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
Most published authors
AISTATS papers
4,516 records sourced from DBLP. Search titles, filter by year, sort by recency.
| Year | Title | Authors |
|---|---|---|
| 2017 | On the Troll-Trust Model for Edge Sign Prediction in Social Networks. | Graud Le Falher, Nicol Cesa-Bianchi, Claudio Gentile, Fabio Vitale |
| 2017 | Annular Augmentation Sampling. | Francois Fagan, Jalaj Bhandari, John P. Cunningham |
| 2017 | Encrypted Accelerated Least Squares Regression. | Pedro M. Esperana, Louis J. M. Aslett, Chris C. Holmes |
| 2017 | Trading off Rewards and Errors in Multi-Armed Bandits. | Akram Erraqabi, Alessandro Lazaric, Michal Valko, Emma Brunskill, Yun-En Liu |
| 2017 | Scalable Learning of Non-Decomposable Objectives. | Elad Eban, Mariano Schain, Alan Mackey, Ariel Gordon, Ryan Rifkin, Gal Elidan |
| 2017 | Data Driven Resource Allocation for Distributed Learning. | Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Nina Balcan, Alexander J. Smola |
| 2017 | Automated Inference with Adaptive Batches. | Soham De, Abhay Kumar Yadav, David W. Jacobs, Tom Goldstein |
| 2017 | Learning from Conditional Distributions via Dual Embeddings. | Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song |
| 2017 | Online Optimization of Smoothed Piecewise Constant Functions. | Vincent Cohen-Addad, Varun Kanade |
| 2017 | Rank Aggregation and Prediction with Item Features. | Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon |
| 2017 | Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection. | Lijie Chen, Jian Li, Mingda Qiao |
| 2017 | Near-optimal Bayesian Active Learning with Correlated and Noisy Tests. | Yuxin Chen, Seyed Hamed Hassani, Andreas Krause |
| 2017 | Clustering from Multiple Uncertain Experts. | Yale Chang, Junxiang Chen, Michael H. Cho, Peter J. Castaldi, Edwin K. Silverman, Jennifer G. Dy |
| 2017 | Distributed Adaptive Sampling for Kernel Matrix Approximation. | Daniele Calandriello, Alessandro Lazaric, Michal Valko |
| 2017 | Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. | Ioan Gabriel Bucur, Tom Claassen, Tom Heskes |
| 2017 | Bayesian Hybrid Matrix Factorisation for Data Integration. | Thomas Brouwer, Pietro Li |
| 2017 | Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification. | Aleksandar Botev, Bowen Zheng, David Barber |
| 2017 | Structured adaptive and random spinners for fast machine learning computations. | Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski, Francois Fagan, Cdric Gouy-Pailler, Anne Morvan, Nourhan Sakr, Tams Sarls, Jamal Atif |
| 2017 | Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains. | Andrew An Bian, Baharan Mirzasoleiman, Joachim M. Buhmann, Andreas Krause |
| 2017 | Frequency Domain Predictive Modelling with Aggregated Data. | Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo |
| 2017 | Active Positive Semidefinite Matrix Completion: Algorithms, Theory and Applications. | Aniruddha Bhargava, Ravi Ganti, Robert D. Nowak |
| 2017 | Distribution of Gaussian Process Arc Lengths. | Justin Bewsher, Alessandra Tosi, Michael A. Osborne, Stephen J. Roberts |
| 2017 | Local Perturb-and-MAP for Structured Prediction. | Gedas Bertasius, Qiang Liu, Lorenzo Torresani, Jianbo Shi |
| 2017 | Random projection design for scalable implicit smoothing of randomly observed stochastic processes. | Francois Belletti, Evan Randall Sparks, Alexandre M. Bayen, Joseph Gonzalez |
| 2017 | Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation. | Sohail Bahmani, Justin Romberg |
3,701–3,725 of 4,516← PreviousNext →
Comparable venues
Other A*/A conferences filed under the same field of research.
- A*ICMLInternational Conference on Machine Learning
- A*ICLRInternational Conference on Learning Representations
- A*COLTConference on Learning Theory
- APPSNParallel Problem Solving from Nature
- AECML PKDDEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (PKDD and ECML combined from 2008)
- A*NeurIPSAdvances in Neural Information Processing Systems (was NIPS)