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Iowa
Gradient Methods for Online DR-Submodular Maximization with Stochastic Long-Term Constraints
In this paper, we consider the problem of online monotone DR-submodular maximization subject to long-term stochastic constraints.
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization
Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang
Neural Information Processing Systems http://nips.cc/
Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition
Neural Information Processing Systems http://nips.cc/
Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter
Yi Xu, Qihang Lin, Tianbao Yang
Neural Information Processing Systems http://nips.cc/
Adaptive Negative Curvature Descent with Applications in Non-convex Optimization
Mingrui Liu, Zhe Li, Xiaoyu Wang, Jinfeng Yi, Tianbao Yang
Neural Information Processing Systems http://nips.cc/
Derivative Estimation in Random Design
Neural Information Processing Systems http://nips.cc/
Online Robust Policy Learning in the Presence of Unknown Adversaries
Aaron Havens, Zhanhong Jiang, Soumik Sarkar
Neural Information Processing Systems http://nips.cc/
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions
Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang
Neural Information Processing Systems http://nips.cc/
Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization
Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang
Neural Information Processing Systems http://nips.cc/
Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations
We present the Multi-value Rule Set (MRS) for interpretable classification with feature efficient presentations. Compared to rule sets built from single-value rules, MRS adopts a more generalized form of association rules that allows multiple values in a condition. Rules of this form are more concise than classical singlevalue rules in capturing and describing patterns in data. Our formulation also pursues a higher efficiency of feature utilization, which reduces possible cost in data collection and storage. We propose a Bayesian framework for formulating an MRS model and develop an efficient inference method for learning a maximum a posteriori, incorporating theoretically grounded bounds to iteratively reduce the search space and improve the search efficiency. Experiments on synthetic and realworld data demonstrate that MRS models have significantly smaller complexity and fewer features than baseline models while being competitive in predictive accuracy. Human evaluations show that MRS is easier to understand and use compared to other rule-based models.