Papers
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods
Kevin Liang, Guoyin Wang, Yitong Li et al.
Kernel Instrumental Variable Regression
Rahul Singh, Maneesh Sahani, Arthur Gretton
Kernelized Bayesian Softmax for Text Generation
Ning Miao, Hao Zhou, Chengqi Zhao et al.
Kernel quadrature with DPPs
Ayoub Belhadji, Rémi Bardenet, Pierre Chainais
Kernel Stein Tests for Multiple Model Comparison
Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf et al.
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration
Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona
k-Means Clustering of Lines for Big Data
Yair Marom, Dan Feldman
KNG: The K-Norm Gradient Mechanism
Matthew Reimherr, Jordan Awan
Knowledge Extraction with No Observable Data
Jaemin Yoo, Minyong Cho, Taebum Kim et al.
Landmark Ordinal Embedding
Nikhil Ghosh, Yuxin Chen, Yisong Yue
Language as an Abstraction for Hierarchical Deep Reinforcement Learning
YiDing Jiang, Shixiang (Shane) Gu, Kevin P. Murphy et al.
Large Memory Layers with Product Keys
Guillaume Lample, Alexandre Sablayrolles, Marc'Aurelio Ranzato et al.
Large Scale Adversarial Representation Learning
Jeff Donahue, Karen Simonyan
Large Scale Markov Decision Processes with Changing Rewards
Adrian Rivera Cardoso, He Wang, Huan Xu
Large-scale optimal transport map estimation using projection pursuit
Cheng Meng, Yuan Ke, Jingyi Zhang et al.
Large Scale Structure of Neural Network Loss Landscapes
Stanislav Fort, Stanislaw Jastrzebski
Latent distance estimation for random geometric graphs
Ernesto Araya Valdivia, De Castro Yohann
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series
Yulia Rubanova, Ricky T. Q. Chen, David K. Duvenaud
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization
Koen Helwegen, James Widdicombe, Lukas Geiger et al.
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Difan Zou, Ziniu Hu, Yewen Wang et al.
LCA: Loss Change Allocation for Neural Network Training
Janice Lan, Rosanne Liu, Hattie Zhou et al.
L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise
Yilun Xu, Peng Cao, Yuqing Kong et al.
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models
Yunfei Teng, Wenbo Gao, François Chalus et al.
Learnable Tree Filter for Structure-preserving Feature Transform
Lin Song, Yanwei Li, Zeming Li et al.
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
Sebastian Tschiatschek, Ahana Ghosh, Luis Haug et al.