Papers
938 papers found
Generative Archimedean copulas
Yuting Ng, Ali Hasan, Khalil Elkhalil et al.
Geometric rates of convergence for kernel-based sampling algorithms
Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney
Global explanations with decision rules: a co-learning approach
Géraldin Nanfack, Paul Temple, Benoît Frénay
GP-ConvCNP: Better generalization for conditional convolutional Neural Processes on time series data
Jens Petersen, Gregor Köhler, David Zimmerer et al.
Gradient-based optimization for multi-resource spatial coverage problems
Nitin Kamra, Yan Liu
Graph-based semi-supervised learning through the lens of safety
Shreyas Sheshadri, Avirup Saha, Priyank Patel et al.
Graph reparameterizations for enabling 1000+ Monte Carlo iterations in Bayesian deep neural networks
Jurijs Nazarovs, Ronak R. Mehta, Vishnu Suresh Lokhande et al.
Hierarchical Indian buffet neural networks for Bayesian continual learning
Samuel Kessler, Vu Nguyen, Stefan Zohren et al.
Hierarchical infinite relational model
Feras A. Saad, Vikash K. Mansinghka
Hierarchical learning of Hidden Markov Models with clustering regularization
Hui Lan, Antoni B. Chan
Hierarchical probabilistic model for blind source separation via Legendre transformation
Simon Luo, Lamiae Azizi, Mahito Sugiyama
High-dimensional Bayesian optimization with sparse axis-aligned subspaces
David Eriksson, Martin Jankowiak
Identifying regions of trusted predictions
Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner et al.
Identifying untrustworthy predictions in neural networks by geometric gradient analysis
Leo Schwinn, An Nguyen, René Raab et al.
Improved generalization bounds of group invariant / equivariant deep networks via quotient feature spaces
Akiyoshi Sannai, Masaaki Imaizumi, Makoto Kawano
Improving approximate optimal transport distances using quantization
Gaspard Beugnot, Aude Genevay, Kristjan Greenewald et al.
Improving uncertainty calibration of deep neural networks via truth discovery and geometric optimization
Chunwei Ma, Ziyun Huang, Jiayi Xian et al.
Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation
Takeshi Teshima, Masashi Sugiyama
Inference of causal effects when control variables are unknown
Ludvig Hult, Dave Zachariah
Information theoretic meta learning with Gaussian processes
Michalis K. Titsias, Francisco J. R. Ruiz, Sotirios Nikoloutsopoulos et al.
Integer programming-based error-correcting output code design for robust classification
Samarth Gupta, Saurabh Amin
Invariant representation learning for treatment effect estimation
Claudia Shi, Victor Veitch, David M. Blei
Known unknowns: Learning novel concepts using reasoning-by-elimination
Harsh Agrawal, Eli A. Meirom, Yuval Atzmon et al.
Know your limits: Uncertainty estimation with ReLU classifiers fails at reliable OOD detection
Dennis Ulmer, Giovanni Cinà