Yuchen Li

I am a Ph.D. student in the Machine Learning Department at Carnegie Mellon University, where I am fortunate to be advised by Professor Andrej Risteski.

Previously, I spent four wonderful years at the University of Illinois at Urbana-Champaign, completing my bachelor's degree in Statistics & Computer Science, as well as Mathematics. I am grateful for the mentorship of Professor Jiawei Han, Professor AJ Hildebrand, and Professor Pramod Viswanath.

In the industry, I spent some exciting time at Quora and Facebook.

Email  /  CV  /  LinkedIn  /  Google Scholar  /  Twitter  /  GitHub

profile photo


I study machine learning, natural language processing, and data mining. My long-term goals include improving mathematical understanding of empirical phenomena, and developing principled approaches to modern deep learning applications.


In the following list, the asterisk symbol (*) in the author list means equal contribution or alphabetical order.

pcfg_context The Limitations of Limited Context for Constituency Parsing
Yuchen Li*, Andrej Risteski*

Association for Computational Linguistics (ACL), 2021
bibtex / slides / My Twitter post / Andrej's Twitter post

We prove that, if the context for the parsing decision at each word is unbounded & bidirectional, then the parsing model has full representational power.

On the other hand, even if the context is bounded in one direction (which is the case for various leading approaches to constituency parsing), the parsing power is quite limited for hard grammars.

complexity Complexity of Leading Digit Sequences
Xinwei He*, AJ Hildebrand*, Yuchen Li*, Yunyi Zhang*

Journal of Discrete Mathematics & Theoretical Computer Science (DMTCS), vol. 22 no. 1, Automata, Logic and Semantics, 2020
bibtex / poster

Consider the sequence of leading digits of 2^n: 1, 2, 4, 8, 1, 3, 6, ...... What is the complexity of such sequences? They are neither periodic nor random. How to quantify and distinguish the middle ground?

We prove that, the block complexity is linear for the sequence of leading digits of a^n in base b (except for some special cases). In fact, we give explicit formula for the linear coefficients in terms of a and b.

HyperMine Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity
Yu Shi*, Jiaming Shen*, Yuchen Li, Naijing Zhang, Xinwei He, Zhengzhi Lou, Qi Zhu, Matthew Walker, Myunghwan Kim, Jiawei Han

Conference on Information and Knowledge Management (CIKM), 2019

This work discovers hypernym-hyponym ("is-a" relation) pairs by an innovative combination of information from natural language and graph structure. Our system outperforms existing methods that utilize signals from text, graph, or both.

spell_correction Context-Sensitive Malicious Spelling Error Correction
Hongyu Gong, Yuchen Li, Suma Bhat, Pramod Viswanath

The Web Conference (WWW), 2019

Given a misspelled word, our algorithm generates candidates based on the surface form, represents the context as a linear space, and selects the candidate whose embedding is closest to the context subspace. The spelling correction accuracy is better than the state-of-the-art methods, improving the performance in downstream applications.

hin_motif Temporal Motifs in Heterogeneous Information Networks
Yuchen Li*, Zhengzhi Lou*, Yu Shi, Jiawei Han

Workshop on Mining and Learning with Graphs (MLG), in conjunction with KDD, 2018

This work shows the effectiveness of temporal motifs (frequent substructures) in quantifying the relationship between nodes in heterogeneous information networks, and proposes efficient algorithms for counting a family of useful motifs.

pcfg_context The Limitations of Limited Context for Constituency Parsing

2021-08 Association for Computational Linguistics (ACL) Conference

2021-07 NEC Laboratories Europe

2021-06 Approximately Correct Machine Intelligence (ACMI) Lab, CMU

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