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AISTATS 2024

A

International Conference on Artificial Intelligence and Statistics

Official site ↗
548
accepted papers
540
authors
May 2-4, 2024
dates
Spain
location

548 / 548 papers

  1. 1
    A 4-Approximation Algorithm for Min Max Correlation Clustering
    Holger S. G. Heidrich
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  2. 2
    A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent
    Mehdi Jafarnia-Jahromi
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  3. 3
    A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning
    Mizhaan Prajit Maniyar
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  4. 4
    A Doubly Robust Approach to Sparse Reinforcement Learning
    Wonyoung Kim
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  5. 5
    A General Algorithm for Solving Rank-one Matrix Sensing
    Lianke Qin
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  6. 6
    A General Theoretical Paradigm to Understand Learning from Human Preferences
    Mohammad Gheshlaghi Azar
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  7. 7
    A Greedy Approximation for k-Determinantal Point Processes
    Julia Grosse
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  8. 8
    A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
    Mathieu Dagréou
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  9. 9
    A Neural Architecture Predictor based on GNN-Enhanced Transformer
    Xunzhi Xiang
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  10. 10
    A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning
    Kihyuk Hong
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  11. 11
    A Scalable Algorithm for Individually Fair k-Means Clustering
    MohammadHossein Bateni
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  12. 12
    A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport
    Tianyi Lin
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  13. 13
    A Unified Framework for Discovering Discrete Symmetries
    Pavan Karjol
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  14. 14
    A Unifying Variational Framework for Gaussian Process Motion Planning
    Lucas Cosier
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  15. 15
    A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models
    Zhongliang Guo
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  16. 16
    A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity
    Zhihan Xiong
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  17. 17
    A/B testing under Interference with Partial Network Information
    Shiv Shankar
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  18. 18
    Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses
    Ziye Ma
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  19. 19
    Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo
    Haoyang Zheng
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  20. 20
    Acceleration and Implicit Regularization in Gaussian Phase Retrieval
    Tyler Maunu
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  21. 21
    Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex
    Yasushi Esaki
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  22. 22
    Achieving Fairness through Separability: A Unified Framework for Fair Representation Learning
    Taeuk Jang
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  23. 23
    Achieving Group Distributional Robustness and Minimax Group Fairness with Interpolating Classifiers
    Natalia Martínez
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  24. 24
    Adaptive and non-adaptive minimax rates for weighted Laplacian-Eigenmap based nonparametric regression
    Zhaoyang Shi
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  25. 25
    Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach
    Masaki Adachi
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  26. 26
    Adaptive Compression in Federated Learning via Side Information
    Berivan Isik
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  27. 27
    Adaptive Discretization for Event PredicTion (ADEPT)
    Jimmy Hickey
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  28. 28
    Adaptive Experiment Design with Synthetic Controls
    Alihan Hüyük
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  29. 29
    Adaptive Federated Minimax Optimization with Lower Complexities
    Feihu Huang
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  30. 30
    Adaptive importance sampling for heavy-tailed distributions via α-divergence minimization
    Thomas Guilmeau
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  31. 31
    Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification
    Marzi Heidari
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  32. 32
    Adaptive Quasi-Newton and Anderson Acceleration Framework with Explicit Global (Accelerated) Convergence Rates
    Damien Scieur
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  33. 33
    Adaptivity of Diffusion Models to Manifold Structures
    Rong Tang
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  34. 34
    Agnostic Multi-Robust Learning using ERM
    Saba Ahmadi
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  35. 35
    ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data
    Maria C. Novitasari
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  36. 36
    An Analytic Solution to Covariance Propagation in Neural Networks
    Oren Wright
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  37. 37
    An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization
    Lesi Chen
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  38. 38
    An Impossibility Theorem for Node Embedding
    T. Mitchell Roddenberry
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  39. 39
    An Improved Algorithm for Learning Drifting Discrete Distributions
    Alessio Mazzetto
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  40. 40
    An Online Bootstrap for Time Series
    Nicolai Palm
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  41. 41
    Analysis of Kernel Mirror Prox for Measure Optimization
    Pavel E. Dvurechensky
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  42. 42
    Analysis of Privacy Leakage in Federated Large Language Models
    Minh N. Vu
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  43. 43
    Analysis of Using Sigmoid Loss for Contrastive Learning
    Chungpa Lee
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  44. 44
    Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions
    Zulqarnain Khan
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  45. 45
    Any-dimensional equivariant neural networks
    Eitan Levin
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  46. 46
    Anytime-Constrained Reinforcement Learning
    Jeremy McMahan
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  47. 47
    Approximate Bayesian Class-Conditional Models under Continuous Representation Shift
    Thomas L. Lee
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  48. 48
    Approximate Control for Continuous-Time POMDPs
    Yannick Eich
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  49. 49
    Approximate Leave-one-out Cross Validation for Regression with ℓ1 Regularizers
    Arnab Auddy
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  50. 50
    AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms
    Rustem Islamov
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