| Can local particle filters beat the curse of dimensionality? P Rebeschini, R Van Handel | 270 | 2015 |
| Decentralized Cooperative Stochastic Multi-armed Bandits D Martínez-Rubio, V Kanade, P Rebeschini NeurIPS 2019, 2018 | 118* | 2018 |
| Implicit Regularization for Optimal Sparse Recovery T Vaškevičius, V Kanade, P Rebeschini NeurIPS 2019, 2019 | 94* | 2019 |
| Fast mixing for discrete point processes P Rebeschini, A Karbasi Conference on Learning Theory, 1480-1500, 2015 | 32 | 2015 |
| The statistical complexity of early-stopped mirror descent T Vaskevicius, V Kanade, P Rebeschini Advances in Neural Information Processing Systems 33, 253-264, 2020 | 24 | 2020 |
| Graph-dependent implicit regularisation for distributed stochastic subgradient descent D Richards, P Rebeschini Journal of Machine Learning Research 21 (34), 1-44, 2020 | 24 | 2020 |
| Decentralised learning with random features and distributed gradient descent D Richards, P Rebeschini, L Rosasco International conference on machine learning, 8105-8115, 2020 | 21 | 2020 |
| Hadamard Wirtinger flow for sparse phase retrieval F Wu, P Rebeschini International Conference on Artificial Intelligence and Statistics, 982-990, 2021 | 19 | 2021 |
| Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up D Richards, P Rebeschini NeurIPS 2019, 2019 | 18 | 2019 |
| Time-independent generalization bounds for SGLD in non-convex settings T Farghly, P Rebeschini Advances in Neural Information Processing Systems 34, 19836-19846, 2021 | 17 | 2021 |
| A continuous-time mirror descent approach to sparse phase retrieval F Wu, P Rebeschini Advances in Neural Information Processing Systems 33, 20192-20203, 2020 | 16 | 2020 |
| Comparison theorems for Gibbs measures P Rebeschini, R van Handel Journal of Statistical Physics 157, 234-281, 2014 | 13 | 2014 |
| Linear convergence for natural policy gradient with log-linear policy parametrization C Alfano, P Rebeschini arXiv preprint arXiv:2209.15382, 2022 | 10 | 2022 |
| Accelerated consensus via min-sum splitting P Rebeschini, SC Tatikonda Advances in Neural Information Processing Systems 30, 2017 | 10 | 2017 |
| A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence C Alfano, R Yuan, P Rebeschini Advances in Neural Information Processing Systems 36, 2024 | 9 | 2024 |
| Exponential tail local rademacher complexity risk bounds without the bernstein condition V Kanade, P Rebeschini, T Vaskevicius arXiv preprint arXiv:2202.11461, 2022 | 8 | 2022 |
| Implicit regularization in matrix sensing via mirror descent F Wu, P Rebeschini Advances in Neural Information Processing Systems 34, 20558-20570, 2021 | 8 | 2021 |
| Phase transitions in nonlinear filtering P Rebeschini, R van Handel | 8 | 2015 |
| Distributed machine learning with sparse heterogeneous data D Richards, S Negahban, P Rebeschini Advances in Neural Information Processing Systems 34, 18008-18020, 2021 | 7 | 2021 |
| Nonlinear filtering in high dimension P Rebeschini Princeton University, 2014 | 7 | 2014 |
