Papers
Throughput-Optimal Topology Design for Cross-Silo Federated Learning
Othmane MARFOQ, CHUAN XU, Giovanni Neglia et al.
Thunder: a Fast Coordinate Selection Solver for Sparse Learning
Shaogang Ren, Weijie Zhao, Ping Li
Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits
Shinji Ito, Shuichi Hirahara, Tasuku Soma et al.
Tight last-iterate convergence rates for no-regret learning in multi-player games
Noah Golowich, Sarath Pattathil, Constantinos Daskalakis
Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model
Raphaël Berthier, Francis R. Bach, Pierre Gaillard
Time-Reversal Symmetric ODE Network
In Huh, Eunho Yang, Sung Ju Hwang et al.
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network
Lifeng Shen, Zhuocong Li, James T. Kwok
TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning
Han Cai, Chuang Gan, Ligeng Zhu et al.
Top-KAST: Top-K Always Sparse Training
Siddhant Jayakumar, Razvan Pascanu, Jack Rae et al.
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples
Samarth Sinha, Zhengli Zhao, Anirudh Goyal ALIAS PARTH GOYAL et al.
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search
Tarun Gogineni, Ziping Xu, Exequiel Punzalan et al.
Towards a Better Global Loss Landscape of GANs
Ruoyu Sun, Tiantian Fang, Alexander Schwing
Towards a Combinatorial Characterization of Bounded-Memory Learning
Alon Gonen, Shachar Lovett, Michal Moshkovitz
Towards Better Generalization of Adaptive Gradient Methods
Yingxue Zhou, Belhal Karimi, Jinxing Yu et al.
Towards Convergence Rate Analysis of Random Forests for Classification
Wei Gao, Zhi-Hua Zhou
Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts
Max Ryabinin, Anton Gusev
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Kaixiong Zhou, Xiao Huang, Yuening Li et al.
Towards Interpretable Natural Language Understanding with Explanations as Latent Variables
Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang et al.
Towards Learning Convolutions from Scratch
Behnam Neyshabur
Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples
Jay Nandy, Wynne Hsu, Mong Li Lee
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes
Yi Tian, Jian Qian, Suvrit Sra
Towards More Practical Adversarial Attacks on Graph Neural Networks
Jiaqi Ma, Shuangrui Ding, Qiaozhu Mei
Towards Neural Programming Interfaces
Zachary Brown, Nathaniel Robinson, David Wingate et al.
Towards Playing Full MOBA Games with Deep Reinforcement Learning
Deheng Ye, Guibin Chen, Wen Zhang et al.
Towards practical differentially private causal graph discovery
Lun Wang, Qi Pang, Dawn Song