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International Conference on Artificial Intelligence and Statistics

AISTATS

A

CORE rank

CORE rank (raw)

A

Acceptance rate

27.6% (2024)

Fields of research

Machine Learning · Artificial Intelligence

Papers indexed

4,516

1995–2025

Papers per year

1995583 peak2025

AISTATS papers

4,516 records sourced from DBLP. Search titles, filter by year, sort by recency.

YearTitleAuthors
2023Causal Entropy Optimization.Nicola Branchini, Virginia Aglietti, Neil Dhir, Theodoros Damoulas
2023Probabilistic Querying of Continuous-Time Event Sequences.Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth
2023Exploration in Reward Machines with Low Regret.Hippolyte Bourel, Anders Jonsson, Odalric-Ambrym Maillard, Mohammad Sadegh Talebi
2023Random Features Model with General Convex Regularization: A Fine Grained Analysis with Precise Asymptotic Learning Curves.David Bosch, Ashkan Panahi, Aya zelikkale, Devdatt P. Dubhashi
2023Isotropic Gaussian Processes on Finite Spaces of Graphs.Viacheslav Borovitskiy, Mohammad Reza Karimi, Vignesh Ram Somnath, Andreas Krause
2023From Shapley Values to Generalized Additive Models and back.Sebastian Bordt, Ulrike von Luxburg
2023Identification of Blackwell Optimal Policies for Deterministic MDPs.Victor Boone, Bruno Gaujal
2023Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems.Matthias Bitzer, Mona Meister, Christoph Zimmer
2023Recurrent Neural Networks and Universal Approximation of Bayesian Filters.Adrian N. Bishop, Edwin V. Bonilla
2023Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty.Felix Biggs, Benjamin Guedj
2023On Universal Portfolios with Continuous Side Information.Alankrita Bhatt, J. Jon Ryu, Young-Han Kim
2023Piecewise Stationary Bandits under Risk Criteria.Sujay Bhatt, Guanhua Fang, Ping Li
2023Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws.Kush Bhatia, Wenshuo Guo, Jacob Steinhardt
2023Competing against Adaptive Strategies in Online Learning via Hints.Aditya Bhaskara, Kamesh Munagala
2023Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods.Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou
2023On the Limitations of the Elo, Real-World Games are Transitive, not Additive.Quentin Bertrand, Wojciech Marian Czarnecki, Gauthier Gidel
2023To Impute or not to Impute? Missing Data in Treatment Effect Estimation.Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar
2023Provable Safe Reinforcement Learning with Binary Feedback.Andrew Bennett, Dipendra Misra, Nathan Kallus
2023Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach.Syrine Belakaria, Janardhan Rao Doppa, Nicol Fusi, Rishit Sheth
2023On the Implicit Geometry of Cross-Entropy Parameterizations for Label-Imbalanced Data.Tina Behnia, Ganesh Ramachandra Kini, Vala Vakilian, Christos Thrampoulidis
2023High Probability Bounds for Stochastic Continuous Submodular Maximization.Evan Becker, Jingdong Gao, Ted Zadouri, Baharan Mirzasoleiman
2023Principled Approaches for Private Adaptation from a Public Source.Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh
2023A Faster Sampler for Discrete Determinantal Point Processes.Simon Barthelm, Nicolas Tremblay, Pierre-Olivier Amblard
2023Adaptive Cholesky Gaussian Processes.Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Muoz, Wouter Boomsma, Jes Frellsen, Sren Hauberg
2023Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data.Batiste Le Bars, Aurlien Bellet, Marc Tommasi, Erick Lavoie, Anne-Marie Kermarrec
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