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Qijia Jiang
Qijia Jiang
Lawrence Berkeley National Lab
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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
792019
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
772019
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
592019
Subgradient descent learns orthogonal dictionaries
Y Bai, Q Jiang, J Sun
7th International Conference on Learning Representations, ICLR 2019, 2018
572018
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
412020
Mirror Langevin Monte Carlo: the Case Under Isoperimetry
Q Jiang
Advances in Neural Information Processing Systems 34, 715-725, 2021
172021
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
132020
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
52021
On the Dissipation of Ideal Hamiltonian Monte Carlo Sampler
Q Jiang
Stat 12, e629, 2022
32022
Near-Isometric Properties of Kronecker-Structured Random Tensor Embeddings
Q Jiang
Advances in Neural Information Processing Systems 35, 10191-10202, 2022
12022
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
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