Vakili, Sattar

27 publications

ICML 2025 Bayesian Optimization from Human Feedback: Near-Optimal Regret Bounds Aya Kayal, Sattar Vakili, Laura Toni, Da-Shan Shiu, Alberto Bernacchia

AISTATS 2025 Near-Optimal Sample Complexity in Reward-Free Kernel-Based Reinforcement Learning Aya Kayal, Sattar Vakili, Laura Toni, Alberto Bernacchia

NeurIPS 2025 No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes Jasmine Bayrooti, Sattar Vakili, Amanda Prorok, Carl Henrik Ek

ALT 2024 Adversarial Contextual Bandits Go Kernelized Gergely Neu, Julia Olkhovskaya, Sattar Vakili

NeurIPS 2024 Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm Sattar Vakili, Julia Olkhovskaya

COLT 2024 Open Problem: Order Optimal Regret Bounds for Kernel-Based Reinforcement Learning Sattar Vakili

ALT 2024 Optimal Regret Bounds for Collaborative Learning in Bandits Amitis Shidani, Sattar Vakili

ICML 2024 Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency Sudeep Salgia, Sattar Vakili, Qing Zhao

ICML 2024 Reward-Free Kernel-Based Reinforcement Learning Sattar Vakili, Farhang Nabiei, Da-Shan Shiu, Alberto Bernacchia

NeurIPSW 2024 Trieste: Efficiently Exploring the Depths of Black-Box Functions with TensorFlow Henry Moss, Victor Picheny, Hrvoje Stojic, Sebastian W. Ober, Artem Artemev, Andrei Paleyes, Sattar Vakili, Stratis Markou, Jixiang Qing, Nasrulloh Ratu Bagus Satrio Loka, Ivo Couckuyt

ICML 2023 Delayed Feedback in Kernel Bandits Sattar Vakili, Danyal Ahmed, Alberto Bernacchia, Ciara Pike-Burke

ICLR 2023 Fisher-Legendre (FishLeg) Optimization of Deep Neural Networks Jezabel R Garcia, Federica Freddi, Stathi Fotiadis, Maolin Li, Sattar Vakili, Alberto Bernacchia, Guillaume Hennequin

ICML 2023 Image Generation with Shortest Path Diffusion Ayan Das, Stathi Fotiadis, Anil Batra, Farhang Nabiei, Fengting Liao, Sattar Vakili, Da-Shan Shiu, Alberto Bernacchia

NeurIPS 2023 Kernelized Reinforcement Learning with Order Optimal Regret Bounds Sattar Vakili, Julia Olkhovskaya

AISTATS 2023 Sample Complexity of Kernel-Based Q-Learning Sing-Yuan Yeh, Fu-Chieh Chang, Chang-Wei Yueh, Pei-Yuan Wu, Alberto Bernacchia, Sattar Vakili

NeurIPSW 2022 Gradient Descent: Robustness to Adversarial Corruption Fu-Chieh Chang, Farhang Nabiei, Pei-Yuan Wu, Alexandru Cioba, Sattar Vakili, Alberto Bernacchia

ICML 2022 Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning Sattar Vakili, Jonathan Scarlett, Da-Shan Shiu, Alberto Bernacchia

NeurIPS 2022 Near-Optimal Collaborative Learning in Bandits Clémence Réda, Sattar Vakili, Emilie Kaufmann

COLT 2022 Open Problem: Regret Bounds for Noise-Free Kernel-Based Bandits Sattar Vakili

AISTATS 2021 On Information Gain and Regret Bounds in Gaussian Process Bandits Sattar Vakili, Kia Khezeli, Victor Picheny

NeurIPS 2021 A Domain-Shrinking Based Bayesian Optimization Algorithm with Order-Optimal Regret Performance Sudeep Salgia, Sattar Vakili, Qing Zhao

COLT 2021 Open Problem: Tight Online Confidence Intervals for RKHS Elements Sattar Vakili, Jonathan Scarlett, Tara Javidi

NeurIPS 2021 Optimal Order Simple Regret for Gaussian Process Bandits Sattar Vakili, Nacime Bouziani, Sepehr Jalali, Alberto Bernacchia, Da-shan Shiu

NeurIPS 2021 Scalable Thompson Sampling Using Sparse Gaussian Process Models Sattar Vakili, Henry Moss, Artem Artemev, Vincent Dutordoir, Victor Picheny

UAI 2020 Amortized Variance Reduction for Doubly Stochastic Objective Ayman Boustati, Sattar Vakili, James Hensman, St John

ICML 2020 Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization Sudeep Salgia, Qing Zhao, Sattar Vakili

ICML 2019 Adaptive Sensor Placement for Continuous Spaces James Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David Leslie, Sattar Vakili, Enrique Munoz De Cote