assumption 2
Solving Non-smooth Constrained Programs with Lower Complexity than $\mathcal{O}(1/\varepsilon)$: A Primal-Dual Homotopy Smoothing Approach
Xiaohan Wei, Hao Yu, Qing Ling, Michael Neely
Neural Information Processing Systems http://nips.cc/
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Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games
Kaiqing Zhang, Zhuoran Yang, Tamer Basar
Neural Information Processing Systems http://nips.cc/
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Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima
To address this gap, we study deterministic/stochastic versions of SAM with practical configurations (i.e., constant
a9df2255ad642b923d95503b9a7958d8-Supplemental.pdf
All models discussed in the paper use ReLU non-linearity.
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Policy Gradient for Coherent Risk Measures
Aviv Tamar, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor
Neural Information Processing Systems http://nips.cc/
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Learning Partitions from Context
Modern machine learning systems are able to learn extremely complicated relations from data.
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Coherence-free Entrywise Estimation of Eigenvectors in Low-rank Signal-plus-noise Matrix Models
Throughout this paper, we assume that the entries of W are subgaussian.
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Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability Ming Xiang
Most prior work either overlooks the potential non-stationarity in the dynamics of client unavailability or requires substantial memory/computation overhead.
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