Papers
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.
A Reductions Approach to Fair Classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik et al.
A Robust Approach to Sequential Information Theoretic Planning
Sue Zheng, Jason Pacheco, John Fisher
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
Jingyi Xu, Zilu Zhang, Tal Friedman et al.
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates
Kaiwen Zhou, Fanhua Shang, James Cheng
A Spectral Approach to Gradient Estimation for Implicit Distributions
Jiaxin Shi, Shengyang Sun, Jun Zhu
A Spline Theory of Deep Learning
Randall Balestriero, baraniuk
Asynchronous Byzantine Machine Learning (the case of SGD)
Georgios Damaskinos, El-Mahdi El-Mhamdi, Rachid Guerraoui et al.
Asynchronous Decentralized Parallel Stochastic Gradient Descent
Xiangru Lian, Wei Zhang, Ce Zhang et al.
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
Umut Simsekli, Cagatay Yildiz, Than Huy Nguyen et al.
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations
Weili Nie, Yang Zhang, Ankit Patel