Near Optimal Methods for Minimizing Convex Functions with Lipschitz -th Derivatives A Gasnikov, P Dvurechensky, E Gorbunov, E Vorontsova, ...
Conference on Learning Theory, 1392-1393, 2019
80 2019 Near-optimal method for highly smooth convex optimization S Bubeck, Q Jiang, YT Lee, Y Li, A Sidford
Conference on Learning Theory, 492-507, 2019
79 2019 Complexity of highly parallel non-smooth convex optimization S Bubeck, Q Jiang, YT Lee, Y Li, A Sidford
Advances in Neural Information Processing Systems 32, 2019
61 2019 Subgradient descent learns orthogonal dictionaries Y Bai, Q Jiang, J Sun
7th International Conference on Learning Representations, ICLR 2019, 2018
58 2018 Acceleration with a ball optimization oracle Y Carmon, A Jambulapati, Q Jiang, Y Jin, YT Lee, A Sidford, K Tian
Advances in Neural Information Processing Systems 33, 19052-19063, 2020
42 2020 Mirror Langevin Monte Carlo: the Case Under Isoperimetry Q Jiang
Advances in Neural Information Processing Systems 34, 715-725, 2021
18 2021 Optimizing black-box metrics with adaptive surrogates Q Jiang, O Adigun, H Narasimhan, MM Fard, M Gupta
International Conference on Machine Learning, 4784-4793, 2020
14 2020 Learning the Truth From Only One Side of the Story H Jiang, Q Jiang, A Pacchiano
International Conference on Artificial Intelligence and Statistics, 2413-2421, 2021
5 2021 On the Dissipation of Ideal Hamiltonian Monte Carlo Sampler Q Jiang
Stat 12, e629, 2022
3 2022 Near-Isometric Properties of Kronecker-Structured Random Tensor Embeddings Q Jiang
Advances in Neural Information Processing Systems 35, 10191-10202, 2022
1 2022 From Estimation to Sampling for Bayesian Linear Regression with Spike-and-Slab Prior Q Jiang
arXiv preprint arXiv:2307.05558, 2023
2023 Fourier Interpolation with Magnitude Only Q Jiang
Fourteenth International Conference on Sampling Theory and Applications, 2023
2023 Randomized Alternating Direction Methods for Efficient Distributed Optimization E Candes, Q Jiang, M Pilanci