Papers
Iterative Amortized Inference
Joe Marino, Yisong Yue, Stephan Mandt
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning
Dhruv Malik, Malayandi Palaniappan, Jaime Fisac et al.
Learning Adversarially Fair and Transferable Representations
David Madras, Elliot Creager, Toniann Pitassi et al.
Orthogonal Machine Learning: Power and Limitations
Lester Mackey, Vasilis Syrgkanis, Ilias Zadik
Approximate message passing for amplitude based optimization
Junjie Ma, Ji Xu, Arian Maleki
Dimensionality-Driven Learning with Noisy Labels
Xingjun Ma, Yisen Wang, Michael E. Houle et al.
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion
Cong Ma, Kaizheng Wang, Yuejie Chi et al.
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers
Yao Ma, Alexander Olshevsky, Csaba Szepesvari et al.
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning
Siyuan Ma, Raef Bassily, Mikhail Belkin
Celer: a Fast Solver for the Lasso with Dual Extrapolation
Mathurin MASSIAS, Alexandre Gramfort, Joseph Salmon
Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design
Wenlong Lyu, Fan Yang, Changhao Yan et al.
Competitive Caching with Machine Learned Advice
Thodoris Lykouris, Sergei Vassilvtiskii
End-to-end Active Object Tracking via Reinforcement Learning
Wenhan Luo, Peng Sun, Fangwei Zhong et al.
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
Yiping Lu, Aoxiao Zhong, Quanzheng Li et al.
Structured Variationally Auto-encoded Optimization
Xiaoyu Lu, Javier Gonzalez, Zhenwen Dai et al.
Accelerating Greedy Coordinate Descent Methods
Haihao Lu, Robert Freund, Vahab Mirrokni
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference
Hao Lu, Yuan Cao, Zhuoran Yang et al.
Spectrally Approximating Large Graphs with Smaller Graphs
Andreas Loukas, Pierre Vandergheynst
Constraining the Dynamics of Deep Probabilistic Models
Marco Lorenzi, Maurizio Filippone
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
Miles Lopes, Shusen Wang, Michael Mahoney
PDE-Net: Learning PDEs from Data
Zichao Long, Yiping Lu, Xianzhong Ma et al.
On Matching Pursuit and Coordinate Descent
Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy et al.
Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate
Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen et al.
Fast Variance Reduction Method with Stochastic Batch Size
Xuanqing Liu, Cho-Jui Hsieh
Open Category Detection with PAC Guarantees
Si Liu, Risheek Garrepalli, Thomas Dietterich et al.