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会议文集
会议名
37th Conference on Uncertainty in Artificial Intelligence (UAI 2021)
中译名
《第三十七届人工智能不确定性会议,卷1》
机构
Association for Uncertainty in Artificial Intelligence (AUAI)
会议日期
27-30 July 2021
会议地点
Online
出版年
2021
馆藏号
342228
题名
作者
出版年
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence - Preface
Cassio de Campos; Marloes H. Maathuis; Erik Quaeghebeur
2021
The Neural Moving Average Model for Scalable Variational Inference of State Space Models
Thomas Ryder; Dennis Prangle; Andrew Golightly; Isaac Matthews
2021
Task Similarity Aware Meta Learning: Theory-inspired Improvement on MAML
Pan Zhou; Yingtian Zou; Xiao-Tong Yuan; Jiashi Feng; Caiming Xiong; Steven Hoi
2021
Efficient Debiased Evidence Estimation by Multilevel Monte Carlo Sampling
Kei Ishikawa; Takashi Goda
2021
Variational Inference with Continuously-Indexed Normalizing Flows
Anthony Caterini; Rob Cornish; Dino Sejdinovic; Arnaud Doucet
2021
TreeBERT: A Tree-Based Pre-Trained Model for Programming Language
Xue Jiang; Zhuoran Zheng; Chen Lyu; Liang Li; Lei Lyu
2021
Competitive Policy Optimization
Manish Prajapat; Kamyar Azizzadenesheli; Alexander Liniger; Yisong Yue; Anima Anandkumar
2021
Improving Uncertainty Calibration of Deep Neural Networks via Truth Discovery and Geometric Optimization
Chunwei Ma; Ziyun Huang; JiayiXian; Mingchen Gao; Jinhui Xu
2021
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation
Takeshi Teshima; Masashi Sugiyama
2021
Causal Additive Models with Unobserved Variables
Takashi Nicholas Maeda; Shohei Shimizu
2021
A Variational Approximation for Analyzing the Dynamics of Panel Data
Jurijs Nazarovs; Rudrasis Chakraborty; Songwong Tasneeyapant; Sathya N. Ravi; Vikas Singh
2021
Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks
Jurijs Nazarovs; Ronak R. Mehta; Vishnu Suresh Lokhande; Vikas Singh
2021
The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization
Yifei Min; Lin Chen; Amin Karbasi
2021
Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning
Yizhao Gao; Nanyi Fei; Guangzhen Liu; Zhiwu Lu; Tao Xiang
2021
XOR-SGD: Provable Convex Stochastic Optimization for Decision-making under Uncertainty
Fan Ding; Yexiang Xue
2021
Path Dependent Structural Equation Models
Ranjani Srinivasan; Jaron J. R. Lee; Rohit Bhattacharya; Ilya Shpitser
2021
Featurized Density Ratio Estimation
Kristy Choi; Madeline Liao; Stefano Ermon
2021
Variance Reduction in Frequency Estimators via Control Variates Method
Rameshwar Pratap; Raghav Kulkarni
2021
Application of Kernel Hypothesis Testing on Set-valued Data
Alexis Bellot; Mihaela van der Schaar
2021
A Kernel Two-Sample Test with Selection Bias
Alexis Bellot; Mihaela van der Schaar
2021
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