Papers
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
Tim Pearce, Felix Leibfried, Alexandra Brintrup
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
Zepeng Huo, Arash PakBin, Xiaohan Chen et al.
Uncertainty Quantification for Sparse Deep Learning
Yuexi Wang, Veronika Rockova
Unconditional Coresets for Regularized Loss Minimization
Alireza Samadian, Kirk Pruhs, Benjamin Moseley et al.
Understanding Generalization in Deep Learning via Tensor Methods
Jingling Li, Yanchao Sun, Jiahao Su et al.
Understanding the Effects of Batching in Online Active Learning
Kareem Amin, Corinna Cortes, Giulia DeSalvo et al.
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models
Xiao Zhang, Jinghui Chen, Quanquan Gu et al.
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces
David Alvarez-Melis, Youssef Mroueh, Tommi Jaakkola
Unsupervised Neural Universal Denoiser for Finite-Input General-Output Noisy Channel
Taeeon Park, Taesup Moon
Utility/Privacy Trade-off through the lens of Optimal Transport
Etienne Boursier, Vianney Perchet
Validated Variational Inference via Practical Posterior Error Bounds
Jonathan Huggins, Mikolaj Kasprzak, Trevor Campbell et al.
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations
Niccolo Dalmasso, Ann Lee, Rafael Izbicki et al.
Value Preserving State-Action Abstractions
David Abel, Nate Umbanhowar, Khimya Khetarpal et al.
Variance Reduction for Evolution Strategies via Structured Control Variates
Yunhao Tang, Krzysztof Choromanski, Alp Kucukelbir
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
Ilyes Khemakhem, Diederik Kingma, Ricardo Monti et al.
Variational Autoencoders for Sparse and Overdispersed Discrete Data
He Zhao, Piyush Rai, Lan Du et al.
Variational Integrator Networks for Physically Structured Embeddings
Steindor Saemundsson, Alexander Terenin, Katja Hofmann et al.
Variational Optimization on Lie Groups, with Examples of Leading (Generalized) Eigenvalue Problems
Molei Tao, Tomoki Ohsawa
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks
Alexander Levine, Soheil Feizi
Wasserstein Style Transfer
Youssef Mroueh
$HS^2$: Active learning over hypergraphs with pointwise and pairwise queries
I (Eli) Chien, Huozhi Zhou, Pan Li
$β^3$-IRT: A New Item Response Model and its Applications
Yu Chen, Telmo Silva Filho, Ricardo B. Prudencio et al.
A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure
Juho Lee, Lancelot James, Seungjin Choi et al.
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
Raj Agrawal, Chandler Squires, Karren Yang et al.