Papers
Fast Bellman Updates for Wasserstein Distributionally Robust MDPs
Zhuodong Yu, Ling Dai, Shaohang Xu et al.
Fast Conditional Mixing of MCMC Algorithms for Non-log-concave Distributions
Xiang Cheng, Bohan Wang, Jingzhao Zhang et al.
Faster approximate subgraph counts with privacy
Dung Nguyen, Mahantesh Halappanavar, Venkatesh Srinivasan et al.
Faster Differentially Private Convex Optimization via Second-Order Methods
Arun Ganesh, Mahdi Haghifam, Thomas Steinke et al.
Faster Discrete Convex Function Minimization with Predictions: The M-Convex Case
Taihei Oki, Shinsaku Sakaue
Faster Margin Maximization Rates for Generic Optimization Methods
Guanghui Wang, Zihao Hu, Vidya Muthukumar et al.
Faster Query Times for Fully Dynamic $k$-Center Clustering with Outliers
Leyla Biabani, Annika Hennes, Morteza Monemizadeh et al.
Faster Relative Entropy Coding with Greedy Rejection Coding
Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato
Fast Exact Leverage Score Sampling from Khatri-Rao Products with Applications to Tensor Decomposition
Vivek Bharadwaj, Osman Asif Malik, Riley Murray et al.
Fast Model DeBias with Machine Unlearning
Ruizhe Chen, Jianfei Yang, Huimin Xiong et al.
Fast Optimal Locally Private Mean Estimation via Random Projections
Hilal Asi, Vitaly Feldman, Jelani Nelson et al.
Fast Optimal Transport through Sliced Generalized Wasserstein Geodesics
Guillaume Mahey, Laetitia Chapel, Gilles Gasso et al.
Fast Partitioned Learned Bloom Filter
Atsuki Sato, Yusuke Matsui
Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity
Jian-Feng CAI, José Vinícius de Miranda Cardoso, Daniel Palomar et al.
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow Shrink Trees
Bryan Andrews, Joseph Ramsey, Ruben Sanchez Romero et al.
Fast Trainable Projection for Robust Fine-tuning
Junjiao Tian, Yen-Cheng Liu, James S Smith et al.
FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-Trained Models in Few-Shot Learning
Kun Song, Huimin Ma, Bochao Zou et al.
Feature Adaptation for Sparse Linear Regression
Jonathan Kelner, Frederic Koehler, Raghu Meka et al.
Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning
Alex Tamkin, Margalit Glasgow, Xiluo He et al.
Feature Learning for Interpretable, Performant Decision Trees
Jack Good, Torin Kovach, Kyle Miller et al.
Feature-Learning Networks Are Consistent Across Widths At Realistic Scales
Nikhil Vyas, Alexander Atanasov, Blake Bordelon et al.
Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond
Taiji Suzuki, Denny Wu, Kazusato Oko et al.
Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples
Marco Jiralerspong, Joey Bose, Ian Gemp et al.
Feature Selection in the Contrastive Analysis Setting
Ethan Weinberger, Ian Covert, Su-In Lee