Papers
How Powerful are Spectral Graph Neural Networks
Xiyuan Wang, Muhan Zhang
How Tempering Fixes Data Augmentation in Bayesian Neural Networks
Gregor Bachmann, Lorenzo Noci, Thomas Hofmann
How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity
Chengyue Gong, Lemeng Wu, Qiang Liu
How to Leverage Unlabeled Data in Offline Reinforcement Learning
Tianhe Yu, Aviral Kumar, Yevgen Chebotar et al.
How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation
Augustine Mavor-Parker, Kimberly Young, Caswell Barry et al.
How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection
Mantas Mazeika, Bo Li, David Forsyth
How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective
Akhilan Boopathy, Ila Fiete
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation
Xiaoyu Chen, Han Zhong, Zhuoran Yang et al.
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection
Daniel Jarrett, Bogdan C Cebere, Tennison Liu et al.
HyperPrompt: Prompt-based Task-Conditioning of Transformers
Yun He, Steven Zheng, Yi Tay et al.
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
Andrey Zhmoginov, Mark Sandler, Maksym Vladymyrov
Identifiability Conditions for Domain Adaptation
Ishaan Gulrajani, Tatsunori Hashimoto
Identification of Linear Non-Gaussian Latent Hierarchical Structure
Feng Xie, Biwei Huang, Zhengming Chen et al.
Identity-Disentangled Adversarial Augmentation for Self-supervised Learning
Kaiwen Yang, Tianyi Zhou, Xinmei Tian et al.
IDYNO: Learning Nonparametric DAGs from Interventional Dynamic Data
Tian Gao, Debarun Bhattacharjya, Elliot Nelson et al.
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
Emanuele Bugliarello, Fangyu Liu, Jonas Pfeiffer et al.
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
Anastasios N Angelopoulos, Amit Pal Kohli, Stephen Bates et al.
Imitation Learning by Estimating Expertise of Demonstrators
Mark Beliaev, Andy Shih, Stefano Ermon et al.
Implicit Bias of Linear Equivariant Networks
Hannah Lawrence, Kristian Georgiev, Andrew Dienes et al.
Implicit Bias of the Step Size in Linear Diagonal Neural Networks
Mor Shpigel Nacson, Kavya Ravichandran, Nathan Srebro et al.
Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks
Noam Razin, Asaf Maman, Nadav Cohen
Implicit Regularization with Polynomial Growth in Deep Tensor Factorization
Kais Hariz, Hachem Kadri, Stephane Ayache et al.
Importance Weighted Kernel Bayes’ Rule
Liyuan Xu, Yutian Chen, Arnaud Doucet et al.
Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation
Wenxiao Wang, Alexander J Levine, Soheil Feizi
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning
Sattar Vakili, Jonathan Scarlett, Da-Shan Shiu et al.