Distributed node-weighted connected dominating set problems. - Sattar Vakili; Qing Zhao 001 (ACSSC, 2013)

Achieving complete learning in Multi-Armed Bandit problems. - Sattar Vakili; Qing Zhao 001 (ACSSC, 2013)

Deterministic Sequencing of Exploration and Exploitation for Multi-Armed Bandit Problems. - Sattar Vakili; Keqin Liu; Qing Zhao 001 (IEEE J. Sel. Top. Signal Process., 2013)

Time-varying stochastic multi-armed bandit problems. - Sattar Vakili; Qing Zhao 001; Yuan Zhou 002 (ACSSC, 2014)

Mean-variance and value at risk in multi-armed bandit problems. - Sattar Vakili; Qing Zhao 001 (Allerton, 2015)

Bayesian quickest short-term voltage instability detection in power systems. - Sattar Vakili; Qing Zhao 001; Lang Tong 001 (CDC, 2015)

Risk-averse online learning under mean-variance measures. - Sattar Vakili; Qing Zhao 001 (ICASSP, 2015)

Quickest detection of short-term voltage instability with PMU measurements. - Sattar Vakili; Qing Zhao 001; Lang Tong 001 (ICASSP, 2015)

Risk-Averse Multi-Armed Bandit Problems Under Mean-Variance Measure. - Sattar Vakili; Qing Zhao 001 (IEEE J. Sel. Top. Signal Process., 2016)

Online learning with side information. - Xiao Xu; Sattar Vakili; Qing Zhao 001; Ananthram Swami (MILCOM, 2017)

Anomaly Detection in Hierarchical Data Streams under Unknown Models. - Sattar Vakili; Qing Zhao 001; Chang Liu; Chen-Nee Chuah (CoRR, 2017)

Hierarchical Heavy Hitter Detection Under Unknown Models. - Sattar Vakili; Qing Zhao 001; Chang Liu; Chen-Nee Chuah (ICASSP, 2018)

Multi-Armed Bandits on Unit Interval Graphs. - Xiao Xu; Sattar Vakili; Qing Zhao 001; Ananthram Swami (CoRR, 2018)

Decision Variance in Online Learning. - Sattar Vakili; Alexis Boukouvalas (CoRR, 2018)

Stochastic Gradient Descent on a Tree: an Adaptive and Robust Approach to Stochastic Convex Optimization. - Sattar Vakili; Sudeep Salgia; Qing Zhao 001 (Allerton, 2019)

Decision Variance in Risk-Averse Online Learning. - Sattar Vakili; Alexis Boukouvalas; Qing Zhao 001 (CDC, 2019)

Adaptive Sensor Placement for Continuous Spaces. - James A. Grant; Alexis Boukouvalas; Ryan-Rhys Griffiths; David S. Leslie; Sattar Vakili; Enrique Munoz de Cote (ICML, 2019)

A Random Walk Approach to First-Order Stochastic Convex Optimization. - Sattar Vakili; Qing Zhao 001 (ISIT, 2019)

Ordinal Bayesian Optimisation. - Victor Picheny; Sattar Vakili; Artem Artemev (CoRR, 2019)

Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization. - Sudeep Salgia; Qing Zhao 001; Sattar Vakili (ICML, 2020)

Amortized variance reduction for doubly stochastic objective. - Ayman Boustati; Sattar Vakili; James Hensman; S. T. John (UAI, 2020)

Regret Bounds for Noise-Free Bayesian Optimization. - Sattar Vakili; Victor Picheny; Nicolas Durrande (CoRR, 2020)

Amortized variance reduction for doubly stochastic objectives. - Ayman Boustati; Sattar Vakili; James Hensman; S. T. John (CoRR, 2020)

A Computationally Efficient Approach to Black-box Optimization using Gaussian Process Models. - Sudeep Salgia; Sattar Vakili; Qing Zhao 001 (CoRR, 2020)

Multi-Armed Bandits on Partially Revealed Unit Interval Graphs. - Xiao Xu; Sattar Vakili; Qing Zhao 001; Ananthram Swami (IEEE Trans. Netw. Sci. Eng., 2020)

On Information Gain and Regret Bounds in Gaussian Process Bandits. - Sattar Vakili; Kia Khezeli; Victor Picheny (AISTATS, 2021)

Gambler Bandits and the Regret of Being Ruined. - Filipo Studzinski Perotto; Sattar Vakili; Pratik Gajane; Yaser Faghan; Mathieu Bourgais (AAMAS, 2021)

Open Problem: Tight Online Confidence Intervals for RKHS Elements. - Sattar Vakili; Jonathan Scarlett; Tara Javidi (COLT, 2021)

A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance. - Sudeep Salgia; Sattar Vakili; Qing Zhao 001 (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 B. Moss; Artem Artemev; Vincent Dutordoir; Victor Picheny (NeurIPS, 2021)

Uniform Generalization Bounds for Overparameterized Neural Networks. - Sattar Vakili; Michael Bromberg; Da-Shan Shiu; Alberto Bernacchia (CoRR, 2021)

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

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

Provably and Practically Efficient Neural Contextual Bandits. - Sudeep Salgia; Sattar Vakili; Qing Zhao 001 (CoRR, 2022)

Kernel-based Federated Learning with Personalization. - Sudeep Salgia; Sattar Vakili; Qing Zhao 001 (CoRR, 2022)

Sample Complexity of Kernel-Based Q-Learning. - Sing-Yuan Yeh; Fu-Chieh Chang 001; Chang-Wei Yueh; Pei-Yuan Wu; Alberto Bernacchia; Sattar Vakili (AISTATS, 2023)

Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling. - Ushnish Sengupta; Chinkuo Jao; Alberto Bernacchia; Sattar Vakili; Da-shan Shiu (GLOBECOM, 2023)

Fisher-Legendre (FishLeg) optimization of deep neural networks. - Jezabel R. Garcia; Federica Freddi; Stathi Fotiadis; Maolin Li; Sattar Vakili; Alberto Bernacchia; Guillaume Hennequin (ICLR, 2023)

Image generation with shortest path diffusion. - Ayan Das 005; Stathi Fotiadis; Anil Batra; Farhang Nabiei; Fengting Liao; Sattar Vakili; Da-Shan Shiu; Alberto Bernacchia (ICML, 2023)

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

Information Gain and Uniform Generalization Bounds for Neural Kernel Models. - Sattar Vakili; Michael Bromberg; Jezabel R. Garcia; Da-Shan Shiu; Alberto Bernacchia (ISIT, 2023)

Kernelized Reinforcement Learning with Order Optimal Regret Bounds. - Sattar Vakili; Julia Olkhovskaya (NeurIPS, 2023)

Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow. - 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; et al. (CoRR, 2023)

Robust Best-arm Identification in Linear Bandits. - Wei Wang; Sattar Vakili; Ilija Bogunovic (CoRR, 2023)

Collaborative Learning in Kernel-Based Bandits for Distributed Users. - Sudeep Salgia; Sattar Vakili; Qing Zhao 001 (IEEE Trans. Signal Process., 2023)

Adversarial Contextual Bandits Go Kernelized. - Gergely Neu; Julia Olkhovskaya; Sattar Vakili (ALT, 2024)

Optimal Regret Bounds for Collaborative Learning in Bandits. - Amitis Shidani; Sattar Vakili (ALT, 2024)

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

Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency. - Sudeep Salgia; Sattar Vakili; Qing Zhao 001 (ICML, 2024)

Reward-Free Kernel-Based Reinforcement Learning. - Sattar Vakili; Farhang Nabiei; Da-shan Shiu; Alberto Bernacchia (ICML, 2024)

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

Near-Optimal Sample Complexity in Reward-Free Kernel-based Reinforcement Learning. - Aya Kayal; Sattar Vakili; Laura Toni; Alberto Bernacchia (AISTATS, 2025)

Group Think: Multiple Concurrent Reasoning Agents Collaborating at Token Level Granularity. - Chan-Jan Hsu; Davide Buffelli; Jamie McGowan; Feng-Ting Liao; Yi-Chang Chen; Sattar Vakili; Da-shan Shiu (CoRR, 2025)

Towards a Foundation Model for Communication Systems. - Davide Buffelli; Sowmen Das; Yu-Wei Lin; Sattar Vakili; Chien-Yi Wang; Masoud Attarifar; Pritthijit Nath; Da-shan Shiu (CoRR, 2025)

Bayesian Optimization from Human Feedback: Near-Optimal Regret Bounds. - Aya Kayal; Sattar Vakili; Laura Toni; Da-shan Shiu; Alberto Bernacchia (CoRR, 2025)

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