| A simple but tough-to-beat baseline for sentence embeddings S Arora, Y Liang, T Ma International conference on learning representations, 2017 | 988 | 2017 |
| Generalization and equilibrium in generative adversarial nets (gans) S Arora, R Ge, Y Liang, T Ma, Y Zhang International Conference on Machine Learning, 224-232, 2017 | 515 | 2017 |
| Learning and generalization in overparameterized neural networks, going beyond two layers Z Allen-Zhu, Y Li, Y Liang Advances in neural information processing systems, 2019 | 419 | 2019 |
| Learning overparameterized neural networks via stochastic gradient descent on structured data Y Li, Y Liang arXiv preprint arXiv:1808.01204, 2018 | 395 | 2018 |
| A latent variable model approach to pmi-based word embeddings S Arora, Y Li, Y Liang, T Ma, A Risteski Transactions of the Association for Computational Linguistics 4, 385-399, 2016 | 289 | 2016 |
| Scalable kernel methods via doubly stochastic gradients B Dai, B Xie, N He, Y Liang, A Raj, MF Balcan, L Song arXiv preprint arXiv:1407.5599, 2014 | 217 | 2014 |
| Linear algebraic structure of word senses, with applications to polysemy S Arora, Y Li, Y Liang, T Ma, A Risteski Transactions of the Association for Computational Linguistics 6, 483-495, 2018 | 164 | 2018 |
| Distributed k-means and k-median clustering on general topologies MF Balcan, S Ehrlich, Y Liang arXiv preprint arXiv:1306.0604, 2013 | 153 | 2013 |
| Robust hierarchical clustering MF Balcan, Y Liang, P Gupta The Journal of Machine Learning Research 15 (1), 3831-3871, 2014 | 138 | 2014 |
| Improved distributed principal component analysis MF Balcan, V Kanchanapally, Y Liang, D Woodruff arXiv preprint arXiv:1408.5823, 2014 | 132 | 2014 |
| Improved distributed principal component analysis MF Balcan, V Kanchanapally, Y Liang, D Woodruff arXiv preprint arXiv:1408.5823, 2014 | 132 | 2014 |
| Diverse neural network learns true target functions B Xie, Y Liang, L Song Artificial Intelligence and Statistics, 1216-1224, 2017 | 115* | 2017 |
| Clustering under perturbation resilience MF Balcan, Y Liang SIAM Journal on Computing 45 (1), 102-155, 2016 | 100 | 2016 |
| Clustering under perturbation resilience MF Balcan, Y Liang SIAM Journal on Computing 45 (1), 102-155, 2016 | 100 | 2016 |
| Influence function learning in information diffusion networks N Du, Y Liang, M Balcan, L Song International Conference on Machine Learning, 2016-2024, 2014 | 87 | 2014 |
| Domain adapted word embeddings for improved sentiment classification PK Sarma, Y Liang, WA Sethares arXiv preprint arXiv:1805.04576, 2018 | 58 | 2018 |
| Random walks on context spaces: Towards an explanation of the mysteries of semantic word embeddings S Arora, Y Li, Y Liang, T Ma, A Risteski arXiv preprint arXiv:1502.03520, 385-399, 2015 | 56 | 2015 |
| A la carte embedding: Cheap but effective induction of semantic feature vectors M Khodak, N Saunshi, Y Liang, T Ma, B Stewart, S Arora arXiv preprint arXiv:1805.05388, 2018 | 55 | 2018 |
| Why are deep nets reversible: A simple theory, with implications for training S Arora, Y Liang, T Ma arXiv preprint arXiv:1511.05653, 2015 | 53 | 2015 |
| Distributed PCA and k-means clustering Y Liang, MF Balcan, V Kanchanapally The Big Learning Workshop at NIPS 2013, 2013 | 46 | 2013 |
Maria-Florina BalcanProfessor, School of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
