randomized algorithm
New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity
Pan Zhou, Xiaotong Yuan, Jiashi Feng
Neural Information Processing Systems http://nips.cc/
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Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Gradient Descent (1.00)
On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds
David P. Woodruff, CMU, dwoodruf@cs.cmu.edu "3026 Fred Zhang, UC Berkeley, z0@berkeley.edu "3026 Samson Zhou, Texas A&M University, samsonzhou@gmail.com
In this paper, we study robust algorithms for the experts problem under memory constraints.
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Lower Bounds on Adaptive Sensing for Matrix Recovery
We study lower bounds on adaptive sensing algorithms for recovering low rank matrices using linear measurements.
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Near-OptimalLowerBoundsForConvex OptimizationForAllOrdersofSmoothness
We study the complexity of optimizing highly smooth convex functions.
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Barely Random Algorithms and Collective Metrical Task Systems
Theorem 1.1 relies on two
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Near-Optimal k-Clustering in the Sliding Window Model
Clustering is an important technique for identifying structural information in large-scale data analysis, where the underlying dataset may be too large to store.
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