19 citations · 123 across the 25 of their papers we have counts for
5 papers · 1 filter
Optimal Policies Tend to Seek Power
Alexander Matt Turner, Logan Smith, Rohin Shah +2
Some researchers speculate that intelligent reinforcement learning (RL) agents would be incentivized to seek resources and power in pursuit of their objectives. Other researchers p…
The Choice Function Framework for Online Policy Improvement
Murugeswari Issakkimuthu, Alan Fern, Prasad Tadepalli
There are notable examples of online search improving over hand-coded or learned policies (e.g. AlphaZero) for sequential decision making. It is not clear, however, whether or not…
Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation
Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini +2
We present a new local entity disambiguation system. The key to our system is a novel approach for learning entity representations. In our approach we learn an entity aware extensi…
Saliency Learning: Teaching the Model Where to Pay Attention
Reza Ghaeini, Xiaoli Z. Fern, Hamed Shahbazi +1
Deep learning has emerged as a compelling solution to many NLP tasks with remarkable performances. However, due to their opacity, such models are hard to interpret and trust. Recen…
Conservative Agency via Attainable Utility Preservation
Alexander Matt Turner, Dylan Hadfield-Menell, Prasad Tadepalli
Reward functions are easy to misspecify; although designers can make corrections after observing mistakes, an agent pursuing a misspecified reward function can irreversibly change…