Sattar Vakili

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 64

Top Topics

Gaussian Process

Bandit Problems

Top Venues

Publications

Sattar Vakili
Open Problem: Order Optimal Regret Bounds for Kernel-Based Reinforcement Learning. COLT (2024)
Sattar Vakili
Open Problem: Order Optimal Regret Bounds for Kernel-Based Reinforcement Learning. CoRR (2024)
Amitis Shidani, Sattar Vakili
Optimal Regret Bounds for Collaborative Learning in Bandits. ALT (2024)
Gergely Neu, Julia Olkhovskaya, Sattar Vakili
Adversarial Contextual Bandits Go Kernelized. ALT (2024)
Ushnish Sengupta, Chinkuo Jao, Alberto Bernacchia, Sattar Vakili, Da-shan Shiu
Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling. GLOBECOM (2023)
Sattar Vakili, Michael Bromberg, Jezabel R. Garcia, Da-Shan Shiu, Alberto Bernacchia
Information Gain and Uniform Generalization Bounds for Neural Kernel Models. ISIT (2023)
Wei Wang, Sattar Vakili, Ilija Bogunovic
Robust Best-arm Identification in Linear Bandits. CoRR (2023)
Victor Picheny, Joel Berkeley, Henry B. Moss, Hrvoje Stojic, Uri Granta, Sebastian W. Ober, Artem Artemev, Khurram Ghani, Alexander Goodall, Andrei Paleyes, Sattar Vakili, Sergio Pascual-Diaz, Stratis Markou, Jixiang Qing, Nasrulloh R. B. S. Loka, Ivo Couckuyt
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow. CoRR (2023)
Sing-Yuan Yeh, Fu-Chieh Chang, Chang-Wei Yueh, Pei-Yuan Wu, Alberto Bernacchia, Sattar Vakili
Sample Complexity of Kernel-Based Q-Learning. AISTATS (2023)
Jezabel R. Garcia, Federica Freddi, Stathi Fotiadis, Maolin Li, Sattar Vakili, Alberto Bernacchia, Guillaume Hennequin
Fisher-Legendre (FishLeg) optimization of deep neural networks. ICLR (2023)
Ushnish Sengupta, Chinkuo Jao, Alberto Bernacchia, Sattar Vakili, Da-shan Shiu
Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling. CoRR (2023)
Gergely Neu, Julia Olkhovskaya, Sattar Vakili
Adversarial Contextual Bandits Go Kernelized. CoRR (2023)
Sing-Yuan Yeh, Fu-Chieh Chang, Chang-Wei Yueh, Pei-Yuan Wu, Alberto Bernacchia, Sattar Vakili
Sample Complexity of Kernel-Based Q-Learning. CoRR (2023)
Sudeep Salgia, Sattar Vakili, Qing Zhao
Collaborative Learning in Kernel-Based Bandits for Distributed Users. IEEE Trans. Signal Process. 71 (2023)
Sattar Vakili, Julia Olkhovskaya
Kernelized Reinforcement Learning with Order Optimal Regret Bounds. CoRR (2023)
Ayan Das, Stathi Fotiadis, Anil Batra, Farhang Nabiei, Fengting Liao, Sattar Vakili, Da-Shan Shiu, Alberto Bernacchia
Image generation with shortest path diffusion. ICML (2023)
Amitis Shidani, Sattar Vakili
Optimal Regret Bounds for Collaborative Learning in Bandits. CoRR (2023)
Sattar Vakili, Danyal Ahmed, Alberto Bernacchia, Ciara Pike-Burke
Delayed Feedback in Kernel Bandits. ICML (2023)
Sattar Vakili, Julia Olkhovskaya
Kernelized Reinforcement Learning with Order Optimal Regret Bounds. NeurIPS (2023)
Ayan Das, Stathi Fotiadis, Anil Batra, Farhang Nabiei, Feng-Ting Liao, Sattar Vakili, Da-shan Shiu, Alberto Bernacchia
Image generation with shortest path diffusion. CoRR (2023)
Sudeep Salgia, Sattar Vakili, Qing Zhao
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency. CoRR (2023)
Sattar Vakili, Danyal Ahmed, Alberto Bernacchia, Ciara Pike-Burke
Delayed Feedback in Kernel Bandits. CoRR (2023)
Sudeep Salgia, Sattar Vakili, Qing Zhao
Kernel-based Federated Learning with Personalization. CoRR (2022)
Sattar Vakili, Jonathan Scarlett, Da-shan Shiu, Alberto Bernacchia
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning. CoRR (2022)
Clémence Réda, Sattar Vakili, Emilie Kaufmann
Near-Optimal Collaborative Learning in Bandits. CoRR (2022)
Clémence Réda, Sattar Vakili, Emilie Kaufmann
Near-Optimal Collaborative Learning in Bandits. NeurIPS (2022)
Sattar Vakili, Jonathan Scarlett, Da-Shan Shiu, Alberto Bernacchia
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning. ICML (2022)
Sudeep Salgia, Sattar Vakili, Qing Zhao
Provably and Practically Efficient Neural Contextual Bandits. CoRR (2022)
Sattar Vakili, Henry B. Moss, Artem Artemev, Vincent Dutordoir, Victor Picheny
Scalable Thompson Sampling using Sparse Gaussian Process Models. NeurIPS (2021)
Sattar Vakili, Jonathan Scarlett, Tara Javidi
Open Problem: Tight Online Confidence Intervals for RKHS Elements. CoRR (2021)
Sattar Vakili, Jonathan Scarlett, Tara Javidi
Open Problem: Tight Online Confidence Intervals for RKHS Elements. COLT (2021)
Sattar Vakili, Nacime Bouziani, Sepehr Jalali, Alberto Bernacchia, Da-Shan Shiu
Optimal Order Simple Regret for Gaussian Process Bandits. NeurIPS (2021)
Sattar Vakili, Michael Bromberg, Da-shan Shiu, Alberto Bernacchia
Uniform Generalization Bounds for Overparameterized Neural Networks. CoRR (2021)
Sattar Vakili, Kia Khezeli, Victor Picheny
On Information Gain and Regret Bounds in Gaussian Process Bandits. AISTATS (2021)
Filipo Studzinski Perotto, Sattar Vakili, Pratik Gajane, Yaser Faghan, Mathieu Bourgais
Gambler Bandits and the Regret of Being Ruined. AAMAS (2021)
Sattar Vakili, Nacime Bouziani, Sepehr Jalali, Alberto Bernacchia, Da-Shan Shiu
Optimal Order Simple Regret for Gaussian Process Bandits. CoRR (2021)
Sudeep Salgia, Sattar Vakili, Qing Zhao
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance. NeurIPS (2021)
Sattar Vakili, Victor Picheny, Artem Artemev
Scalable Thompson Sampling using Sparse Gaussian Process Models. CoRR (2020)
Ayman Boustati, Sattar Vakili, James Hensman, S. T. John
Amortized variance reduction for doubly stochastic objectives. CoRR (2020)
Sudeep Salgia, Qing Zhao, Sattar Vakili
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization. ICML (2020)
Xiao Xu, Sattar Vakili, Qing Zhao, Ananthram Swami
Multi-Armed Bandits on Partially Revealed Unit Interval Graphs. IEEE Trans. Netw. Sci. Eng. 7 (3) (2020)
Sattar Vakili, Victor Picheny, Nicolas Durrande
Regret Bounds for Noise-Free Bayesian Optimization. CoRR (2020)
Sudeep Salgia, Sattar Vakili, Qing Zhao
A Computationally Efficient Approach to Black-box Optimization using Gaussian Process Models. CoRR (2020)
Sudeep Salgia, Qing Zhao, Sattar Vakili
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization. CoRR (2020)
Sattar Vakili, Kia Khezeli, Victor Picheny
On Information Gain and Regret Bounds in Gaussian Process Bandits. CoRR (2020)
Ayman Boustati, Sattar Vakili, James Hensman, S. T. John
Amortized variance reduction for doubly stochastic objective. UAI (2020)
James A. Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David S. Leslie, Sattar Vakili, Enrique Munoz de Cote
Adaptive Sensor Placement for Continuous Spaces. ICML (2019)
Sattar Vakili, Alexis Boukouvalas, Qing Zhao
Decision Variance in Risk-Averse Online Learning. CDC (2019)
James A. Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David S. Leslie, Sattar Vakili, Enrique Munoz de Cote
Adaptive Sensor Placement for Continuous Spaces. CoRR (2019)
Sattar Vakili, Qing Zhao
A Random Walk Approach to First-Order Stochastic Convex Optimization. CoRR (2019)
Victor Picheny, Sattar Vakili, Artem Artemev
Ordinal Bayesian Optimisation. CoRR (2019)
Sattar Vakili, Sudeep Salgia, Qing Zhao
Stochastic Gradient Descent on a Tree: an Adaptive and Robust Approach to Stochastic Convex Optimization. Allerton (2019)
Sattar Vakili, Qing Zhao
A Random Walk Approach to First-Order Stochastic Convex Optimization. ISIT (2019)
Sattar Vakili, Alexis Boukouvalas
Decision Variance in Online Learning. CoRR (2018)
Xiao Xu, Sattar Vakili, Qing Zhao, Ananthram Swami
Multi-Armed Bandits on Unit Interval Graphs. CoRR (2018)
Sattar Vakili, Qing Zhao, Chang Liu, Chen-Nee Chuah
Hierarchical Heavy Hitter Detection Under Unknown Models. ICASSP (2018)
Sattar Vakili, Qing Zhao, Chang Liu, Chen-Nee Chuah
Anomaly Detection in Hierarchical Data Streams under Unknown Models. CoRR (2017)
Xiao Xu, Sattar Vakili, Qing Zhao, Ananthram Swami
Online learning with side information. MILCOM (2017)
Sattar Vakili, Qing Zhao
Risk-Averse Multi-Armed Bandit Problems Under Mean-Variance Measure. IEEE J. Sel. Top. Signal Process. 10 (6) (2016)
Sattar Vakili, Qing Zhao
Risk-Averse Multi-Armed Bandit Problems under Mean-Variance Measure. CoRR (2016)
Sattar Vakili, Qing Zhao, Lang Tong
Bayesian quickest short-term voltage instability detection in power systems. CDC (2015)
Sattar Vakili, Qing Zhao, Lang Tong
Quickest detection of short-term voltage instability with PMU measurements. ICASSP (2015)
Sattar Vakili, Qing Zhao
Risk-averse online learning under mean-variance measures. ICASSP (2015)
Sattar Vakili, Qing Zhao
Mean-variance and value at risk in multi-armed bandit problems. Allerton (2015)
Sattar Vakili, Qing Zhao, Yuan Zhou
Time-varying stochastic multi-armed bandit problems. ACSSC (2014)
Sattar Vakili, Keqin Liu, Qing Zhao
Deterministic Sequencing of Exploration and Exploitation for Multi-Armed Bandit Problems. IEEE J. Sel. Top. Signal Process. 7 (5) (2013)
Sattar Vakili, Qing Zhao
Achieving complete learning in Multi-Armed Bandit problems. ACSSC (2013)
Sattar Vakili, Qing Zhao
Distributed node-weighted connected dominating set problems. ACSSC (2013)