Leveraging Multilayer Telemetry to Realize AI-assisted Service Provisioning in IP over Elastic Optical Networks: (Invited Paper). - Zuqing Zhu; Bin Niu; Jiawei Kong; Shaofei Tang; Yingcong Li; Hongqiang Fang; Wei Lu 007 (OECC/PSC, 2019)

Visualize Your IP-Over-Optical Network in Realtime: A P4-Based Flexible Multilayer In-Band Network Telemetry (ML-INT) System. - Bin Niu; Jiawei Kong; Shaofei Tang; Yingcong Li; Zuqing Zhu (IEEE Access, 2019)

Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks. - Xiangyu Chang; Yingcong Li; Samet Oymak; Christos Thrampoulidis (AAAI, 2021)

Provable and Efficient Continual Representation Learning. - Yingcong Li; Mingchen Li; M. Salman Asif; Samet Oymak (CoRR, 2022)

Confident Clustering via PCA Compression Ratio and Its Application to Single-cell RNA-seq Analysis. - Yingcong Li; Chandra Sekhar Mukherjee; Jiapeng Zhang (CoRR, 2022)

Provable Pathways: Learning Multiple Tasks over Multiple Paths. - Yingcong Li; Samet Oymak (AAAI, 2023)

Stochastic Contextual Bandits with Long Horizon Rewards. - Yuzhen Qin; Yingcong Li; Fabio Pasqualetti; Maryam Fazel; Samet Oymak (AAAI, 2023)

On The Fairness of Multitask Representation Learning. - Yingcong Li; Samet Oymak (ICASSP, 2023)

Transformers as Algorithms: Generalization and Stability in In-context Learning. - Yingcong Li; Muhammed Emrullah Ildiz; Dimitris Papailiopoulos; Samet Oymak (ICML, 2023)

Dissecting Chain-of-Thought: Compositionality through In-Context Filtering and Learning. - Yingcong Li; Kartik Sreenivasan; Angeliki Giannou; Dimitris Papailiopoulos; Samet Oymak (NeurIPS, 2023)

Max-Margin Token Selection in Attention Mechanism. - Davoud Ataee Tarzanagh; Yingcong Li; Xuechen Zhang 002; Samet Oymak (NeurIPS, 2023)

Transformers as Algorithms: Generalization and Implicit Model Selection in In-context Learning. - Yingcong Li; Muhammed Emrullah Ildiz; Dimitris S. Papailiopoulos; Samet Oymak (CoRR, 2023)

Dissecting Chain-of-Thought: A Study on Compositional In-Context Learning of MLPs. - Yingcong Li; Kartik Sreenivasan; Angeliki Giannou; Dimitris S. Papailiopoulos; Samet Oymak (CoRR, 2023)

Transformers as Support Vector Machines. - Davoud Ataee Tarzanagh; Yingcong Li; Christos Thrampoulidis; Samet Oymak (CoRR, 2023)

Mechanics of Next Token Prediction with Self-Attention. - Yingcong Li; Yixiao Huang 004; Muhammed Emrullah Ildiz; Ankit Singh Rawat; Samet Oymak (AISTATS, 2024)

From Self-Attention to Markov Models: Unveiling the Dynamics of Generative Transformers. - Muhammed Emrullah Ildiz; Yixiao Huang 004; Yingcong Li; Ankit Singh Rawat; Samet Oymak (ICML, 2024)

Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond. - Yingcong Li; Ankit Singh Rawat; Samet Oymak (NeurIPS, 2024)

Provable Benefits of Task-Specific Prompts for In-context Learning. - Xiangyu Chang; Yingcong Li; Muti Kara; Samet Oymak; Amit Roy-Chowdhury 001 (AISTATS, 2025)

Gating is Weighting: Understanding Gated Linear Attention through In-context Learning. - Yingcong Li; Davoud Ataee Tarzanagh; Ankit Singh Rawat; Maryam Fazel; Samet Oymak (CoRR, 2025)

When and How Unlabeled Data Provably Improve In-Context Learning. - Yingcong Li; Xiangyu Chang; Muti Kara; Xiaofeng Liu; Amit K. Roy-Chowdhury; Samet Oymak (CoRR, 2025)

BREAD: Branched Rollouts from Expert Anchors Bridge SFT & RL for Reasoning. - Xuechen Zhang 002; Zijian Huang 015; Yingcong Li; Chenshun Ni; Jiasi Chen; Samet Oymak (CoRR, 2025)