Katja Hofmann

Cross Domain Regularization for Neural Ranking Models using Adversarial Learning

2018

Unlike traditional learning to rank models that depend on hand-crafted features, neural representation learning models learn higher level features for the ranking task by training on large datasets. Their ability to learn new features directly …

Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning

2018 · arXiv (Cornell University)

Posterior sampling for reinforcement learning (PSRL) is an effective method for balancing exploration and exploitation in reinforcement learning. Randomised value functions (RVF) can be viewed as a promising approach to scaling PSRL. However, we show …

Exploration in Approximate Hyper-State Space for Meta Reinforcement\n Learning

2020 · arXiv (Cornell University)

To rapidly learn a new task, it is often essential for agents to explore\nefficiently -- especially when performance matters from the first timestep. One\nway to learn such behaviour is via meta-learning. Many existing methods however\nrely …

Towards Conversational Recommender Systems

2016

People often ask others for restaurant recommendations as a way to discover new dining experiences. This makes restaurant recommendation an exciting scenario for recommender systems and has led to substantial research in this area. However, …