NLP-Based Detection of Mathematics Subject Classification. - Yihe Dong (ICMS, 2018)

NLP and Large-Scale Information Retrieval on Mathematical Texts. - Yihe Dong (ICMS, 2018)

Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection. - Yihe Dong; Samuel B. Hopkins 001; Jerry Li 001 (NeurIPS, 2019)

Learning Sublinear-Time Indexing for Nearest Neighbor Search. - Yihe Dong; Piotr Indyk; Ilya P. Razenshteyn; Tal Wagner (CoRR, 2019)

Scalable Nearest Neighbor Search for Optimal Transport. - Yihe Dong; Piotr Indyk; Ilya P. Razenshteyn; Tal Wagner (CoRR, 2019)

SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search. - Hao Chen 030; Ilaria Chillotti; Yihe Dong; Oxana Poburinnaya; Ilya P. Razenshteyn; M. Sadegh Riazi (IACR Cryptol. ePrint Arch., 2019)

Scalable Nearest Neighbor Search for Optimal Transport. - Arturs Backurs; Yihe Dong; Piotr Indyk; Ilya P. Razenshteyn; Tal Wagner (ICML, 2020)

CoinPress: Practical Private Mean and Covariance Estimation. - Sourav Biswas 001; Yihe Dong; Gautam Kamath 001; Jonathan R. Ullman (NeurIPS, 2020)

COPT: Coordinated Optimal Transport on Graphs. - Yihe Dong; Will Sawin (NeurIPS, 2020)

Social Media Big Data-Based Research on the Influencing Factors of Insomnia and Spatiotemporal Evolution. - Yu Liu 039; Qinyao Luo; Hang Shen 002; Sida Zhuang; Chen Xu; Yihe Dong; Yukai Sun; Shaochen Wang; Hao Deng 004 (IEEE Access, 2020)

A Study of Performance of Optimal Transport. - Yihe Dong; Yu Gao 001; Richard Peng; Ilya P. Razenshteyn; Saurabh Sawlani (CoRR, 2020)

HNHN: Hypergraph Networks with Hyperedge Neurons. - Yihe Dong; Will Sawin; Yoshua Bengio (CoRR, 2020)

Attention is not all you need: pure attention loses rank doubly exponentially with depth. - Yihe Dong; Jean-Baptiste Cordonnier; Andreas Loukas (ICML, 2021)

Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts. - Rui Wang 086; Yihe Dong; Sercan Ö. Arik; Rose Yu (CoRR, 2022)

Koopman Neural Operator Forecaster for Time-series with Temporal Distributional Shifts. - Rui Wang 086; Yihe Dong; Sercan Ö. Arik; Rose Yu (ICLR, 2023)

SLM: End-to-end Feature Selection via Sparse Learnable Masks. - Yihe Dong; Sercan Ö. Arik (CoRR, 2023)

LANISTR: Multimodal Learning from Structured and Unstructured Data. - Sayna Ebrahimi; Sercan Ö. Arik; Yihe Dong; Tomas Pfister (CoRR, 2023)

COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning. - Chuizheng Meng; Yihe Dong; Sercan Ö. Arik; Yan Liu 002; Tomas Pfister (CoRR, 2023)

Learning Space Partitions for Nearest Neighbor Search. - Yihe Dong; Piotr Indyk; Ilya P. Razenshteyn; Tal Wagner (IEEE Data Eng. Bull., 2023)

Learned Feature Importance Scores for Automated Feature Engineering. - Yihe Dong; Sercan Ö. Arik; Nathanael C. Yoder; Tomas Pfister (CoRR, 2024)

AdaRank: Disagreement Based Module Rank Prediction for Low-rank Adaptation. - Yihe Dong (CoRR, 2024)

Metadata Conditioning Accelerates Language Model Pre-training. - Tianyu Gao 001; Alexander Wettig; Luxi He; Yihe Dong; Sadhika Malladi; Danqi Chen 001 (CoRR, 2025)

Attention Retrieves, MLP Memorizes: Disentangling Trainable Components in the Transformer. - Yihe Dong; Lorenzo Noci; Mikhail Khodak; Mufan Bill Li (CoRR, 2025)

Cache Me If You Can: How Many KVs Do You Need for Effective Long-Context LMs? - Adithya Bhaskar; Alexander Wettig; Tianyu Gao 001; Yihe Dong; Danqi Chen 001 (CoRR, 2025)