activity
20202022
most citedThe AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies

44 citations · 57 across the 5 of their papers we have counts for

collaborators

7 papers

eess.SY20221 cited

Control Barrier Function Contracts for Vehicular Mission Planning Under Signal Temporal Logic Specifications

Muhammad Waqas, Nikhil Vijay Naik, Petros Ioannou +1

We present a compositional control synthesis method based on assume-guarantee contracts with application to correct-by-construction design of vehicular mission plans. In our approa…

cs.CV2022

Can domain adaptation make object recognition work for everyone?

Viraj Prabhu, Ramprasaath R. Selvaraju, Judy Hoffman +1

Despite the rapid progress in deep visual recognition, modern computer vision datasets significantly overrepresent the developed world and models trained on such datasets underperf…

cs.LG202112 cited

Deep Extrapolation for Attribute-Enhanced Generation

Alvin Chan, Ali Madani, Ben Krause +1

Attribute extrapolation in sample generation is challenging for deep neural networks operating beyond the training distribution. We formulate a new task for extrapolation in sequen…

cs.CV2020

CASTing Your Model: Learning to Localize Improves Self-Supervised Representations

Ramprasaath R. Selvaraju, Karan Desai, Justin Johnson +1

Recent advances in self-supervised learning (SSL) have largely closed the gap with supervised ImageNet pretraining. Despite their success these methods have been primarily applied…

econ.GN202044 cited

The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies

Stephan Zheng, Alexander Trott, Sunil Srinivasa +4

Tackling real-world socio-economic challenges requires designing and testing economic policies. However, this is hard in practice, due to a lack of appropriate (micro-level) econom…

cs.CV2020

Improving out-of-distribution generalization via multi-task self-supervised pretraining

Isabela Albuquerque, Nikhil Naik, Junnan Li +2

Self-supervised feature representations have been shown to be useful for supervised classification, few-shot learning, and adversarial robustness. We show that features obtained us…