| 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 | 183 | 2016 |
| 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 | 116 | 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 | 76* | 2019 |
| Escaping saddles with stochastic gradients H Daneshmand, J Kohler, A Lucchi, T Hofmann International Conference on Machine Learning, 1155-1164, 2018 | 74 | 2018 |
| 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 | 67 | 2019 |
| Starting small-learning with adaptive sample sizes H Daneshmand, A Lucchi, T Hofmann International conference on machine learning, 1463-1471, 2016 | 39 | 2016 |
| Adaptive Newton method for empirical risk minimization to statistical accuracy A Mokhtari, A Ribeiro arXiv preprint arXiv:1605.07659, 2016 | 37* | 2016 |
| 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 | 33 | 2016 |
| 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 | 18 | 2015 |
| Batch normalization provably avoids rank collapse for randomly initialised deep networks H Daneshmand, J Kohler, F Bach, T Hofmann, A Lucchi arXiv preprint arXiv:2003.01652, 2020 | 9* | 2020 |
| Rethinking the Variational Interpretation of Nesterov's Accelerated Method P Zhang, A Orvieto, H Daneshmand arXiv preprint arXiv:2107.05040, 2021 | | 2021 |
| Batch Normalization Orthogonalizes Representations in Deep Random Networks H Daneshmand, A Joudaki, F Bach arXiv preprint arXiv:2106.03970, 2021 | | 2021 |
| 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 | | 2021 |
| Optimization for Neural Networks: Quest for Theoretical Understandings H Daneshmand ETH Zurich, 2020 | | 2020 |
| Mixing of Stochastic Accelerated Gradient Descent P Zhang, H Daneshmand, T Hofmann arXiv preprint arXiv:1910.14616, 2019 | | 2019 |
| Accelerated Dual Learning by Homotopic Initialization H Daneshmand, H Hassani, T Hofmann arXiv preprint arXiv:1706.03958, 2017 | | 2017 |
Thomas HofmannProfessor of Computer Science, ETH ZurichVerified email at inf.ethz.ch
