Papers
Adaptive Regret of Convex and Smooth Functions
Lijun Zhang, Tie-Yan Liu, Zhi-Hua Zhou
Adaptive Scale-Invariant Online Algorithms for Learning Linear Models
Michal Kempka, Wojciech Kotlowski, Manfred K. Warmuth
Adaptive Sensor Placement for Continuous Spaces
James Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths et al.
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari et al.
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment
Chen Huang, Shuangfei Zhai, Walter Talbott et al.
A Deep Reinforcement Learning Perspective on Internet Congestion Control
Nathan Jay, Noga Rotman, Brighten Godfrey et al.
Adjustment Criteria for Generalizing Experimental Findings
Juan Correa, Jin Tian, Elias Bareinboim
Adversarial Attacks on Node Embeddings via Graph Poisoning
Aleksandar Bojchevski, Stephan Günnemann
Adversarial camera stickers: A physical camera-based attack on deep learning systems
Juncheng Li, Frank Schmidt, Zico Kolter
Adversarial Examples Are a Natural Consequence of Test Error in Noise
Justin Gilmer, Nicolas Ford, Nicholas Carlini et al.
Adversarial examples from computational constraints
Sebastien Bubeck, Yin Tat Lee, Eric Price et al.
Adversarial Generation of Time-Frequency Features with application in audio synthesis
Andrés Marafioti, Nathanaël Perraudin, Nicki Holighaus et al.
Adversarially Learned Representations for Information Obfuscation and Inference
Martin Bertran, Natalia Martinez, Afroditi Papadaki et al.
Adversarial Online Learning with noise
Alon Resler, Yishay Mansour
A Dynamical Systems Perspective on Nesterov Acceleration
Michael Muehlebach, Michael Jordan
A Framework for Bayesian Optimization in Embedded Subspaces
Amin Nayebi, Alexander Munteanu, Matthias Poloczek
A fully differentiable beam search decoder
Ronan Collobert, Awni Hannun, Gabriel Synnaeve
Agnostic Federated Learning
Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh
A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization
Yucheng Chen, Matus Telgarsky, Chao Zhang et al.
A Kernel Perspective for Regularizing Deep Neural Networks
Alberto Bietti, Grégoire Mialon, Dexiong Chen et al.
A Kernel Theory of Modern Data Augmentation
Tri Dao, Albert Gu, Alexander Ratner et al.
A Large-Scale Study on Regularization and Normalization in GANs
Karol Kurach, Mario Lučić, Xiaohua Zhai et al.
Almost surely constrained convex optimization
Olivier Fercoq, Ahmet Alacaoglu, Ion Necoara et al.
Almost Unsupervised Text to Speech and Automatic Speech Recognition
Yi Ren, Xu Tan, Tao Qin et al.
Alternating Minimizations Converge to Second-Order Optimal Solutions
Qiuwei Li, Zhihui Zhu, Gongguo Tang