| 2025 | AISTATS | Near-Optimal Sample Complexity in Reward-Free Kernel-based Reinforcement Learning. | Aya Kayal, Sattar Vakili, Laura Toni, Alberto Bernacchia |
| 2025 | ICML | Bayesian Optimization from Human Feedback: Near-Optimal Regret Bounds. | Aya Kayal, Sattar Vakili, Laura Toni, Da-shan Shiu, Alberto Bernacchia |
| 2024 | ALT | Adversarial Contextual Bandits Go Kernelized. | Gergely Neu, Julia Olkhovskaya, Sattar Vakili |
| 2024 | ALT | Optimal Regret Bounds for Collaborative Learning in Bandits. | Amitis Shidani, Sattar Vakili |
| 2024 | COLT | Open Problem: Order Optimal Regret Bounds for Kernel-Based Reinforcement Learning. | Sattar Vakili |
| 2024 | ICML | Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency. | Sudeep Salgia, Sattar Vakili, Qing Zhao |
| 2024 | ICML | Reward-Free Kernel-Based Reinforcement Learning. | Sattar Vakili, Farhang Nabiei, Da-shan Shiu, Alberto Bernacchia |
| 2023 | AISTATS | Sample Complexity of Kernel-Based Q-Learning. | Sing-Yuan Yeh, Fu-Chieh Chang, Chang-Wei Yueh, Pei-Yuan Wu, Alberto Bernacchia, Sattar Vakili |
| 2023 | GLOBECOM | Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling. | Ushnish Sengupta, Chinkuo Jao, Alberto Bernacchia, Sattar Vakili, Da-shan Shiu |
| 2023 | ICLR | Fisher-Legendre (FishLeg) optimization of deep neural networks. | Jezabel R. Garcia, Federica Freddi, Stathi Fotiadis, Maolin Li, Sattar Vakili, Alberto Bernacchia, Guillaume Hennequin |
| 2023 | ICML | Image generation with shortest path diffusion. | Ayan Das, Stathi Fotiadis, Anil Batra, Farhang Nabiei, Fengting Liao, Sattar Vakili, Da-Shan Shiu, Alberto Bernacchia |
| 2023 | ICML | Delayed Feedback in Kernel Bandits. | Sattar Vakili, Danyal Ahmed, Alberto Bernacchia, Ciara Pike-Burke |
| 2023 | ISIT | Information Gain and Uniform Generalization Bounds for Neural Kernel Models. | Sattar Vakili, Michael Bromberg, Jezabel R. Garcia, Da-Shan Shiu, Alberto Bernacchia |
| 2022 | ICML | Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning. | Sattar Vakili, Jonathan Scarlett, Da-Shan Shiu, Alberto Bernacchia |
| 2021 | AISTATS | On Information Gain and Regret Bounds in Gaussian Process Bandits. | Sattar Vakili, Kia Khezeli, Victor Picheny |
| 2021 | COLT | Open Problem: Tight Online Confidence Intervals for RKHS Elements. | Sattar Vakili, Jonathan Scarlett, Tara Javidi |
| 2020 | ICML | Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization. | Sudeep Salgia, Qing Zhao, Sattar Vakili |
| 2020 | UAI | Amortized variance reduction for doubly stochastic objective. | Ayman Boustati, Sattar Vakili, James Hensman, S. T. John |
| 2019 | ICML | Adaptive Sensor Placement for Continuous Spaces. | James A. Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David S. Leslie, Sattar Vakili, Enrique Munoz de Cote |
| 2019 | ISIT | A Random Walk Approach to First-Order Stochastic Convex Optimization. | Sattar Vakili, Qing Zhao |
| 2018 | ICASSP | Hierarchical Heavy Hitter Detection Under Unknown Models. | Sattar Vakili, Qing Zhao, Chang Liu, Chen-Nee Chuah |
| 2015 | ICASSP | Risk-averse online learning under mean-variance measures. | Sattar Vakili, Qing Zhao |
| 2015 | ICASSP | Quickest detection of short-term voltage instability with PMU measurements. | Sattar Vakili, Qing Zhao, Lang Tong |
| 2014 | ACSSC | Time-varying stochastic multi-armed bandit problems. | Sattar Vakili, Qing Zhao, Yuan Zhou |
| 2013 | ACSSC | Distributed node-weighted connected dominating set problems. | Sattar Vakili, Qing Zhao |
| 2013 | ACSSC | Achieving complete learning in Multi-Armed Bandit problems. | Sattar Vakili, Qing Zhao |