Research Interests

Machine learning and artificial intelligence
Sequential decision making, bandit and RL
Kernel methods, Gaussian processes and Bayesian optimisation
Diffusion models

Machine Learning, Bandit, Reinforcement learning, kernel methods, Google Scholar, LinkedIn, Cornell, MediaTek,

Latest

December 2023: Optimal Regret Bounds for Collaborative Learning in Bandits is accepted at Algorithmic Learning Theory (ALT) 2024.
December 2023: Adversarial Contextual Bandits Go Kernelized is accepted at Algorithmic Learning Theory (ALT) 2024.
December 2023: Presenting our NeurIPS paper at local meetup, Cambrdige University, Dec 8th.
October 2023: Collaborative Learning in Kernel-based Bandits for Distributed Users is accepted at IEEE Transactions on Signal Processing.
October 2023: Giving a talk at the Inria Scool seminar series at University of Lille.
October 2023: Adversarial Contextual Bandits Go Kernelized is available on arXiv.
September 2023: Kernelized Reinforcement Learning with Order Optimal Regret Bounds is accepted at NeurIPS 2023.
August 2023: Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling is accepted at GLOBECOM 2023.
August 2023: Kernelized Reinforcement Learning with Order Optimal Regret Bounds is accepted at EWRL 2023.
August 2023: Check out Tor Lattimore's response to the open problem on online confidence intervals for RKHS elemets.
July 2023: Giving an online lecture at FeDucation seminar series (Florida International University).
June 2023: Giving a seminar on kernel-based reinforcement learning at Deepmind/Ellis CSML seminar series.
June 2023: Presenting Information Gain and Uniform Generalization Bounds for Neural Kernel Models at ISIT 2023 , Taipei.
May 2023: Giving an invited talk on kernel-based RL at the London Symposium on Information Theory
April 2023: "Delayed Feedback in Kernel Bandits" is accepted at ICML 2023.
April 2023: "Provably and Practically Efficient Neural Contextual Bandits" is accepted at ICML 2023.
April 2023: "Image generation with shortest path diffusion" is accepted at ICML 2023.
Feburary 2023: "Delayed Feedback in Kernel Bandits" is available on arXiv.
January 2023: "Sample Complexity of Kernel-Based Q-Learning" is accepted at AISTATS 2023.
January 2023: "Fisher-Legendre (FishLeg) optimization of deep neural networks" is accepted at ICLR 2023.
December 2022: Presenting "Gradient Descent: Robustness to Adversarial Corruption" at OPT2022 workshop at NeurIPS 2022, New Orleans.
October 2022: "Near-Optimal Collaborative Learning in Bandits" has been designated as an Oral presentation at NeurIPS 2022.
July 2022: Presenting an open problem on noise-free kernel-based bandit at COLT 2022, London.
May 2022: "Provably and Practically Efficient Neural Contextual Bandits" is available on arXiv.
May 2022: "Near-Optimal Collaborative Learning in Bandits" is available on arXiv.
May 2022: "Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning" is accepted at ICML 2022 for a Spotlight presentation.
October 2021: "Scalable Thompson Sampling using Sparse Gaussian Process Models" is accepted at NeurIPS 2021.
October 2021: "A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance" is accepted at NeurIPS 2021.
October 2021: "Optimal Order Simple Regret for Gaussian Process Bandits" is accepted at NeurIPS 2021.
August 2021: Moderating the "Bandits, RL and Control" session at COLT 2021.
August 2021: Presenting "Tight Online Confidence Intervals for RKHS Elements" at COLT 2021.
January 2021: "On Information Gain and Regret Bounds in Gaussian Process Bandits" is accepted to be presented at AIStats 2021.