Papers
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
Topological Autoencoders
Michael Moor, Max Horn, Bastian Rieck et al.
Topologically Densified Distributions
Christoph Hofer, Florian Graf, Marc Niethammer et al.
Towards Accurate Post-training Network Quantization via Bit-Split and Stitching
Peisong Wang, Qiang Chen, Xiangyu He et al.
Towards Adaptive Residual Network Training: A Neural-ODE Perspective
Chengyu Dong, Liyuan Liu, Zichao Li et al.
Towards Non-Parametric Drift Detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)
Fabian Hinder, André Artelt, Barbara Hammer
Towards Understanding the Dynamics of the First-Order Adversaries
Zhun Deng, Hangfeng He, Jiaoyang Huang et al.
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks
Yuxin Wen, Shuai Li, Kui Jia
Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
Zhuohan Li, Eric Wallace, Sheng Shen et al.
Training Binary Neural Networks through Learning with Noisy Supervision
Kai Han, Yunhe Wang, Yixing Xu et al.