu
University of Washington Fall 2025 graduate course with public slides, Lean examples, projects, and readings on machine learning, formalization, autoformalization, mathematical discovery, and ethics.
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AI for Mathematics and Theoretical Computer Science simons.berkeley.edu
Simons Institute and SLMath workshop archive with recorded talks on proof assistants, automated reasoning, machine learning, and mathematical discovery.
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