Papers
Fast and Adversarial Robust Kernelized SDU Learning
Yajing Fan, wanli shi, Yi Chang et al.
Fast Dynamic Sampling for Determinantal Point Processes
Zhao Song, Junze Yin, Lichen Zhang et al.
Faster Convergence with MultiWay Preferences
Aadirupa Saha, Vitaly Feldman, Yishay Mansour et al.
Faster Recalibration of an Online Predictor via Approachability
Princewill Okoroafor, Bobby Kleinberg, Wen Sun
Fast Fourier Bayesian Quadrature
Houston Warren, Fabio Ramos
Fast Minimization of Expected Logarithmic Loss via Stochastic Dual Averaging
Chung-En Tsai, Hao-Chung Cheng, Yen-Huan Li
Feasible $Q$-Learning for Average Reward Reinforcement Learning
Ying Jin, Ramki Gummadi, Zhengyuan Zhou et al.
Federated Experiment Design under Distributed Differential Privacy
Wei-Ning Chen, Graham Cormode, Akash Bharadwaj et al.
Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks
Soheila Molaei, Anshul Thakur, Ghazaleh Niknam et al.
Federated Linear Contextual Bandits with Heterogeneous Clients
Ethan Blaser, Chuanhao Li, Hongning Wang
FedFisher: Leveraging Fisher Information for One-Shot Federated Learning
Divyansh Jhunjhunwala, Shiqiang Wang, Gauri Joshi
Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models
Soheun Yi, Sanghack Lee
First Passage Percolation with Queried Hints
Kritkorn Karntikoon, Yiheng Shen, Sreenivas Gollapudi et al.
Fitting ARMA Time Series Models without Identification: A Proximal Approach
Yin Liu, Sam Davanloo Tajbakhsh
Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit
Shintaro Nakamura, Masashi Sugiyama
Fixed-kinetic Neural Hamiltonian Flows for enhanced interpretability and reduced complexity
Vincent Souveton, Arnaud Guillin, Jens Jasche et al.
Formal Verification of Unknown Stochastic Systems via Non-parametric Estimation
Zhi Zhang, Chenyu Ma, Saleh Soudijani et al.
Free-form Flows: Make Any Architecture a Normalizing Flow
Felix Draxler, Peter Sorrenson, Lea Zimmermann et al.
From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach
Tuan Nguyen, Hirotada Honda, Takashi Sano et al.
From Data Imputation to Data Cleaning — Automated Cleaning of Tabular Data Improves Downstream Predictive Performance
Sebastian Jäger, Felix Biessmann
Functional Flow Matching
Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization
Kuba Grudzien, Masatoshi Uehara, Sergey Levine et al.
Fusing Individualized Treatment Rules Using Secondary Outcomes
Daiqi Gao, Yuanjia Wang, Donglin Zeng
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
Raphaël Carpintero Perez, Sébastien Da Veiga, Josselin Garnier et al.
General Identifiability and Achievability for Causal Representation Learning
Burak Varici, Emre Acartürk, Karthikeyan Shanmugam et al.