Statistical Learning
Gradient Descent Can Take Exponential Time to Escape Saddle Points
Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Aarti Singh, Barnabas Poczos
Neural Information Processing Systems http://nips.cc/
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, Ji Liu
CNTK, are built in a centralized fashion.
Overcoming Catastrophic Forgetting by Incremental Moment Matching
Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, Byoung-Tak Zhang
Neural Information Processing Systems http://nips.cc/
Generative Local Metric Learning for Kernel Regression
Yung-Kyun Noh, Masashi Sugiyama, Kee-Eung Kim, Frank Park, Daniel D. Lee
Neural Information Processing Systems http://nips.cc/
Alternating Estimation for Structured High-Dimensional Multi-Response Models
We consider the problem of learning high-dimensional multi-response linear models with structured parameters.
Protein Interface Prediction using Graph Convolutional Networks
Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur
Neural Information Processing Systems http://nips.cc/
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, Julien Stainer
We study the resilience to Byzantine failures of distributed implementations of Stochastic Gradient Descent (SGD). So far, distributed machine learning frameworks have largely ignored the possibility of failures, especially arbitrary (i.e., Byzantine) ones.
On the Consistency of Quick Shift
Quick Shift is a popular mode-seeking and clustering algorithm.
Unbounded cache model for online language modeling with open vocabulary
Edouard Grave, Moustapha M. Cisse, Armand Joulin
Neural Information Processing Systems http://nips.cc/
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
Privacy preserving is an important issue in learning.