Papers
On the Local Hessian in Back-propagation
Huishuai Zhang, Wei Chen, Tie-Yan Liu
On the Local Minima of the Empirical Risk
Chi Jin, Lydia T. Liu, Rong Ge et al.
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
Rad Niazadeh, Tim Roughgarden, Joshua Wang
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
Kevin Scaman, Francis Bach, Sebastien Bubeck et al.
Optimal Subsampling with Influence Functions
Daniel Ting, Eric Brochu
Optimistic optimization of a Brownian
Jean-Bastien Grill, Michal Valko, Remi Munos
Optimization for Approximate Submodularity
Yaron Singer, Avinatan Hassidim
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates
Yining Wang, Sivaraman Balakrishnan, Aarti Singh
Optimization over Continuous and Multi-dimensional Decisions with Observational Data
Dimitris Bertsimas, Christopher McCord
Orthogonally Decoupled Variational Gaussian Processes
Hugh Salimbeni, Ching-An Cheng, Byron Boots et al.
Out-of-Distribution Detection using Multiple Semantic Label Representations
Gabi Shalev, Yossi Adi, Joseph Keshet
Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering
Medhini Narasimhan, Svetlana Lazebnik, Alexander Schwing
Overcoming Language Priors in Visual Question Answering with Adversarial Regularization
Sainandan Ramakrishnan, Aishwarya Agrawal, Stefan Lee
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate
Mikhail Belkin, Daniel J. Hsu, Partha Mitra
Overlapping Clustering Models, and One (class) SVM to Bind Them All
Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti
PAC-Bayes bounds for stable algorithms with instance-dependent priors
Omar Rivasplata, Emilio Parrado-Hernandez, John S Shawe-Taylor et al.
PAC-Bayes Tree: weighted subtrees with guarantees
Tin D Nguyen, Samory Kpotufe
PacGAN: The power of two samples in generative adversarial networks
Zinan Lin, Ashish Khetan, Giulia Fanti et al.
PAC-learning in the presence of adversaries
Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal
Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks
Grant Rotskoff, Eric Vanden-Eijnden
Paraphrasing Complex Network: Network Compression via Factor Transfer
Jangho Kim, Seonguk Park, Nojun Kwak
Parsimonious Bayesian deep networks
Mingyuan Zhou
Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning
Xing Yan, Weizhong Zhang, Lin Ma et al.
Partially-Supervised Image Captioning
Peter Anderson, Stephen Gould, Mark Johnson
PCA of high dimensional random walks with comparison to neural network training
Joseph Antognini, Jascha Sohl-Dickstein