Papers
Online Dense Subgraph Discovery via Blurred-Graph Feedback
Yuko Kuroki, Atsushi Miyauchi, Junya Honda et al.
Online Learned Continual Compression with Adaptive Quantization Modules
Lucas Caccia, Eugene Belilovsky, Massimo Caccia et al.
Online Learning for Active Cache Synchronization
Andrey Kolobov, Sebastien Bubeck, Julian Zimmert
Online Learning with Dependent Stochastic Feedback Graphs
Corinna Cortes, Giulia Desalvo, Claudio Gentile et al.
Online Learning with Imperfect Hints
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar et al.
Online metric algorithms with untrusted predictions
Antonios Antoniadis, Christian Coester, Marek Elias et al.
Online mirror descent and dual averaging: keeping pace in the dynamic case
Huang Fang, Nick Harvey, Victor Portella et al.
Online Multi-Kernel Learning with Graph-Structured Feedback
Pouya M Ghari, Yanning Shen
Online Pricing with Offline Data: Phase Transition and Inverse Square Law
Jinzhi Bu, David Simchi-Levi, Yunzong Xu
On Lp-norm Robustness of Ensemble Decision Stumps and Trees
Yihan Wang, Huan Zhang, Hongge Chen et al.
On Relativistic f-Divergences
Alexia Jolicoeur-Martineau
On Second-Order Group Influence Functions for Black-Box Predictions
Samyadeep Basu, Xuchen You, Soheil Feizi
On Semi-parametric Inference for BART
Veronika Rockova
On the consistency of top-k surrogate losses
Forest Yang, Sanmi Koyejo
On the Convergence of Nesterov’s Accelerated Gradient Method in Stochastic Settings
Mahmoud Assran, Mike Rabbat
On the Expressivity of Neural Networks for Deep Reinforcement Learning
Kefan Dong, Yuping Luo, Tianhe Yu et al.
On the Generalization Benefit of Noise in Stochastic Gradient Descent
Samuel Smith, Erich Elsen, Soham De
On the Generalization Effects of Linear Transformations in Data Augmentation
Sen Wu, Hongyang Zhang, Gregory Valiant et al.
On the Global Convergence Rates of Softmax Policy Gradient Methods
Jincheng Mei, Chenjun Xiao, Csaba Szepesvari et al.
On the Global Optimality of Model-Agnostic Meta-Learning
Lingxiao Wang, Qi Cai, Zhuoran Yang et al.
On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes
Naoto Ohsaka, Tatsuya Matsuoka
On the Iteration Complexity of Hypergradient Computation
Riccardo Grazzi, Luca Franceschi, Massimiliano Pontil et al.
On the Noisy Gradient Descent that Generalizes as SGD
Jingfeng Wu, Wenqing Hu, Haoyi Xiong et al.
On the Number of Linear Regions of Convolutional Neural Networks
Huan Xiong, Lei Huang, Mengyang Yu et al.
On the Power of Compressed Sensing with Generative Models
Akshay Kamath, Eric Price, Sushrut Karmalkar