Papers
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Jonathan Uesato, Brendan O’Donoghue, Pushmeet Kohli et al.
Adversarial Time-to-Event Modeling
Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li et al.
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
Beilun Wang, Arshdeep Sekhon, Yanjun Qi
A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
Adam Roberts, Jesse Engel, Colin Raffel et al.
Alternating Randomized Block Coordinate Descent
Jelena Diakonikolas, Lorenzo Orecchia
An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method
Li Shen, Peng Sun, Yitong Wang et al.
An Alternative View: When Does SGD Escape Local Minima?
Bobby Kleinberg, Yuanzhi Li, Yang Yuan
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
Hideaki Imamura, Issei Sato, Masashi Sugiyama
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples
Yizhen Wang, Somesh Jha, Kamalika Chaudhuri
Analyzing Uncertainty in Neural Machine Translation
Myle Ott, Michael Auli, David Grangier et al.
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning
Dhruv Malik, Malayandi Palaniappan, Jaime Fisac et al.
An Efficient Semismooth Newton based Algorithm for Convex Clustering
Yancheng Yuan, Defeng Sun, Kim-Chuan Toh
An Estimation and Analysis Framework for the Rasch Model
Andrew Lan, Mung Chiang, Christoph Studer
An Inference-Based Policy Gradient Method for Learning Options
Matthew Smith, Herke Hoof, Joelle Pineau
An Iterative, Sketching-based Framework for Ridge Regression
Agniva Chowdhury, Jiasen Yang, Petros Drineas
Anonymous Walk Embeddings
Sergey Ivanov, Evgeny Burnaev
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
Shuaiwen Wang, Wenda Zhou, Haihao Lu et al.
Approximate message passing for amplitude based optimization
Junjie Ma, Ji Xu, Arian Maleki
Approximation Algorithms for Cascading Prediction Models
Matthew Streeter
Approximation Guarantees for Adaptive Sampling
Eric Balkanski, Yaron Singer
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
Xiao Zhang, Lingxiao Wang, Yaodong Yu et al.
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
Akifumi Okuno, Tetsuya Hada, Hidetoshi Shimodaira
A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization
Robin Vogel, Aurélien Bellet, Stéphan Clémençon
A Progressive Batching L-BFGS Method for Machine Learning
Raghu Bollapragada, Jorge Nocedal, Dheevatsa Mudigere et al.