Publications of Rong Ge    :chronological  alphabetical  combined  bibtex listing:

Papers Published

1. Chidambaram, M; Wu, C; Cheng, Y; Ge, R, Hiding Data Helps: On the Benefits of Masking for Sparse Coding, Proceedings of Machine Learning Research, vol. 202 (January, 2023), pp. 5600-5615  [abs]
2. Zhou, M; Ge, R, Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression, Proceedings of Machine Learning Research, vol. 202 (January, 2023), pp. 42543-42573  [abs]
3. Chidambaram, M; Wang, X; Wu, C; Ge, R, Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup, Proceedings of Machine Learning Research, vol. 202 (January, 2023), pp. 5563-5599  [abs]
4. Zhao, H; Panigrahi, A; Ge, R; Arora, S, Do Transformers Parse while Predicting the Masked Word?, EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings (January, 2023), pp. 16513-16542, ISBN 9798891760608  [abs]
5. Damian, A; Nichani, E; Ge, R; Lee, JD, Smoothing the Landscape Boosts the Signal for SGD Optimal Sample Complexity for Learning Single Index Models, Advances in Neural Information Processing Systems, vol. 36 (January, 2023)  [abs]
6. Wu, C; Li, LE; Ermon, S; Haffner, P; Ge, R; Zhang, Z, The Role of Linguistic Priors in Measuring Compositional Generalization of Vision-Language Models, Proceedings of Machine Learning Research, vol. 239 (January, 2023), pp. 118-126  [abs]
7. Frandsen, A; Ge, R, Optimization landscape of Tucker decomposition, Mathematical Programming, vol. 193 no. 2 (June, 2022), pp. 687-712 [doi]  [abs]
8. Ge, R; Ma, T, On the optimization landscape of tensor decompositions, Mathematical Programming, vol. 193 no. 2 (June, 2022), pp. 713-759 [doi]  [abs]
9. Anand, K; Ge, R; Kumar, A; Panigrahi, D, Online Algorithms with Multiple Predictions, Proceedings of Machine Learning Research, vol. 162 (January, 2022), pp. 582-598  [abs]
10. Frandsen, A; Ge, R; Lee, H, Extracting Latent State Representations with Linear Dynamics from Rich Observations, Proceedings of Machine Learning Research, vol. 162 (January, 2022), pp. 6705-6725  [abs]
11. Cheng, Y; Diakonikolas, I; Ge, R; Gupta, S; Kane, DM; Soltanolkotabi, M, Outlier-Robust Sparse Estimation via Non-Convex Optimization, Advances in Neural Information Processing Systems, vol. 35 (January, 2022), ISBN 9781713871088  [abs]
12. Chidambaram, M; Wang, X; Hu, Y; Wu, C; Ge, R, TOWARDS UNDERSTANDING THE DATA DEPENDENCY OF MIXUP-STYLE TRAINING, ICLR 2022 - 10th International Conference on Learning Representations (January, 2022)  [abs]
13. Azar, Y; Ganesh, A; Ge, R; Panigrahi, D, Online Service with Delay, ACM Transactions on Algorithms, vol. 17 no. 3 (August, 2021) [doi]  [abs]
14. Jin, C; Netrapalli, P; Ge, R; Kakade, SM; Jordan, MI, On Nonconvex Optimization for Machine Learning, Journal of the ACM, vol. 68 no. 2 (March, 2021) [doi]  [abs]
15. Ge, R; Ren, Y; Wang, X; Zhou, M, Understanding Deflation Process in Over-parametrized Tensor Decomposition, Advances in Neural Information Processing Systems, vol. 2 (January, 2021), pp. 1299-1311, ISBN 9781713845393  [abs]
16. Anand, K; Ge, R; Kumar, A; Panigrahi, D, A Regression Approach to Learning-Augmented Online Algorithms, Advances in Neural Information Processing Systems, vol. 36 (January, 2021), pp. 30504-30517, ISBN 9781713845393  [abs]
17. Wang, X; Yuan, S; Wu, C; Ge, R, Guarantees for Tuning the Step Size using a Learning-to-Learn Approach, Proceedings of Machine Learning Research, vol. 139 (January, 2021), pp. 10981-10990, ISBN 9781713845065  [abs]
18. Ge, R; Lee, H; Lu, J; Risteski, A, Efficient sampling from the Bingham distribution, Proceedings of Machine Learning Research, vol. 132 (January, 2021), pp. 673-685  [abs]
19. Zhou, M; Ge, R; Jin, C, A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network, Proceedings of Machine Learning Research, vol. 134 (January, 2021), pp. 4577-4632  [abs]
20. Ge, R; Lee, H; Lu, J, Estimating normalizing constants for log-concave distributions: Algorithms and lower bounds, Proceedings of the Annual ACM Symposium on Theory of Computing (June, 2020), pp. 579-586 [doi]  [abs]
21. Wang, X; Wu, C; Lee, JD; Ma, T; Ge, R, Beyond lazy training for over-parameterized tensor decomposition, Advances in Neural Information Processing Systems, vol. 2020-December (January, 2020)  [abs]
22. Cheng, Y; Diakonikolas, I; Ge, R; Soltanolkotabi, M, High-dimensional robust mean estimation via gradient descent, 37th International Conference on Machine Learning, ICML 2020, vol. PartF168147-3 (January, 2020), pp. 1746-1756, ISBN 9781713821120  [abs]
23. Anand, K; Ge, R, Customizing ML predictions for online algorithms, 37th International Conference on Machine Learning, ICML 2020, vol. PartF168147-1 (January, 2020), pp. 280-290, ISBN 9781713821120  [abs]
24. Ge, R; Li, Z; Kuditipudi, R; Wang, X, Learning two-layer neural networks with symmetric inputs, 7th International Conference on Learning Representations, ICLR 2019 (January, 2019)  [abs]
25. Janzamin, M; Ge, R; Kossaifi, J; Anandkumar, A, Spectral learning on matrices and tensors, Foundations and Trends in Machine Learning, vol. 12 no. 5-6 (January, 2019), pp. 393-536 [doi]  [abs]
26. Kuditipudi, R; Wang, X; Lee, H; Zhang, Y; Li, Z; Hu, W; Arora, S; Ge, R, Explaining landscape connectivity of low-cost solutions for multilayer nets, Advances in Neural Information Processing Systems, vol. 32 (January, 2019)  [abs]
27. Cheng, Y; Diakonikolas, I; Ge, R, High-dimensional robust mean estimation in nearly-linear time, Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms (January, 2019), pp. 2755-2771 [doi]  [abs]
28. Frandsen, A; Ge, R, Understanding composition of word embeddings via tensor decomposition, 7th International Conference on Learning Representations, ICLR 2019 (January, 2019)  [abs]
29. Ge, R; Li, Z; Kuditipudi, R; Wang, X, Learning two-layer neural networks with symmetric inputs, 7th International Conference on Learning Representations, ICLR 2019 (January, 2019)  [abs]
30. Frandsen, A; Ge, R, Understanding composition of word embeddings via tensor decomposition, 7th International Conference on Learning Representations, ICLR 2019 (January, 2019)  [abs]
31. Ge, R; Kakade, SM; Kidambi, R; Netrapalli, P, The step decay schedule: A near optimal, geometrically decaying learning rate procedure for least squares, Advances in Neural Information Processing Systems, vol. 32 (January, 2019)  [abs]
32. Cheng, Y; Diakonikolas, I; Ge, R; Woodruff, DP, Faster Algorithms for High-Dimensional Robust Covariance Estimation, Proceedings of Machine Learning Research, vol. 99 (January, 2019), pp. 727-757  [abs]
33. Ge, R; Li, Z; Wang, W; Wang, X, Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization, Proceedings of Machine Learning Research, vol. 99 (January, 2019), pp. 1394-1448  [abs]
34. Ge, R; Jain, P; Kakade, SM; Kidambi, R; Nagaraj, DM; Netrapalli, P, Open Problem: Do Good Algorithms Necessarily Query Bad Points?, Proceedings of Machine Learning Research, vol. 99 (January, 2019), pp. 3190-3193  [abs]
35. Arora, S; Ge, R; Halpern, Y; Mimno, D; Moitra, A; Sontag, D; Wu, Y; Zhu, M, Learning topic models — Provably and efficiently, Communications of the ACM, vol. 61 no. 4 (April, 2018), pp. 85-93, Association for Computing Machinery (ACM) [doi]
36. Fazel, M; Ge, R; Kakade, SM; Mesbahi, M, Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator, 35th International Conference on Machine Learning, ICML 2018, vol. 4 (January, 2018), pp. 2385-2413, ISBN 9781510867963  [abs]
37. Arora, S; Ge, R; Neyshabur, B; Zhang, Y, Stronger generalization bounds for deep nets via a compression approach, 35th International Conference on Machine Learning, ICML 2018, vol. 1 (January, 2018), pp. 390-418, ISBN 9781510867963  [abs]
38. Jin, C; Ge, R; Liu, LT; Jordan, MI, On the local minima of the empirical risk, Advances in Neural Information Processing Systems, vol. 2018-December (January, 2018), pp. 4896-4905  [abs]
39. Ge, R; Lee, H; Risteski, A, Beyond log-concavity: Provable guarantees for sampling multi-modal distributions using simulated tempering langevin Monte Carlo, Advances in Neural Information Processing Systems, vol. 2018-December (January, 2018), pp. 7847-7856  [abs]
40. Ge, R; Lee, JD; Ma, T, Learning one-hidden-layer neural networks with landscape design, 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings (January, 2018)  [abs]
41. Ge, R; Lee, JD; Ma, T, Learning one-hidden-layer neural networks with landscape design, 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings (January, 2018)  [abs]
42. Cheng, Y; Ge, R, Non-Convex Matrix Completion Against a Semi-Random Adversary, Proceedings of Machine Learning Research, vol. 75 (January, 2018), pp. 1362-1394  [abs]
43. Arora, S; Ge, R; Ma, T; Risteski, A, Provable learning of noisy-or networks, Proceedings of the Annual ACM Symposium on Theory of Computing, vol. Part F128415 (June, 2017), pp. 1057-1066, ACM Press, ISBN 9781450345286 [doi]  [abs]
44. Azar, Y; Ganesh, A; Ge, R; Panigrahi, D, Online service with delay, Proceedings of the Annual ACM Symposium on Theory of Computing, vol. Part F128415 (June, 2017), pp. 551-563, ACM Press, ISBN 9781450345286 [doi]  [abs]
45. Anandkumar, A; Ge, R; Janzamin, M, Analyzing tensor power method dynamics in overcomplete regime, Journal of Machine Learning Research, vol. 18 (April, 2017), pp. 1-40  [abs]
46. Ge, R; Ma, T, On the optimization landscape of tensor decompositions, Advances in Neural Information Processing Systems, vol. 2017-December (January, 2017), pp. 3654-3664  [abs]
47. Arora, S; Ge, R; Liang, Y; Ma, T; Zhang, Y, Generalization and equilibrium in generative adversarial nets (GANs), 34th International Conference on Machine Learning, ICML 2017, vol. 1 (January, 2017), pp. 322-349, ISBN 9781510855144  [abs]
48. Ge, R; Jin, C; Zheng, Y, No spurious local minima in nonconvex low rank problems: A unified geometric analysis, 34th International Conference on Machine Learning, ICML 2017, vol. 3 (January, 2017), pp. 1990-2028, ISBN 9781510855144  [abs]
49. Jin, C; Ge, R; Netrapalli, P; Kakade, SM; Jordan, MI, How to escape saddle points efficiently, 34th International Conference on Machine Learning, ICML 2017, vol. 4 (January, 2017), pp. 2727-2752, ISBN 9781510855144  [abs]
50. Anandkumar, A; Ge, R, Efficient approaches for escaping higher order saddle points in non-convex optimization, Journal of Machine Learning Research, vol. 49 no. June (June, 2016), pp. 81-102  [abs]
51. Huang, Q; Ge, R; Kakade, S; Dahleh, M, Minimal Realization Problems for Hidden Markov Models, IEEE Transactions on Signal Processing, vol. 64 no. 7 (April, 2016), pp. 1896-1904, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
52. Ge, R; Jin, C; Kakade, S; Netrapalli, P; Sidford, A, Efficient algorithms for large-scale generalized eigenvector computation and canonical correlation analysis, 33rd International Conference on Machine Learning, ICML 2016, vol. 6 (January, 2016), pp. 4009-4026, ISBN 9781510829008  [abs]
53. Arora, S; Ge, R; Koehler, F; Ma, T; Moitra, A, Provable algorithms for inference in topic models, 33rd International Conference on Machine Learning, ICML 2016, vol. 6 (January, 2016), pp. 4176-4184, ISBN 9781510829008  [abs]
54. Arora, S; Ge, R; Kannan, R; Moitra, A, Computing a nonnegative matrix factorization-provably, SIAM Journal on Computing, vol. 45 no. 4 (January, 2016), pp. 1582-1611, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
55. Ge, R; Zou, J, Rich component analysis, 33rd International Conference on Machine Learning, ICML 2016, vol. 3 (January, 2016), pp. 2238-2255, ISBN 9781510829008  [abs]
56. Ge, R; Lee, JD; Ma, T, Matrix completion has no spurious local minimum, Advances in Neural Information Processing Systems (January, 2016), pp. 2981-2989  [abs]
57. Ge, R; Ma, T, Decomposing overcomplete 3rd order tensors using sum-of-squares algorithms, Leibniz International Proceedings in Informatics, LIPIcs, vol. 40 (August, 2015), pp. 829-849, ISBN 9783939897897 [doi]  [abs]
58. Ge, R; Huang, Q; Kakade, SM, Learning mixtures of gaussians in high dimensions, Proceedings of the Annual ACM Symposium on Theory of Computing, vol. 14-17-June-2015 (June, 2015), pp. 761-770, ACM Press, ISBN 9781450335362 [doi]  [abs]
59. Arora, S; Ge, R; Moitra, A; Sachdeva, S, Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders, Algorithmica, vol. 72 no. 1 (May, 2015), pp. 215-236, Springer Nature, ISSN 0178-4617 [doi]  [abs]
60. Arora, S; Ge, R; Ma, T; Moitra, A, Simple, efficient, and neural algorithms for sparse coding, Journal of Machine Learning Research, vol. 40 no. 2015 (January, 2015)  [abs]
61. Frostig, R; Ge, R; Kakade, SM; Sidford, A, Competing with the empirical risk minimizer in a single pass, Journal of Machine Learning Research, vol. 40 no. 2015 (January, 2015)  [abs]
62. Anandkumar, A; Ge, R; Janzamin, M, Learning overcomplete latent variable models through tensor methods, Journal of Machine Learning Research, vol. 40 no. 2015 (January, 2015)  [abs]
63. Ge, R; Huang, F; Jin, C; Yuan, Y, Escaping from saddle points: Online stochastic gradient for tensor decomposition, Journal of Machine Learning Research, vol. 40 no. 2015 (January, 2015)  [abs]
64. Frostig, R; Ge, R; Kakade, SM; Sidford, A, Un-regularizing: Approximate proximal point and faster stochastic algorithms for empirical risk minimization, 32nd International Conference on Machine Learning, ICML 2015, vol. 3 (January, 2015), pp. 2530-2538, ISBN 9781510810587  [abs]
65. Ge, R; Zou, J, Intersecting faces: Non-negative matrix factorization with new guarantees, 32nd International Conference on Machine Learning, ICML 2015, vol. 3 (January, 2015), pp. 2285-2293, ISBN 9781510810587  [abs]
66. Anandkumar, A; Ge, R; Hsu, D; Kakade, SM; Telgarsky, M, Tensor decompositions for learning latent variable models (A survey for ALT), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9355 (January, 2015), pp. 19-38, Springer International Publishing, ISBN 9783319244853 [doi]  [abs]
67. Anandkumar, A; Ge, R; Hsu, D; Kakade, SM; Telgarsky, M, Tensor decompositions for learning latent variable models, Journal of Machine Learning Research, vol. 15 (August, 2014), pp. 2773-2832, ISSN 1532-4435  [abs]
68. Huang, Q; Ge, R; Kakade, S; Dahleh, M, Minimal realization problem for Hidden Markov Models, 2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014 (January, 2014), pp. 4-11, IEEE [doi]  [abs]
69. Arora, S; Bhaskara, A; Ge, R; Ma, T, Provable bounds for learning some deep representations, 31st International Conference on Machine Learning, ICML 2014, vol. 1 (January, 2014), pp. 883-891  [abs]
70. Arora, S; Ge, R; Moitra, A, New algorithms for learning incoherent and overcomplete dictionaries, Journal of Machine Learning Research, vol. 35 (January, 2014), pp. 779-806, ISSN 1532-4435  [abs]
71. Anandkumar, A; Ge, R; Hsu, D; Kakade, SM, A tensor approach to learning mixed membership community models, Journal of Machine Learning Research, vol. 15 (January, 2014), pp. 2239-2312, ISSN 1532-4435  [abs]
72. Arora, S; Ge, R; Sinop, AK, Towards a better approximation for SPARSEST CUT?, Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS (December, 2013), pp. 270-279, IEEE, ISSN 0272-5428 [doi]  [abs]
73. Anandkumar, A; Ge, R; Hsu, D; Kakade, SM, A tensor spectral approach to learning mixed membership community models, Journal of Machine Learning Research, vol. 30 (January, 2013), pp. 867-881, ISSN 1532-4435  [abs]
74. Arora, S; Ge, R; Halpern, Y; Mimno, D; Moitra, A; Sontag, D; Wu, Y; Zhu, M, A practical algorithm for topic modeling with provable guarantees, 30th International Conference on Machine Learning, ICML 2013 no. PART 2 (January, 2013), pp. 939-947  [abs]
75. Arora, S; Ge, R; Moitra, A; Sachdeva, S, Provable ICA with unknown Gaussian noise, with implications for Gaussian mixtures and autoencoders, Advances in Neural Information Processing Systems, vol. 3 (December, 2012), pp. 2375-2383, ISSN 1049-5258  [abs]
76. Arora, S; Ge, R; Moitra, A, Learning topic models - Going beyond SVD, Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS (December, 2012), pp. 1-10, IEEE, ISSN 0272-5428 [doi]  [abs]
77. Arora, S; Ge, R; Sachdeva, S; Schoenebeck, G, Finding overlapping communities in social networks: Toward a rigorous approach, Proceedings of the ACM Conference on Electronic Commerce (July, 2012), pp. 37-54, ACM Press [doi]  [abs]
78. Arora, S; Ge, R; Kannan, R; Moitra, A, Computing a nonnegative matrix factorization - Provably, Proceedings of the Annual ACM Symposium on Theory of Computing (June, 2012), pp. 145-161, ACM Press, ISSN 0737-8017 [doi]  [abs]
79. Dai, D; Ge, R, Another sub-exponential algorithm for the simple stochastic game, Algorithmica (New York), vol. 61 no. 4 (December, 2011), pp. 1092-1104, Springer Nature, ISSN 0178-4617 [doi]  [abs]
80. Arora, S; Ge, R, New tools for graph coloring, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6845 LNCS (September, 2011), pp. 1-12, Springer Berlin Heidelberg, ISSN 0302-9743 [doi]  [abs]
81. Arora, S; Ge, R, New algorithms for learning in presence of errors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6755 LNCS no. PART 1 (July, 2011), pp. 403-415, Springer Berlin Heidelberg, ISSN 0302-9743 [doi]  [abs]
82. Arora, S; Barak, B; Brunnermeier, M; Ge, R, Computational complexity and information asymmetry in financial products, Communications of the ACM, vol. 54 no. 5 (May, 2011), pp. 101-107, Association for Computing Machinery (ACM), ISSN 0001-0782 [doi]  [abs]
83. Dai, D; Ge, R, New results on simple stochastic games, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5878 LNCS (December, 2009), pp. 1014-1023, Springer Berlin Heidelberg, ISSN 0302-9743 [doi]  [abs]