Kaiyu Yang

I am a postdoctoral researcher at Caltech in the Computing + Mathematical Sciences (CMS) Department , working with Anima Anandkumar. I received my Ph.D. from Princeton University, where I was advised by Jia Deng and also worked with Olga Russakovsky and Danqi Chen.

杨凯峪 / kaiyuy [MASK] caltech [MASK] edu / CV / Bio / Google Scholar / Github

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.

	Learning Symbolic Rules for Reasoning in Quasi-Natural Language Kaiyu Yang and Jia Deng Transactions on Machine Learning Research (TMLR), 2023 arXiv / code 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.
	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 / code / slides / poster 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.
	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 / code / slides / talk / poster / project / media 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.
	Strongly Incremental Constituency Parsing with Graph Neural Networks Kaiyu Yang and Jia Deng Neural Information Processing Systems (NeurIPS), 2020 arXiv / code / slides / talk / poster We propose a novel transition-based constituency parser named Attach-Juxtapose, inspired by how humans perform parsing.
	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 / 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.
	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 / slides / talk / blog / media We reveal and mitigate fairness issues of ImageNet, filtering its concept vocabulary and balancing its representation of various demographic groups in images.
	Learning to Prove Theorems via Interacting with Proof Assistants Kaiyu Yang and Jia Deng International Conference on Machine Learning (ICML), 2019 arXiv / code / slides / poster 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.
	SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition Kaiyu Yang, Olga Russakovsky, and Jia Deng International Conference on Computer Vision (ICCV), 2019 arXiv / code / poster We propose Adversarial Crowdsourcing to reduce dataset bias and use it to construct SpatialSense, a challenging dataset for recognizing spatial relations in images.
	Stacked Hourglass Networks for Human Pose Estimation Alejandro Newell, Kaiyu Yang, and Jia Deng European Conference on Computer Vision (ECCV), 2016 arXiv / code We introduce Stacked Hourglass Networks—one of the most popular architectures for human pose estimation, object detection, and more.