Large graph learning

• B. Zhou, Y. Sun, R. Babenezhad. Fast online node labeling for very large graphs. International Conference on Machine Learning,.2023. (link)

• B. Zhou, Y. Sun, R. Babanezhad, X. Guo, D. Yang, Y. Xiao. Iterative Methods via Locally Evolving Set Process. Neurips 2024.

• Y. Sun. On the scalability of large graph methods for kernel-based machine learning. Allerton Proceedings.

Optimization 

• Y. Huang, E. Luo, S. Bak, Y. Sun. On the Difficulty of Intersection Checking with Polynomial Zonotopes.  International Symposium on Automated Technology for Verification and Analysis. 2023.  (link) (arxiv)

• Z. Chen, Y. Sun. Reducing Discretization Error in the Frank-Wolfe Method.  2023.  International Conference on Artificial Intelligence and Statistics (link)

• X. Jiang, Y. Sun, M. S. Andersen, L. Vandenberghe. Minimum-rank positive semidefinite matrix completion with chordal patterns and applications to semidefinite relaxations. Applied Set-Valued Analysis and Optimization (link) (dtu)

Natural language processing and expressiveness

• X. Bai, J. Shang, Y. Sun, N. Balasubramanian.  Learning for Expressive Task-Related Sentence Representations. 2023 (arxiv)

• X. Bai, Y. Sun, N. Balasubramanian. Does RoBERTa Perform Better than BERT in Continual Learning: An Attention Sink Perspective? COLM 2024.

• X. Bai, Y. Sun, N. Balasubramanian.  Continual Learning with Global Alignment. Neurips 2024.

Frank Wolfe method

• B. Zhou, Y. Sun. Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets. ICML 2022. (arxiv)

• Y. Sun, F. Bach.  Screening for a Reweighted Penalized Conditional Gradient Method. Submitted to Open Journal of Mathematical Optimization (OJMO). (arxiv) (github)

Structured sparsity and optimization

• Y. Sun, F. Bach. Safe Screening for the Generalized Conditional Gradient Method. Technical report, arXiv:2002.09718, 2020. (pdf) (CIRM slides)

• Y. Sun, H. Jeong, J. Nutini, M. Schmidt. Are we there yet? Manifold identification of gradient-related proximal methods. AISTATS 2019 (aistats) (opt-online) (poster) (ICCOPT)

Gauges and gauge duality

• Z. Fan, H. Jeong, Y. Sun, M.  Friedlander. Atomic Decomposition via Polar Alignment: The Geometry of Structured Optimization. Foundations and Trends(r) in Optimization (2020). (pdf)

• Y. Sun, M. Friedlander. One-Shot  Atomic Detection.   IEEE CAMSAP 2019 (IEEE) (pdf)

• Y. Sun, R. Estrin, M. Friedlander. Approximate methods for phase retrieval via gauge duality (arxiv)  (SIAM CSE slides)

Encoding for distributed optimization

• C. Karakus, Y. Sun, S. Diggavi, W. Yin.  Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning. JMLR 2019. (jmlr)

• C. Karakus, Y. Sun, S. Diggavi, W. Yin. Straggler Mitigation in Distributed Optimization Through Data Encoding. NIPS 2018 (nips)

• C. Karakus, Y. Sun, S. Diggavi. "Encoded distributed optimization." ISIT 2017 (pdf)

Deep learning

• W. Wang, Y. Sun, E. Eriksson, W. Wang, V. Aggarwal. Wide Compression: Tensor Ring Nets. CVPR 2018 (cvpr) (arxiv)

• D. Zhang, Y. Sun, B. Eriksson, L. Balzono. Deep Unsupervised Clustering Using Mixture of Autoencoders. (arxiv)

Natural language processing and data mining

• Z. Yao, Y. Sun, W. Ding, N. Rao, H. Xiong. Discovery of Evolving Semantics through Dynamic Word Embedding Learning. WSDM 2018. (wsdm) (arxiv) 

• Y. Sun, N. Rao, W. Ding. A Simple Approach to Learn Polysemous Word Embeddings (arxiv)

• V. Rakesh, W. Ding, N. Rao, Y. Sun, A. Ahuja, C. Reddy. A Sparse Topic Model for Extracting Aspect-Specific Summaries from Online Reviews. WWW 2018 (www)

Chordal decomposition

• Ph.D. Thesis: Decomposition methods for semidefinite optimization

• Y. Sun and L. Vandenberghe. Decomposition methods for sparse matrix nearness problems. SIAM. J. Matrix Anal. & Appl., 36(4), 1691–1717, 2015, (pdf) (code directory), (code zip) (poster)

• Y. Sun, M. S. Andersen, and L. Vandenberghe. Decomposition in Conic Optimization with Partially Separable Structure.SIAM Journal on Optimization, 24(2):873-897, 2014. (pdf) (code directory) (code zip) (presentation) 

Belief propagation and spectrum sensing

• F. Penna, Y. Sun, L. Dolecek, and D. Cabric. Detecting and Counteracting Statistical Attacks in Cooperative Spectrum Sensing. IEEE Transactions on Signal Processing, 60(4):1806-1822, April 2012. (IEEE)

• F. Penna, Y. Sun, L. Dolecek, and D. Cabric. Joint Spectrum Sensing and Detection of Malicious Nodes via Belief Propagation, IEEE Global Telecommunications Conference, pp.1-5, 5-9 Dec. 2011. (IEEE)

LDPC

• Y. Sun and L. Dolecek. Complexity analysis of interior point methods for LP decoding, 2011 Conference Record of the Forty FifthAsilomar Conference on Systems, Signals and Computers, pp.659-663, 6-9 Nov. 2011. (IEEE)

• SMST. Yazdi, H. Cho, Y. Sun, S. Mitra, L. Dolecek. Probabilistic analysis of Gallager B faulty decoder. Communications (ICC), 2012 IEEE International Conference on, 7019-7023. (IEEE)

Optical communications

• X. Zhou and Y. Sun. US 8682182 B2: Blind carrier frequency offset detection for coherent receivers using quadrature amplitude modulation formats. Patent. (link)

• X. Zhou and Y. Sun. US 8908809 B2: Complexity reduced feed forward carrier recovery methods for M-QAM modulation formats. Patent. (link)

Adaptive filters

• Y. Sun, J. Chen and K. K. Parhi. Multi-delay block frequency domain adaptive filters with sparse partial subblock update. 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers, pp.206-209, 1-4 Nov. 2009. (IEEE)

Textbook chapters

• J. Li, Y. Sun, and L. Vandenberghe. Chapter 3: Linear and Quadratic Optimization in Electrical Engineering. In Advances and Trends in Optimization with Engineering Applications. SIAM, 2017. (siam)