| Inferring causal molecular networks: empirical assessment through a community-based effort SM Hill, LM Heiser, T Cokelaer, M Unger, NK Nesser, DE Carlin, Y Zhang, ... Nature methods 13 (4), 310-318, 2016 | 227 | 2016 |
| Escaping saddles with stochastic gradients H Daneshmand, J Kohler, A Lucchi, T Hofmann International Conference on Machine Learning, 1155-1164, 2018 | 136 | 2018 |
| Estimating diffusion network structures: Recovery conditions, sample complexity & soft-thresholding algorithm H Daneshmand, M Gomez-Rodriguez, L Song, B Schoelkopf International conference on machine learning, 793-801, 2014 | 135 | 2014 |
| Exponential convergence rates for batch normalization: The power of length-direction decoupling in non-convex optimization J Kohler, H Daneshmand, A Lucchi, T Hofmann, M Zhou, K Neymeyr The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 128* | 2019 |
| Local saddle point optimization: A curvature exploitation approach L Adolphs, H Daneshmand, A Lucchi, T Hofmann The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 114 | 2019 |
| Adaptive newton method for empirical risk minimization to statistical accuracy A Mokhtari, H Daneshmand, A Lucchi, T Hofmann, A Ribeiro Advances in Neural Information Processing Systems 29, 2016 | 50 | 2016 |
| Starting small-learning with adaptive sample sizes H Daneshmand, A Lucchi, T Hofmann International conference on machine learning, 1463-1471, 2016 | 49 | 2016 |
| Batch normalization provably avoids ranks collapse for randomly initialised deep networks H Daneshmand, J Kohler, F Bach, T Hofmann, A Lucchi Advances in Neural Information Processing Systems 33, 18387-18398, 2020 | 40* | 2020 |
| Estimating diffusion networks: Recovery conditions, sample complexity & soft-thresholding algorithm M Gomez-Rodriguez, L Song, H Daneshmand, B Schölkopf The Journal of Machine Learning Research 17 (1), 3092-3120, 2016 | 40* | 2016 |
| Batch normalization orthogonalizes representations in deep random networks H Daneshmand, A Joudaki, F Bach Advances in Neural Information Processing Systems 34, 4896-4906, 2021 | 22 | 2021 |
| A time-aware recommender system based on dependency network of items SM Daneshmand, A Javari, SE Abtahi, M Jalili The Computer Journal 58 (9), 1955-1966, 2015 | 21 | 2015 |
| Revisiting the role of euler numerical integration on acceleration and stability in convex optimization P Zhang, A Orvieto, H Daneshmand, T Hofmann, RS Smith International Conference on Artificial Intelligence and Statistics, 3979-3987, 2021 | 6 | 2021 |
| Transformers learn to implement preconditioned gradient descent for in-context learning K Ahn, X Cheng, H Daneshmand, S Sra arXiv preprint arXiv:2306.00297, 2023 | 4 | 2023 |
| Rethinking the Variational Interpretation of Accelerated Optimization Methods P Zhang, A Orvieto, H Daneshmand Advances in Neural Information Processing Systems 34, 14396-14406, 2021 | 2* | 2021 |
| Efficient displacement convex optimization with particle gradient descent H Daneshmand, JD Lee, C Jin arXiv preprint arXiv:2302.04753, 2023 | 1 | 2023 |
| Polynomial-time sparse measure recovery H Daneshmand, F Bach arXiv preprint arXiv:2204.07879, 2022 | 1 | 2022 |
| Optimization for Neural Networks: Quest for Theoretical Understandings H Daneshmand ETH Zurich, 2020 | 1 | 2020 |
| On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization A Joudaki, H Daneshmand, F Bach | | 2023 |
| On the impact of activation and normalization in obtaining isometric embeddings at initialization A Joudaki, H Daneshmand, F Bach arXiv preprint arXiv:2305.18399, 2023 | | 2023 |
| Mixing of Stochastic Accelerated Gradient Descent P Zhang, H Daneshmand, T Hofmann arXiv preprint arXiv:1910.14616, 2019 | | 2019 |
Thomas HofmannProfessor of Computer Science, ETH ZurichVerified email at inf.ethz.ch
