Papers
The Effect of Natural Distribution Shift on Question Answering Models
John Miller, Karl Krauth, Benjamin Recht et al.
The FAST Algorithm for Submodular Maximization
Adam Breuer, Eric Balkanski, Yaron Singer
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent
Karthik Abinav Sankararaman, Soham De, Zheng Xu et al.
The Implicit and Explicit Regularization Effects of Dropout
Colin Wei, Sham Kakade, Tengyu Ma
The Implicit Regularization of Stochastic Gradient Flow for Least Squares
Alnur Ali, Edgar Dobriban, Ryan Tibshirani
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation
Zhe Feng, David Parkes, Haifeng Xu
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Jakub Swiatkowski, Kevin Roth, Bastiaan Veeling et al.
The Many Shapley Values for Model Explanation
Mukund Sundararajan, Amir Najmi
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization
Ben Adlam, Jeffrey Pennington
The Non-IID Data Quagmire of Decentralized Machine Learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu et al.
The Performance Analysis of Generalized Margin Maximizers on Separable Data
Fariborz Salehi, Ehsan Abbasi, Babak Hassibi
The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture
Francesca Mignacco, Florent Krzakala, Yue Lu et al.
The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons
Wenbo Ren, Jia Liu, Ness Shroff
The Shapley Taylor Interaction Index
Mukund Sundararajan, Kedar Dhamdhere, Ashish Agarwal
The Tree Ensemble Layer: Differentiability meets Conditional Computation
Hussein Hazimeh, Natalia Ponomareva, Petros Mol et al.
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse
Bin Dai, Ziyu Wang, David Wipf
Thompson Sampling Algorithms for Mean-Variance Bandits
Qiuyu Zhu, Vincent Tan
Thompson Sampling via Local Uncertainty
Zhendong Wang, Mingyuan Zhou
Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance
Blair Bilodeau, Dylan Foster, Daniel Roy
Tightening Exploration in Upper Confidence Reinforcement Learning
Hippolyte Bourel, Odalric Maillard, Mohammad Sadegh Talebi
Time-aware Large Kernel Convolutions
Vasileios Lioutas, Yuhong Guo
Time-Consistent Self-Supervision for Semi-Supervised Learning
Tianyi Zhou, Shengjie Wang, Jeff Bilmes
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders
Ioana Bica, Ahmed Alaa, Mihaela Van Der Schaar
Too Relaxed to Be Fair
Michael Lohaus, Michael Perrot, Ulrike Von Luxburg
Topic Modeling via Full Dependence Mixtures
Dan Fisher, Mark Kozdoba, Shie Mannor