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ORCIDSattar Vakili
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2020 – today
- 2024
[c35]Gergely Neu, Julia Olkhovskaya, Sattar Vakili:
Adversarial Contextual Bandits Go Kernelized. ALT 2024: 907-929- [c34]Amitis Shidani, Sattar Vakili:
Optimal Regret Bounds for Collaborative Learning in Bandits. ALT 2024: 1013-1029 - [c33]Sattar Vakili:
Open Problem: Order Optimal Regret Bounds for Kernel-Based Reinforcement Learning. COLT 2024: 5340-5344 - [c32]Sudeep Salgia, Sattar Vakili, Qing Zhao:
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency. ICML 2024 - [c31]Sattar Vakili, Farhang Nabiei, Da-shan Shiu, Alberto Bernacchia:
Reward-Free Kernel-Based Reinforcement Learning. ICML 2024 - [i31]Sattar Vakili:
Open Problem: Order Optimal Regret Bounds for Kernel-Based Reinforcement Learning. CoRR abs/2406.15250 (2024) - 2023
- [j4]Sudeep Salgia
, Sattar Vakili, Qing Zhao:
Collaborative Learning in Kernel-Based Bandits for Distributed Users. IEEE Trans. Signal Process. 71: 3956-3967 (2023) - [c30]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: 453-469 - [c29]Ushnish Sengupta, Chinkuo Jao, Alberto Bernacchia, Sattar Vakili, Da-shan Shiu:
Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling. GLOBECOM 2023: 4779-4784 - [c28]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 - [c27]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: 7009-7024 - [c26]Sattar Vakili, Danyal Ahmed, Alberto Bernacchia, Ciara Pike-Burke:
Delayed Feedback in Kernel Bandits. ICML 2023: 34779-34792 - [c25]Sattar Vakili, Michael Bromberg, Jezabel R. Garcia, Da-Shan Shiu, Alberto Bernacchia:
Information Gain and Uniform Generalization Bounds for Neural Kernel Models. ISIT 2023: 555-560 - [c24]Sattar Vakili, Julia Olkhovskaya:
Kernelized Reinforcement Learning with Order Optimal Regret Bounds. NeurIPS 2023 - [i30]Sattar Vakili, Danyal Ahmed, Alberto Bernacchia, Ciara Pike-Burke:
Delayed Feedback in Kernel Bandits. CoRR abs/2302.00392 (2023) - [i29]Sing-Yuan Yeh, Fu-Chieh Chang, Chang-Wei Yueh, Pei-Yuan Wu, Alberto Bernacchia, Sattar Vakili:
Sample Complexity of Kernel-Based Q-Learning. CoRR abs/2302.00727 (2023) - [i28]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 abs/2302.08436 (2023) - [i27]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 abs/2306.00501 (2023) - [i26]Sattar Vakili, Julia Olkhovskaya:
Kernelized Reinforcement Learning with Order Optimal Regret Bounds. CoRR abs/2306.07745 (2023) - [i25]Ushnish Sengupta, Chinkuo Jao, Alberto Bernacchia, Sattar Vakili, Da-shan Shiu:
Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling. CoRR abs/2308.05583 (2023) - [i24]Gergely Neu, Julia Olkhovskaya, Sattar Vakili:
Adversarial Contextual Bandits Go Kernelized. CoRR abs/2310.01609 (2023) - [i23]Sudeep Salgia, Sattar Vakili, Qing Zhao:
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency. CoRR abs/2310.15351 (2023) - [i22]Wei Wang, Sattar Vakili, Ilija Bogunovic:
Robust Best-arm Identification in Linear Bandits. CoRR abs/2311.04731 (2023) - [i21]Amitis Shidani, Sattar Vakili:
Optimal Regret Bounds for Collaborative Learning in Bandits. CoRR abs/2312.09674 (2023) - 2022
- [c23]Sattar Vakili, Jonathan Scarlett, Da-Shan Shiu, Alberto Bernacchia:
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning. ICML 2022: 21960-21983 - [c22]Clémence Réda, Sattar Vakili, Emilie Kaufmann:
Near-Optimal Collaborative Learning in Bandits. NeurIPS 2022 - [i20]Sattar Vakili, Jonathan Scarlett, Da-Shan Shiu, Alberto Bernacchia:
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning. CoRR abs/2202.04005 (2022) - [i19]Sudeep Salgia, Sattar Vakili, Qing Zhao:
Provably and Practically Efficient Neural Contextual Bandits. CoRR abs/2206.00099 (2022) - [i18]Clémence Réda, Sattar Vakili, Emilie Kaufmann:
Near-Optimal Collaborative Learning in Bandits. CoRR abs/2206.00121 (2022) - [i17]Sudeep Salgia, Sattar Vakili, Qing Zhao:
Kernel-based Federated Learning with Personalization. CoRR abs/2207.07948 (2022) - 2021
- [c21]Sattar Vakili, Kia Khezeli, Victor Picheny:
On Information Gain and Regret Bounds in Gaussian Process Bandits. AISTATS 2021: 82-90 - [c20]Filipo Studzinski Perotto, Sattar Vakili, Pratik Gajane, Yaser Faghan, Mathieu Bourgais:
Gambler Bandits and the Regret of Being Ruined. AAMAS 2021: 1664-1667 - [c19]Sattar Vakili, Jonathan Scarlett, Tara Javidi:
Open Problem: Tight Online Confidence Intervals for RKHS Elements. COLT 2021: 4647-4652 - [c18]Sattar Vakili, Henry B. Moss, Artem Artemev, Vincent Dutordoir, Victor Picheny:
Scalable Thompson Sampling using Sparse Gaussian Process Models. NeurIPS 2021: 5631-5643 - [c17]Sattar Vakili, Nacime Bouziani, Sepehr Jalali, Alberto Bernacchia, Da-Shan Shiu:
Optimal Order Simple Regret for Gaussian Process Bandits. NeurIPS 2021: 21202-21215 - [c16]Sudeep Salgia, Sattar Vakili, Qing Zhao:
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance. NeurIPS 2021: 28836-28847 - [i16]Sattar Vakili, Nacime Bouziani, Sepehr Jalali, Alberto Bernacchia, Da-Shan Shiu:
Optimal Order Simple Regret for Gaussian Process Bandits. CoRR abs/2108.09262 (2021) - [i15]Sattar Vakili, Michael Bromberg, Da-Shan Shiu, Alberto Bernacchia:
Uniform Generalization Bounds for Overparameterized Neural Networks. CoRR abs/2109.06099 (2021) - [i14]Sattar Vakili, Jonathan Scarlett, Tara Javidi:
Open Problem: Tight Online Confidence Intervals for RKHS Elements. CoRR abs/2110.15458 (2021) - 2020
- [j3]Xiao Xu, Sattar Vakili, Qing Zhao, Ananthram Swami:
Multi-Armed Bandits on Partially Revealed Unit Interval Graphs. IEEE Trans. Netw. Sci. Eng. 7(3): 1453-1465 (2020) - [c15]Sudeep Salgia, Qing Zhao, Sattar Vakili:
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization. ICML 2020: 8427-8437 - [c14]Ayman Boustati, Sattar Vakili, James Hensman, S. T. John:
Amortized variance reduction for doubly stochastic objective. UAI 2020: 61-70 - [i13]Sattar Vakili, Victor Picheny, Nicolas Durrande:
Regret Bounds for Noise-Free Bayesian Optimization. CoRR abs/2002.05096 (2020) - [i12]Ayman Boustati, Sattar Vakili, James Hensman, S. T. John:
Amortized variance reduction for doubly stochastic objectives. CoRR abs/2003.04125 (2020) - [i11]Sudeep Salgia, Qing Zhao, Sattar Vakili:
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization. CoRR abs/2003.05482 (2020) - [i10]Sattar Vakili, Victor Picheny, Artem Artemev:
Scalable Thompson Sampling using Sparse Gaussian Process Models. CoRR abs/2006.05356 (2020) - [i9]Sattar Vakili, Kia Khezeli, Victor Picheny:
On Information Gain and Regret Bounds in Gaussian Process Bandits. CoRR abs/2009.06966 (2020) - [i8]Sudeep Salgia, Sattar Vakili, Qing Zhao:
A Computationally Efficient Approach to Black-box Optimization using Gaussian Process Models. CoRR abs/2010.13997 (2020)
2010 – 2019
- 2019
- [c13]Sattar Vakili, Sudeep Salgia, Qing Zhao:
Stochastic Gradient Descent on a Tree: an Adaptive and Robust Approach to Stochastic Convex Optimization. Allerton 2019: 432-438 - [c12]Sattar Vakili, Alexis Boukouvalas, Qing Zhao:
Decision Variance in Risk-Averse Online Learning. CDC 2019: 2738-2744 - [c11]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: 2385-2393 - [c10]Sattar Vakili, Qing Zhao:
A Random Walk Approach to First-Order Stochastic Convex Optimization. ISIT 2019: 395-399 - [i7]Sattar Vakili, Qing Zhao:
A Random Walk Approach to First-Order Stochastic Convex Optimization. CoRR abs/1901.05947 (2019) - [i6]James A. Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David S. Leslie, Sattar Vakili, Enrique Munoz de Cote:
Adaptive Sensor Placement for Continuous Spaces. CoRR abs/1905.06821 (2019) - [i5]Victor Picheny, Sattar Vakili, Artem Artemev:
Ordinal Bayesian Optimisation. CoRR abs/1912.02493 (2019) - 2018
- [c9]Sattar Vakili, Qing Zhao, Chang Liu, Chen-Nee Chuah:
Hierarchical Heavy Hitter Detection Under Unknown Models. ICASSP 2018: 6917-6921 - [i4]Xiao Xu, Sattar Vakili, Qing Zhao, Ananthram Swami:
Multi-Armed Bandits on Unit Interval Graphs. CoRR abs/1802.04339 (2018) - [i3]Sattar Vakili, Alexis Boukouvalas:
Decision Variance in Online Learning. CoRR abs/1807.09089 (2018) - 2017
- [c8]Xiao Xu, Sattar Vakili, Qing Zhao, Ananthram Swami:
Online learning with side information. MILCOM 2017: 303-308 - [i2]Sattar Vakili, Qing Zhao, Chang Liu, Chen-Nee Chuah:
Anomaly Detection in Hierarchical Data Streams under Unknown Models. CoRR abs/1709.03573 (2017) - 2016
- [j2]Sattar Vakili, Qing Zhao:
Risk-Averse Multi-Armed Bandit Problems Under Mean-Variance Measure. IEEE J. Sel. Top. Signal Process. 10(6): 1093-1111 (2016) - [i1]Sattar Vakili, Qing Zhao:
Risk-Averse Multi-Armed Bandit Problems under Mean-Variance Measure. CoRR abs/1604.05257 (2016) - 2015
- [c7]Sattar Vakili, Qing Zhao:
Mean-variance and value at risk in multi-armed bandit problems. Allerton 2015: 1330-1335 - [c6]Sattar Vakili, Qing Zhao, Lang Tong:
Bayesian quickest short-term voltage instability detection in power systems. CDC 2015: 7214-7219 - [c5]Sattar Vakili, Qing Zhao:
Risk-averse online learning under mean-variance measures. ICASSP 2015: 1911-1915 - [c4]Sattar Vakili, Qing Zhao, Lang Tong:
Quickest detection of short-term voltage instability with PMU measurements. ICASSP 2015: 3901-3905 - 2014
- [c3]Sattar Vakili, Qing Zhao, Yuan Zhou:
Time-varying stochastic multi-armed bandit problems. ACSSC 2014: 2103-2107 - 2013
- [j1]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): 759-767 (2013) - [c2]Sattar Vakili, Qing Zhao:
Distributed node-weighted connected dominating set problems. ACSSC 2013: 238-241 - [c1]Sattar Vakili, Qing Zhao:
Achieving complete learning in Multi-Armed Bandit problems. ACSSC 2013: 1778-1782
Coauthor Index
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last updated on 2024-09-04 00:32 CEST by the dblp team
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