822 citations · 2.9k across the 43 of their papers we have counts for
12 papers · 1 filter
Connecting Context-specific Adaptation in Humans to Meta-learning
Rachit Dubey, Erin Grant, Michael Luo +2
Cognitive control, the ability of a system to adapt to the demands of a task, is an integral part of cognition. A widely accepted fact about cognitive control is that it is context…
Projection-Based Constrained Policy Optimization
Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan +1
We consider the problem of learning control policies that optimize a reward function while satisfying constraints due to considerations of safety, fairness, or other costs. We prop…
Keep CALM and Explore: Language Models for Action Generation in Text-based Games
Shunyu Yao, Rohan Rao, Matthew Hausknecht +1
Text-based games present a unique challenge for autonomous agents to operate in natural language and handle enormous action spaces. In this paper, we propose the Contextual Action…
Guiding Attention for Self-Supervised Learning with Transformers
Ameet Deshpande, Karthik Narasimhan
In this paper, we propose a simple and effective technique to allow for efficient self-supervised learning with bi-directional Transformers. Our approach is motivated by recent stu…
Improving Dialog Systems for Negotiation with Personality Modeling
Runzhe Yang, Jingxiao Chen, Karthik Narasimhan
In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent's high-level stra…
Safe Reinforcement Learning with Natural Language Constraints
Tsung-Yen Yang, Michael Hu, Yinlam Chow +2
While safe reinforcement learning (RL) holds great promise for many practical applications like robotics or autonomous cars, current approaches require specifying constraints in ma…
Towards Unique and Informative Captioning of Images
Zeyu Wang, Berthy Feng, Karthik Narasimhan +1
Despite considerable progress, state of the art image captioning models produce generic captions, leaving out important image details. Furthermore, these systems may even misrepres…
Learning Rewards from Linguistic Feedback
Theodore R. Sumers, Mark K. Ho, Robert D. Hawkins +2
We explore unconstrained natural language feedback as a learning signal for artificial agents. Humans use rich and varied language to teach, yet most prior work on interactive lear…
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation
Zhiwei Deng, Karthik Narasimhan, Olga Russakovsky
The ability to perform effective planning is crucial for building an instruction-following agent. When navigating through a new environment, an agent is challenged with (1) connect…
Accelerating Safe Reinforcement Learning with Constraint-mismatched Policies
Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan +1
We consider the problem of reinforcement learning when provided with (1) a baseline control policy and (2) a set of constraints that the learner must satisfy. The baseline policy c…
Universal Adversarial Attacks with Natural Triggers for Text Classification
Liwei Song, Xinwei Yu, Hsuan-Tung Peng +1
Recent work has demonstrated the vulnerability of modern text classifiers to universal adversarial attacks, which are input-agnostic sequences of words added to text processed by c…
Take the Scenic Route: Improving Generalization in Vision-and-Language Navigation
Felix Yu, Zhiwei Deng, Karthik Narasimhan +1
In the Vision-and-Language Navigation (VLN) task, an agent with egocentric vision navigates to a destination given natural language instructions. The act of manually annotating the…