Papers
Weighted Distillation with Unlabeled Examples
Fotis Iliopoulos, Vasilis Kontonis, Cenk Baykal et al.
Weighted Mutual Learning with Diversity-Driven Model Compression
Miao Zhang, Li Wang, David Campos et al.
WeightedSHAP: analyzing and improving Shapley based feature attributions
Yongchan Kwon, James Y Zou
What are the best Systems? New Perspectives on NLP Benchmarking
Pierre Colombo, Nathan Noiry, Ekhine Irurozki et al.
What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?
Nikolaos Tsilivis, Julia Kempe
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes
Shivam Garg, Dimitris Tsipras, Percy Liang et al.
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods
Julien Colin, Thomas FEL, Remi Cadene et al.
What is a Good Metric to Study Generalization of Minimax Learners?
Asuman Ozdaglar, Sarath Pattathil, Jiawei Zhang et al.
What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs
Tal Shaharabany, Yoad Tewel, Lior Wolf
What Makes a "Good" Data Augmentation in Knowledge Distillation - A Statistical Perspective
Huan Wang, Suhas Lohit, Michael N. Jones et al.
What Makes Graph Neural Networks Miscalibrated?
Hans Hao-Hsun Hsu, Yuesong Shen, Christian Tomani et al.
What You See is What You Classify: Black Box Attributions
Steven Stalder, Nathanael Perraudin, Radhakrishna Achanta et al.
What You See is What You Get: Principled Deep Learning via Distributional Generalization
Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu et al.
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture
Yichuan Mo, Dongxian Wu, Yifei Wang et al.
When are Local Queries Useful for Robust Learning?
Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska et al.
When are Offline Two-Player Zero-Sum Markov Games Solvable?
Qiwen Cui, Simon S Du
When Combinatorial Thompson Sampling meets Approximation Regret
Pierre Perrault
When Does Differentially Private Learning Not Suffer in High Dimensions?
Xuechen Li, Daogao Liu, Tatsunori B Hashimoto et al.
When does dough become a bagel? Analyzing the remaining mistakes on ImageNet
Vijay Vasudevan, Benjamin Caine, Raphael Gontijo Lopes et al.
When Does Group Invariant Learning Survive Spurious Correlations?
Yimeng Chen, Ruibin Xiong, Zhi-Ming Ma et al.
When does return-conditioned supervised learning work for offline reinforcement learning?
David Brandfonbrener, Alberto Bietti, Jacob Buckman et al.
When Do Flat Minima Optimizers Work?
Jean Kaddour, Linqing Liu, Ricardo Silva et al.
When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits
Achraf Azize, Debabrota Basu