543 citations · 2.3k across the 163 of their papers we have counts for
171 papers
Tensor Ring Optimized Quantum-Enhanced Tensor Neural Networks
Debanjan Konar, Dheeraj Peddireddy, Vaneet Aggarwal +1
Quantum machine learning researchers often rely on incorporating Tensor Networks (TN) into Deep Neural Networks (DNN) and variational optimization. However, the standard optimizati…
Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes
Qinbo Bai, Washim Uddin Mondal, Vaneet Aggarwal
In this paper, we consider an infinite horizon average reward Markov Decision Process (MDP). Distinguishing itself from existing works within this context, our approach harnesses t…
Statistically Efficient Variance Reduction with Double Policy Estimation for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning
Hanhan Zhou, Tian Lan, Vaneet Aggarwal
Offline reinforcement learning aims to utilize datasets of previously gathered environment-action interaction records to learn a policy without access to the real environment. Rece…
Noisy Tensor Ring approximation for computing gradients of Variational Quantum Eigensolver for Combinatorial Optimization
Dheeraj Peddireddy, Utkarsh Priyam, Vaneet Aggarwal
Variational Quantum algorithms, especially Quantum Approximate Optimization and Variational Quantum Eigensolver (VQE) have established their potential to provide computational adva…
Multi-task Hierarchical Adversarial Inverse Reinforcement Learning
Jiayu Chen, Dipesh Tamboli, Tian Lan +1
Multi-task Imitation Learning (MIL) aims to train a policy capable of performing a distribution of tasks based on multi-task expert demonstrations, which is essential for general-p…
On the Global Convergence of Natural Actor-Critic with Two-layer Neural Network Parametrization
Mudit Gaur, Amrit Singh Bedi, Di Wang +1
Actor-critic algorithms have shown remarkable success in solving state-of-the-art decision-making problems. However, despite their empirical effectiveness, their theoretical underp…