Papers
Forget-free Continual Learning with Winning Subnetworks
Haeyong Kang, Rusty John Lloyd Mina, Sultan Rizky Hikmawan Madjid et al.
For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria
Scott Emmons, Caspar Oesterheld, Andrew Critch et al.
Forward Operator Estimation in Generative Models with Kernel Transfer Operators
Zhichun Huang, Rudrasis Chakraborty, Vikas Singh
Fourier Learning with Cyclical Data
Yingxiang Yang, Zhihan Xiong, Tianyi Liu et al.
Framework for Evaluating Faithfulness of Local Explanations
Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz
FriendlyCore: Practical Differentially Private Aggregation
Eliad Tsfadia, Edith Cohen, Haim Kaplan et al.
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
Krzysztof Choromanski, Han Lin, Haoxian Chen et al.
From data to functa: Your data point is a function and you can treat it like one
Emilien Dupont, Hyunjik Kim, S. M. Ali Eslami et al.
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
Daniil Tiapkin, Denis Belomestny, Eric Moulines et al.
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model
Heesun Bae, Seungjae Shin, Byeonghu Na et al.
Frustratingly Easy Transferability Estimation
Long-Kai Huang, Junzhou Huang, Yu Rong et al.
Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis
Sho Sonoda, Isao Ishikawa, Masahiro Ikeda
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet et al.
Functional Output Regression with Infimal Convolution: Exploring the Huber and $ε$-insensitive Losses
Alex Lambert, Dimitri Bouche, Zoltan Szabo et al.
Function-space Inference with Sparse Implicit Processes
Simon Rodrı́guez-Santana, Bryan Zaldivar, Daniel Hernandez-Lobato
G$^2$CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters
Mingjie Li, Xiaojun Guo, Yifei Wang et al.
GACT: Activation Compressed Training for Generic Network Architectures
Xiaoxuan Liu, Lianmin Zheng, Dequan Wang et al.
GALAXY: Graph-based Active Learning at the Extreme
Jifan Zhang, Julian Katz-Samuels, Robert Nowak
Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers
Rui Liu, Young Jin Kim, Alexandre Muzio et al.
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification
Junwen Bai, Shufeng Kong, Carla P Gomes
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications
Alexandre Capone, Armin Lederer, Sandra Hirche
Generalised Policy Improvement with Geometric Policy Composition
Shantanu Thakoor, Mark Rowland, Diana Borsa et al.
Generalization and Robustness Implications in Object-Centric Learning
Andrea Dittadi, Samuele S Papa, Michele De Vita et al.
Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers
Liam Hodgkinson, Umut Simsekli, Rajiv Khanna et al.
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling
Hongkang Li, Meng Wang, Sijia Liu et al.