Statistical Learning
Simple strategies for recovering inner products from coarsely quantized random projections
The idea of achieving further data compression by means of subsequent scalar quantization of the projected data has been considered for a while.
A Learning Error Analysis for Structured Prediction with Approximate Inference
Yuanbin Wu, Man Lan, Shiliang Sun, Qi Zhang, Xuanjing Huang
Neural Information Processing Systems http://nips.cc/
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization
Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang
In particular, it remains an open question how to quantify the improvement in ADMM's theoretical convergence by using adaptive penalty parameters. Of course, the answer to this question depends on the adaptive scheme being used.
Hierarchical Clustering Beyond the Worst-Case
Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn
Finally, we report empirical evaluation on synthetic and real-world data showing that our proposed SVD-based method does indeed achieve a better cost than other widely-used heurstics and also results in a better classification accuracy when the underlying problem was that of multi-class classification.
Consistent Robust Regression
In this work we present such an estimator, called CRR .
Multitask Spectral Learning of Weighted Automata
Guillaume Rabusseau, Borja Balle, Joelle Pineau
Neural Information Processing Systems http://nips.cc/
Deep Subspace Clustering Networks
Pan Ji, Tong Zhang, Hongdong Li, Mathieu Salzmann, Ian Reid
Our key idea is to introduce a novel self-expressive layer between the encoder and the decoder to mimic the "self-expressiveness" property that has proven effective in traditional subspace clustering.
Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding
Wenbing Huang, Mehrtash Harandi, Tong Zhang, Lijie Fan, Fuchun Sun, Junzhou Huang
Linear Dynamical Systems (LDSs) are fundamental tools for modeling spatiotemporal data in various disciplines.
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems
Celestine Dünner, Thomas Parnell, Martin Jaggi
We propose a generic algorithmic building block to accelerate training of machine learning models on heterogeneous compute systems. Our scheme allows to efficiently employ compute accelerators such as GPUs and FPGAs for the training of large-scale machine learning models, when the training data exceeds their memory capacity. Also, it provides adaptivity to any system's memory hierarchy in terms of size and processing speed. Our technique is built upon novel theoretical insights regarding primal-dual coordinate methods, and uses duality gap information to dynamically decide which part of the data should be made available for fast processing. To illustrate the power of our approach we demonstrate its performance for training of generalized linear models on a large-scale dataset exceeding the memory size of a modern GPU, showing an order-of-magnitude speedup over existing approaches.