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AISTATS 2025

A

International Conference on Artificial Intelligence and Statistics

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584
accepted papers
564
authors
May 3-5, 2025
dates
Thailand
location

584 / 584 papers

  1. 1
    A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets
    Ossi Räisä
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  2. 2
    A Causal Framework for Evaluating Deferring Systems
    Filippo Palomba
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  3. 3
    A Computation-Efficient Method of Measuring Dataset Quality based on the Coverage of the Dataset
    Beomjun Kim
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  4. 4
    A Convex Relaxation Approach to Generalization Analysis for Parallel Positively Homogeneous Networks
    Uday Kiran Reddy Tadipatri
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  5. 5
    A Differential Inclusion Approach for Learning Heterogeneous Sparsity in Neuroimaging Analysis
    Wenjing Han
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  6. 6
    A Family of Distributions of Random Subsets for Controlling Positive and Negative Dependence
    Takahiro Kawashima
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  7. 7
    A Generalized Theory of Mixup for Structure-Preserving Synthetic Data
    Chungpa Lee
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  8. 8
    A graphical global optimization framework for parameter estimation of statistical models with nonconvex regularization functions
    Danial Davarnia
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  9. 9
    A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs
    Kasimir Tanner
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  10. 10
    A Likelihood Based Approach for Watermark Detection
    Xingchi Li
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  11. 11
    A Multi-Armed Bandit Approach to Online Selection and Evaluation of Generative Models
    Xiaoyan Hu
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  12. 12
    A Multi-Task Learning Approach to Linear Multivariate Forecasting
    Liran Nochumsohn
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  13. 13
    A Novel Convex Gaussian Min Max Theorem for Repeated Features
    David Bosch
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  14. 14
    A primer on linear classification with missing data
    Angel David Reyero Lobo
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  15. 15
    A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities
    Yatin Dandi
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  16. 16
    A Robust Kernel Statistical Test of Invariance: Detecting Subtle Asymmetries
    Ashkan Soleymani
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  17. 17
    A Safe Bayesian Learning Algorithm for Constrained MDPs with Bounded Constraint Violation
    Krishna Chaitanya Kalagarla
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  18. 18
    A Safe Exploration Approach to Constrained Markov Decision Processes
    Tingting Ni
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  19. 19
    A Shapley-value Guided Rationale Editor for Rationale Learning
    Zixin Kuang
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  20. 20
    A Shared Low-Rank Adaptation Approach to Personalized RLHF
    Renpu Liu
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  21. 21
    A Subquadratic Time Approximation Algorithm for Individually Fair k-Center
    Matthijs Ebbens
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  22. 22
    A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning
    Chungpa Lee
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  23. 23
    A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration
    Yingqian Cui
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  24. 24
    A Tight Regret Analysis of Non-Parametric Repeated Contextual Brokerage
    François Bachoc
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  25. 25
    A Unified Evaluation Framework for Epistemic Predictions
    Shireen Kudukkil Manchingal
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  26. 26
    A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning
    Khimya Khetarpal
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  27. 27
    Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties
    David Martínez-Rubio
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  28. 28
    Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation
    Amartya Sanyal
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  29. 29
    Achieving $\widetilde{\mathcal{O}}(\sqrt{T})$ Regret in Average-Reward POMDPs with Known Observation Models
    Alessio Russo
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  30. 30
    Active Bipartite Ranking with Smooth Posterior Distributions
    James Cheshire
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  31. 31
    Active Feature Acquisition for Personalised Treatment Assignment
    Julianna Piskorz
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  32. 32
    Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach
    Dheeraj Baby
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  33. 33
    Adaptive Convergence Rates for Log-Concave Maximum Likelihood
    Gil Kur
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  34. 34
    Adaptive Extragradient Methods for Root-finding Problems under Relaxed Assumptions
    Yang Luo
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  35. 35
    Adaptive RKHS Fourier Features for Compositional Gaussian Process Models
    Xinxing Shi
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  36. 36
    Additive Model Boosting: New Insights and Path(ologie)s
    Rickmer Schulte
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  37. 37
    ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning
    Kaan Ozkara
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  38. 38
    Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data
    Hongni Wang
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  39. 39
    Adversarial Training in High-Dimensional Regression: Generated Data and Neural Networks
    Yue Xing
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  40. 40
    Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting
    Fuqiang Liu
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  41. 41
    Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits
    Sreejeet Maity
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  42. 42
    Algorithmic Accountability in Small Data: Sample-Size-Induced Bias Within Classification Metrics
    Jarren Briscoe
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  43. 43
    All models are wrong, some are useful: Model Selection with Limited Labels
    Patrik Okanovic
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  44. 44
    All or None: Identifiable Linear Properties of Next-Token Predictors in Language Modeling
    Emanuele Marconato
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  45. 45
    AlleNoise - large-scale text classification benchmark dataset with real-world label noise
    Alicja Raczkowska
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  46. 46
    Almost linear time differentially private release of synthetic graphs
    Zongrui Zou
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  47. 47
    Amortized Probabilistic Conditioning for Optimization, Simulation and Inference
    Paul Edmund Chang
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  48. 48
    An Adaptive Method for Weak Supervision with Drifting Data
    Alessio Mazzetto
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  49. 49
    An Empirical Bernstein Inequality for Dependent Data in Hilbert Spaces and Applications
    Erfan Mirzaei
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  50. 50
    An Iterative Algorithm for Rescaled Hyperbolic Functions Regression
    Yeqi Gao
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