Research
My research focuses on Neurosymbolic AI, which aims to make machine learning capable of symbolic reasoning. I have approached the goal from two angles: (1) applying machine learning to symbolic reasoning tasks, such as mathematical reasoning and theorem proving in formal logic or natural language; (2) introducing symbolic components into machine learning models to make them more interpretable, verifiable, and data-efficient.
Currently, I'm working on AI that can understand and reason about mathematics. Mathematical reasoning is a critical milestone toward human-level intelligence, and it can potentially transform many important problems in science and engineering, such as solving PDEs and formal verification.
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Learning Symbolic Rules for Reasoning in Quasi-Natural Language
Kaiyu Yang and
Jia Deng
Transactions on Machine Learning Research (TMLR), 2023
arXiv
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We propose MetaQNL, a symbolic "Quasi-Natural" language that can express both formal logic and natural language. Instead of manually constructing MetaQNL rules, we propose MetaInduce: an algorithm for learning rules from data.
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Generating Natural Language Proofs with Verifier-Guided Search
Kaiyu Yang,
Jia Deng, and
Danqi Chen
Empirical Methods in Natural Language Processing (EMNLP), 2022, Oral presentation
arXiv
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We introduce NLProofS (Natural Language Proof Search) for multi-step logical reasoning in natural language. Given a hypothesis and a set of supporting facts, it generates a proof tree indicating how to derive the hypothesis from supporting facts.
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A Study of Face Obfuscation in ImageNet
Kaiyu Yang,
Jacqueline Yau,
Li Fei-Fei,
Jia Deng, and
Olga Russakovsky
International Conference on Machine Learning (ICML), 2022
arXiv
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We annotate human faces in ImageNet and obfuscate them for privacy protection. We show that face obfuscation does not hurt image classification and transfer learning.
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Strongly Incremental Constituency Parsing with Graph Neural Networks
Kaiyu Yang and
Jia Deng
Neural Information Processing Systems (NeurIPS), 2020
arXiv
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We propose a novel transition-based constituency parser named Attach-Juxtapose, inspired by how humans perform parsing.
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Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D
Ankit Goyal,
Kaiyu Yang,
Dawei Yang, and
Jia Deng
Neural Information Processing Systems (NeurIPS), 2020, Spotlight presentation
arXiv
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code
We propose Minimally Contrastive Data Collection: a novel crowdsourcing method for reducing dataset bias. And we use it to construct Rel3D—the first large-scale, human-annotated dataset for grounding spatial relations in 3D.
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Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy
Kaiyu Yang,
Klint Qinami,
Li Fei-Fei,
Jia Deng, and
Olga Russakovsky
Conference on Fairness, Accountability, and Transparency (FAT*), 2020
arXiv
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We reveal and mitigate fairness issues of ImageNet, filtering its concept vocabulary and balancing its representation of various demographic groups in images.
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Learning to Prove Theorems via Interacting with Proof Assistants
Kaiyu Yang and
Jia Deng
International Conference on Machine Learning (ICML), 2019
arXiv
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We introduce CoqGym, one of the first and largest datasets for theorem proving in proof assistants, and ASTactic, a deep learning prover generating tactics as programs.
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SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition
Kaiyu Yang,
Olga Russakovsky, and
Jia Deng
International Conference on Computer Vision (ICCV), 2019
arXiv
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We propose Adversarial Crowdsourcing to reduce dataset bias and use it to construct SpatialSense, a challenging dataset for recognizing spatial relations in images.
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Stacked Hourglass Networks for Human Pose Estimation
Alejandro Newell,
Kaiyu Yang, and
Jia Deng
European Conference on Computer Vision (ECCV), 2016
arXiv
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We introduce Stacked Hourglass Networks—one of the most popular architectures for human pose estimation, object detection, and more.
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Media
My previous work on the fairness and privacy of machine learning datasets are covered by:
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