153 citations · 164 across the 7 of their papers we have counts for
7 papers
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
Ian Gemp, Thomas Anthony, Yoram Bachrach +24
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simul…
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
Julien Perolat, Bart de Vylder, Daniel Hennes +31
We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few…
Changing Computer-Usage Behaviours: What Users Want, Use, and Experience
Mina Khan, Zeel Patel, Kathryn Wantlin +2
Technology based screentime, the time an individual spends engaging with their computer or cell phone, has increased exponentially over the past decade, but perhaps most alarmingly…
Pretrained Encoders are All You Need
Mina Khan, P Srivatsa, Advait Rane +4
Data-efficiency and generalization are key challenges in deep learning and deep reinforcement learning as many models are trained on large-scale, domain-specific, and expensive-to-…
Personalizing Pre-trained Models
Mina Khan, P Srivatsa, Advait Rane +3
Self-supervised or weakly supervised models trained on large-scale datasets have shown sample-efficient transfer to diverse datasets in few-shot settings. We consider how upstream…
PAL: Intelligence Augmentation using Egocentric Visual Context Detection
Mina Khan, Pattie Maes
Egocentric visual context detection can support intelligence augmentation applications. We created a wearable system, called PAL, for wearable, personalized, and privacy-preserving…