Papers
AUCμ: A Performance Metric for Multi-Class Machine Learning Models
Ross Kleiman, David Page
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces
Johannes Kirschner, Mojmir Mutny, Nicole Hiller et al.
CompILE: Compositional Imitation Learning and Execution
Thomas Kipf, Yujia Li, Hanjun Dai et al.
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables
Friso Kingma, Pieter Abbeel, Jonathan Ho
Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension
Jisu Kim, Jaehyeok Shin, Alessandro Rinaldo et al.
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model
Gi-Soo Kim, Myunghee Cho Paik
Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty
Youngjin Kim, Wontae Nam, Hyunwoo Kim et al.
FloWaveNet : A Generative Flow for Raw Audio
Sungwon Kim, Sang-Gil Lee, Jongyoon Song et al.
EMI: Exploration with Mutual Information
Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong et al.
Geometry Aware Convolutional Filters for Omnidirectional Images Representation
Renata Khasanova, Pascal Frossard
Collaborative Evolutionary Reinforcement Learning
Shauharda Khadka, Somdeb Majumdar, Tarek Nassar et al.
CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network
Tom Kenter, Vincent Wan, Chun-An Chan et al.
Adaptive Scale-Invariant Online Algorithms for Learning Linear Models
Michal Kempka, Wojciech Kotlowski, Manfred K. Warmuth
Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity
Ehsan Kazemi, Marko Mitrovic, Morteza Zadimoghaddam et al.
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
Yigitcan Kaya, Sanghyun Hong, Tudor Dumitras
Robust Estimation of Tree Structured Gaussian Graphical Models
Ashish Katiyar, Jessica Hoffmann, Constantine Caramanis
Processing Megapixel Images with Deep Attention-Sampling Models
Angelos Katharopoulos, Francois Fleuret
Neural Inverse Knitting: From Images to Manufacturing Instructions
Alexandre Kaspar, Tae-Hyun Oh, Liane Makatura et al.
Riemannian adaptive stochastic gradient algorithms on matrix manifolds
Hiroyuki Kasai, Pratik Jawanpuria, Bamdev Mishra
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian Stich et al.
Policy Consolidation for Continual Reinforcement Learning
Christos Kaplanis, Murray Shanahan, Claudia Clopath
Differentially Private Learning of Geometric Concepts
Haim Kaplan, Yishay Mansour, Yossi Matias et al.
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang et al.
Trainable Decoding of Sets of Sequences for Neural Sequence Models
Ashwin Kalyan, Peter Anderson, Stefan Lee et al.