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UAI 2024

A

Conference on Uncertainty in Artificial Intelligence

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201
accepted papers
197
authors
July 15-19, 2024
dates
Spain
location

201 / 201 papers

  1. 1
    A General Identification Algorithm For Data Fusion Problems Under Systematic Selection
    Jaron Jia Rong Lee
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  2. 2
    A Generalized Bayesian Approach to Distribution-on-Distribution Regression
    Tin Lok James Ng
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  3. 3
    A Global Markov Property for Solutions of Stochastic Difference Equations and the corresponding Full Time Graphs
    Tom Hochsprung
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  4. 4
    A Graph Theoretic Approach for Preference Learning with Feature Information
    Aadirupa Saha
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  5. 5
    A Homogenization Approach for Gradient-Dominated Stochastic Optimization
    Jiyuan Tan
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  6. 6
    Active Learning Framework for Incomplete Networks
    Tung Khong
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  7. 7
    Adaptive Softmax Trees for Many-Class Classification
    Rasul Kairgeldin
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  8. 8
    Adaptive Time-Stepping Schedules for Diffusion Models
    Yuzhu Chen
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  9. 9
    Adjustment Identification Distance: A gadjid for Causal Structure Learning
    Leonard Henckel
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  10. 10
    Amortized Variational Inference: When and Why?
    Charles C. Margossian
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  11. 11
    Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression
    Lucas Clarté
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  12. 12
    Anomaly Detection with Variance Stabilized Density Estimation
    Amit Rozner
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  13. 13
    Approximate Bayesian Computation with Path Signatures
    Joel Dyer
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  14. 14
    Approximate Kernel Density Estimation under Metric-based Local Differential Privacy
    Yi Zhou
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  15. 15
    Approximation Algorithms for Observer Aware MDPs
    Shuwa Miura
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  16. 16
    AutoDrop: Training Deep Learning Models with Automatic Learning Rate Drop
    Jing Wang
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  17. 17
    BanditQ: Fair Bandits with Guaranteed Rewards
    Abhishek Sinha
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  18. 18
    Bandits with Knapsacks and Predictions
    Davide Drago
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  19. 19
    Base Models for Parabolic Partial Differential Equations
    Xingzi Xu
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  20. 20
    Bayesian Active Learning in the Presence of Nuisance Parameters
    Sabina J. Sloman
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  21. 21
    Bayesian Pseudo-Coresets via Contrastive Divergence
    Piyush Tiwary
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  22. 22
    BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts
    Emanuele Marconato
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  23. 23
    Beyond Dirichlet-based Models: When Bayesian Neural Networks Meet Evidential Deep Learning
    Hanjing Wang
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  24. 24
    Bias-aware Boolean Matrix Factorization Using Disentangled Representation Learning
    Xiao Wang
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  25. 25
    Bootstrap Your Conversions: Thompson Sampling for Partially Observable Delayed Rewards
    Marco Gigli
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  26. 26
    Bounding causal effects with leaky instruments
    David S. Watson
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  27. 27
    Calibrated and Conformal Propensity Scores for Causal Effect Estimation
    Shachi Deshpande
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  28. 28
    Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring
    Khoi Tran Dang
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  29. 29
    Causal Discovery with Deductive Reasoning: One Less Problem
    Jonghwan Kim
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  30. 30
    Causally Abstracted Multi-armed Bandits
    Fabio Massimo Zennaro
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  31. 31
    Center-Based Relaxed Learning Against Membership Inference Attacks
    Xingli Fang
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  32. 32
    Characterising Interventions in Causal Games
    Manuj Mishra
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  33. 33
    Characterizing Data Point Vulnerability as Average-Case Robustness
    Tessa Han
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  34. 34
    Cold-start Recommendation by Personalized Embedding Region Elicitation
    Hieu Trung Nguyen
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  35. 35
    Common Event Tethering to Improve Prediction of Rare Clinical Events
    Quinn Lanners
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  36. 36
    Computing Low-Entropy Couplings for Large-Support Distributions
    Samuel Sokota
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  37. 37
    Conditional Bayesian Quadrature
    Zonghao Chen
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  38. 38
    Consistency Regularization for Domain Generalization with Logit Attribution Matching
    Han Gao
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  39. 39
    ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-variable Context Encoding
    Denis A. Gudovskiy
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  40. 40
    Convergence Behavior of an Adversarial Weak Supervision Method
    Steven An
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  41. 41
    Cooperative Meta-Learning with Gradient Augmentation
    Jongyun Shin
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  42. 42
    Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection
    Moussa Kassem Sbeyti
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  43. 43
    CSS: Contrastive Semantic Similarities for Uncertainty Quantification of LLMs
    Shuang Ao
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  44. 44
    DataSP: A Differential All-to-All Shortest Path Algorithm for Learning Costs and Predicting Paths with Context
    Alan A. Lahoud
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  45. 45
    Decentralized Online Learning in General-Sum Stackelberg Games
    Yaolong Yu
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  46. 46
    Decentralized Two-Sided Bandit Learning in Matching Market
    Yirui Zhang
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  47. 47
    Decision-Focused Evaluation of Worst-Case Distribution Shift
    Kevin Ren
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  48. 48
    Detecting critical treatment effect bias in small subgroups
    Piersilvio De Bartolomeis
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  49. 49
    Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation
    Yoichi Chikahara
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  50. 50
    Differentially Private No-regret Exploration in Adversarial Markov Decision Processes
    Shaojie Bai
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