Ma, Tengyu

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 2,676

A Latent Variable Model Approach to PMI-based Word Embeddings

Arora, S., Li, Y., Liang, Y., Ma, T., & Risteski, A. (2016) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 286

Linear Algebraic Structure of Word Senses, with Applications to Polysemy

Arora, S., Li, Y., Liang, Y., Ma, T., & Risteski, A. (2018) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 186

An Explanation of In-context Learning as Implicit Bayesian Inference

Xie, S. M., Raghunathan, A., Liang, P., & Ma, T. (2021) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 138

A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors

Khodak, M., Saunshi, N., Liang, Y., Ma, T., Stewart, B., & Arora, S. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 111

Larger language models do in-context learning differently

Wei, J., Wei, J., Tay, Y., Tran, D., Webson, A., Lu, Y., Chen, X., Liu, H., Huang, D., Zhou, D., et al. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 109

What learning algorithm is in-context learning? Investigations with linear models

Akyürek, E., Schuurmans, D., Andreas, J., Ma, T., & Zhou, D. (2022) · arXiv

Domain Adaptation and Few-Shot LearningNeural Networks and Applications

Cited by 86

Self-supervised Learning is More Robust to Dataset Imbalance

Liu, H., HaoChen, J. Z., Gaidon, A., & Ma, T. (2021) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 84

Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss

HaoChen, J. Z., Wei, C., Gaidon, A., & Ma, T. (2021) · arXiv

Domain Adaptation and Few-Shot LearningStochastic Gradient Optimization Techniques

Cited by 53

Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning

Wei, C., Xie, S. M., & Ma, T. (2021) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 32

Large language models as tool makers

Cai, T., Wang, X., Ma, T., Chen, X., & Zhou, D. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 28

Symbol tuning improves in-context learning in language models

Wei, J., Hou, L., Lampinen, A., Chen, X., Huang, D., Tay, Y., Chen, X., Lu, Y., Zhou, D., Ma, T., et al. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 1

Non-Asymptotic Length Generalization

Chen, T., Ma, T., & Li, Z. (2025) · arXiv

semigroups and automata theoryMachine Learning and Algorithms

Cited by 0