262 citations · 1.8k across the 75 of their papers we have counts for
82 papers
Kernelized Offline Contextual Dueling Bandits
Viraj Mehta, Ojash Neopane, Vikramjeet Das +3
Preference-based feedback is important for many applications where direct evaluation of a reward function is not feasible. A notable recent example arises in reinforcement learning…
Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading
Vikram Duvvur, Aashay Mehta, Edward Sun +3
The use of machine learning in algorithmic trading systems is increasingly common. In a typical set-up, supervised learning is used to predict the future prices of assets, and thos…
PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks
Ian Char, Jeff Schneider
Deep reinforcement learning (RL) has shown immense potential for learning to control systems through data alone. However, one challenge deep RL faces is that the full state of the…
Enhancing Visual Domain Adaptation with Source Preparation
Anirudha Ramesh, Anurag Ghosh, Christoph Mertz +1
Robotic Perception in diverse domains such as low-light scenarios, where new modalities like thermal imaging and specialized night-vision sensors are increasingly employed, remains…
GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search
Nikhil Angad Bakshi, Tejus Gupta, Ramina Ghods +1
Robotic solutions for quick disaster response are essential to ensure minimal loss of life, especially when the search area is too dangerous or too vast for human rescuers. We mode…
Near-optimal Policy Identification in Active Reinforcement Learning
Xiang Li, Viraj Mehta, Johannes Kirschner +5
Many real-world reinforcement learning tasks require control of complex dynamical systems that involve both costly data acquisition processes and large state spaces. In cases where…