Optimization
Diverse Ensemble Evolution: Curriculum Data-Model Marriage
Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes
Neural Information Processing Systems http://nips.cc/
Scalable Robust Matrix Factorization with Nonconvex Loss
Moreover, even the state-of-the-art RMF solver (RMF-MM) is slow and cannot utilize data sparsity. In this paper, we propose to improve robustness by using nonconvex loss functions. The resultant optimization problem is difficult.
Generalized Inverse Optimization through Online Learning
Chaosheng Dong, Yiran Chen, Bo Zeng
Neural Information Processing Systems http://nips.cc/
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
We propose a new primal-dual homotopy smoothing algorithm for a linearly constrained convex program, where neither the primal nor the dual function has to be smooth or strongly convex.
Structured Local Minima in Sparse Blind Deconvolution
Yuqian Zhang, Han-wen Kuo, John Wright
This paper focuses on the short and sparse blind deconvolu-tion problem, where the one unknown signal is short and the other one is sparsely and randomly supported.
Alternating optimization of decision trees, with application to learning sparse oblique trees
Miguel A. Carreira-Perpinan, Pooya Tavallali
Neural Information Processing Systems http://nips.cc/
Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
Pavel Dvurechenskii, Darina Dvinskikh, Alexander Gasnikov, Cesar Uribe, Angelia Nedich
Given a basis space (e.g., pixel grid) and a transportation cost function (e.g.,
Dual Policy Iteration
Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Bagnell
We also provide a general convergence analysis to support our empirical findings. Although our analysis is similar to CPI's, it has a key difference: as long as MBOC succeeds, we can provide a larger policy improvement than CPI at each iteration.
Scalable Hyperparameter Transfer Learning
Valerio Perrone, Rodolphe Jenatton, Matthias W. Seeger, Cedric Archambeau
In this work, we aim to warm-start BO in the context of hyperparameter optimization (HPO).
Greedy Hash: Towards Fast Optimization for Accurate Hash Coding in CNN
Shupeng Su, Chao Zhang, Kai Han, Yonghong Tian
Despite the considerable progress, it's still a difficult task to realize the real end-to-end training of