Search Results for author: Katie Kang

Found 6 papers, 2 papers with code

Unfamiliar Finetuning Examples Control How Language Models Hallucinate

no code implementations8 Mar 2024 Katie Kang, Eric Wallace, Claire Tomlin, Aviral Kumar, Sergey Levine

Large language models (LLMs) have a tendency to generate plausible-sounding yet factually incorrect responses, especially when queried on unfamiliar concepts.

Multiple-choice

Deep Neural Networks Tend To Extrapolate Predictably

1 code implementation2 Oct 2023 Katie Kang, Amrith Setlur, Claire Tomlin, Sergey Levine

Rather than extrapolating in arbitrary ways, we observe that neural network predictions often tend towards a constant value as input data becomes increasingly OOD.

Decision Making

Multi-Task Imitation Learning for Linear Dynamical Systems

no code implementations1 Dec 2022 Thomas T. Zhang, Katie Kang, Bruce D. Lee, Claire Tomlin, Sergey Levine, Stephen Tu, Nikolai Matni

In particular, we consider a setting where learning is split into two phases: (a) a pre-training step where a shared $k$-dimensional representation is learned from $H$ source policies, and (b) a target policy fine-tuning step where the learned representation is used to parameterize the policy class.

Imitation Learning Representation Learning

Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control

no code implementations21 Jun 2022 Katie Kang, Paula Gradu, Jason Choi, Michael Janner, Claire Tomlin, Sergey Levine

Learned models and policies can generalize effectively when evaluated within the distribution of the training data, but can produce unpredictable and erroneous outputs on out-of-distribution inputs.

Density Estimation

Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robots

no code implementations24 Jun 2021 Katie Kang, Gregory Kahn, Sergey Levine

In this work, we propose a deep reinforcement learning algorithm with hierarchically integrated models (HInt).

Navigate reinforcement-learning +1

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