Search Results for author: Kuno Kim

Found 7 papers, 3 papers with code

Active World Model Learning in Agent-rich Environments with Progress Curiosity

no code implementations ICML 2020 Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins

World models are a family of predictive models that solve self-supervised problems on how the world evolves.

Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations

no code implementations15 Apr 2022 Michael Poli, Winnie Xu, Stefano Massaroli, Chenlin Meng, Kuno Kim, Stefano Ermon

We investigate how to leverage the representations produced by Neural Collages in various tasks, including data compression and generation.

Data Compression

LISA: Learning Interpretable Skill Abstractions from Language

1 code implementation28 Feb 2022 Divyansh Garg, Skanda Vaidyanath, Kuno Kim, Jiaming Song, Stefano Ermon

Learning policies that effectively utilize language instructions in complex, multi-task environments is an important problem in sequential decision-making.

Imitation Learning Quantization

Imitation with Neural Density Models

no code implementations NeurIPS 2021 Kuno Kim, Akshat Jindal, Yang song, Jiaming Song, Yanan Sui, Stefano Ermon

We propose a new framework for Imitation Learning (IL) via density estimation of the expert's occupancy measure followed by Maximum Occupancy Entropy Reinforcement Learning (RL) using the density as a reward.

Density Estimation Imitation Learning +2

Active World Model Learning with Progress Curiosity

no code implementations15 Jul 2020 Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins

Humans learn world models by curiously exploring their environment, in the process acquiring compact abstractions of high bandwidth sensory inputs, the ability to plan across long temporal horizons, and an understanding of the behavioral patterns of other agents.

Domain Adaptive Imitation Learning

1 code implementation ICML 2020 Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon

We formalize the Domain Adaptive Imitation Learning (DAIL) problem, which is a unified framework for imitation learning in the presence of viewpoint, embodiment, and dynamics mismatch.

Imitation Learning

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