Papers
Calibration tests in multi-class classification: A unifying framework
David Widmann, Fredrik Lindsten, Dave Zachariah
Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently
Xiao Liu, Xiaolong Zou, Zilong Ji et al.
Correlation Priors for Reinforcement Learning
Bastian Alt, Adrian Šošić, Heinz Koeppl
MonoForest framework for tree ensemble analysis
Igor Kuralenok, Vasilii Ershov, Igor Labutin
Ouroboros: On Accelerating Training of Transformer-Based Language Models
Qian Yang, Zhouyuan Huo, Wenlin Wang et al.
When to use parametric models in reinforcement learning?
Hado P van Hasselt, Matteo Hessel, John Aslanides
On the Fairness of Disentangled Representations
Francesco Locatello, Gabriele Abbati, Thomas Rainforth et al.
Trivializations for Gradient-Based Optimization on Manifolds
Mario Lezcano Casado
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
David Janz, Jiri Hron, Przemysław Mazur et al.
Generalization Error Analysis of Quantized Compressive Learning
Xiaoyun Li, Ping Li
Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder
Guy Lorberbom, Andreea Gane, Tommi Jaakkola et al.
Zero-shot Learning via Simultaneous Generating and Learning
Hyeonwoo Yu, Beomhee Lee
Hindsight Credit Assignment
Anna Harutyunyan, Will Dabney, Thomas Mesnard et al.
Large Scale Adversarial Representation Learning
Jeff Donahue, Karen Simonyan
Learning Bayesian Networks with Low Rank Conditional Probability Tables
Adarsh Barik, Jean Honorio
Metalearned Neural Memory
Tsendsuren Munkhdalai, Alessandro Sordoni, TONG WANG et al.
Kernel Instrumental Variable Regression
Rahul Singh, Maneesh Sahani, Arthur Gretton
Distributionally Robust Optimization and Generalization in Kernel Methods
Matthew Staib, Stefanie Jegelka
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning
Yizhe Zhu, Jianwen Xie, Zhiqiang Tang et al.
Robust Attribution Regularization
Jiefeng Chen, Xi Wu, Vaibhav Rastogi et al.
Compositional De-Attention Networks
Yi Tay, Anh Tuan Luu, Aston Zhang et al.
Theoretical Analysis of Adversarial Learning: A Minimax Approach
Zhuozhuo Tu, Jingwei Zhang, Dacheng Tao
A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression
JIAJIN LI, SEN HUANG, Anthony Man-Cho So
Convergence Guarantees for Adaptive Bayesian Quadrature Methods
Motonobu Kanagawa, Philipp Hennig
Learning-Based Low-Rank Approximations
Piotr Indyk, Ali Vakilian, Yang Yuan