| Reliably learning the relu in polynomial time S Goel, V Kanade, A Klivans, J Thaler Conference on Learning Theory (COLT) 2017, 2016 | 140 | 2016 |
| Learning neural networks with two nonlinear layers in polynomial time S Goel, A Klivans Conference on Learning Theory (COLT) 2019, 2017 | 103* | 2017 |
| Understanding contrastive learning requires incorporating inductive biases N Saunshi, J Ash, S Goel, D Misra, C Zhang, S Arora, S Kakade, ... International Conference on Machine Learning, 19250-19286, 2022 | 102 | 2022 |
| Transformers learn shortcuts to automata B Liu, JT Ash, S Goel, A Krishnamurthy, C Zhang arXiv preprint arXiv:2210.10749, 2022 | 98 | 2022 |
| Hidden progress in deep learning: Sgd learns parities near the computational limit B Barak, B Edelman, S Goel, S Kakade, E Malach, C Zhang Advances in Neural Information Processing Systems 35, 21750-21764, 2022 | 89 | 2022 |
| Inductive biases and variable creation in self-attention mechanisms BL Edelman, S Goel, S Kakade, C Zhang International Conference on Machine Learning, 5793-5831, 2022 | 89 | 2022 |
| Learning one convolutional layer with overlapping patches S Goel, A Klivans, R Meka International Conference on Machine Learning (ICML) 2018, 2018 | 85 | 2018 |
| Superpolynomial lower bounds for learning one-layer neural networks using gradient descent S Goel, A Gollakota, Z Jin, S Karmalkar, A Klivans International Conference on Machine Learning, 3587-3596, 2020 | 70 | 2020 |
| Gone fishing: Neural active learning with fisher embeddings J Ash, S Goel, A Krishnamurthy, S Kakade Advances in Neural Information Processing Systems 34, 8927-8939, 2021 | 57 | 2021 |
| Time/accuracy tradeoffs for learning a relu with respect to gaussian marginals S Goel, S Karmalkar, A Klivans Advances in neural information processing systems 32, 2019 | 57 | 2019 |
| Approximation schemes for relu regression I Diakonikolas, S Goel, S Karmalkar, AR Klivans, M Soltanolkotabi Conference on learning theory, 1452-1485, 2020 | 55 | 2020 |
| Statistical-query lower bounds via functional gradients S Goel, A Gollakota, A Klivans Advances in Neural Information Processing Systems 33, 2147-2158, 2020 | 54 | 2020 |
| Investigating the role of negatives in contrastive representation learning JT Ash, S Goel, A Krishnamurthy, D Misra arXiv preprint arXiv:2106.09943, 2021 | 46 | 2021 |
| Tight hardness results for training depth-2 ReLU networks S Goel, A Klivans, P Manurangsi, D Reichman arXiv preprint arXiv:2011.13550, 2020 | 34 | 2020 |
| Efficiently learning adversarially robust halfspaces with noise O Montasser, S Goel, I Diakonikolas, N Srebro International Conference on Machine Learning, 7010-7021, 2020 | 33 | 2020 |
| Quantifying perceptual distortion of adversarial examples M Jordan, N Manoj, S Goel, AG Dimakis arXiv preprint arXiv:1902.08265, 2019 | 30 | 2019 |
| Eigenvalue decay implies polynomial-time learnability for neural networks S Goel, A Klivans Advances in Neural Information Processing Systems 30, 2017 | 29 | 2017 |
| Acceleration via fractal learning rate schedules N Agarwal, S Goel, C Zhang International Conference on Machine Learning, 87-99, 2021 | 20 | 2021 |
| Improved learning of one-hidden-layer convolutional neural networks with overlaps SS Du, S Goel arXiv preprint arXiv:1805.07798, 2018 | 20 | 2018 |
| Learning ising and potts models with latent variables S Goel International Conference on Artificial Intelligence and Statistics, 3557-3566, 2020 | 19* | 2020 |
Adam KlivansProfessor of Computer Science, University of Texas at Austin在 cs.utexas.edu 的電子郵件地址已通過驗證
