11 citations · 23 across the 7 of their papers we have counts for
7 papers
What Do Learning Dynamics Reveal About Generalization in LLM Reasoning?
Katie Kang, Amrith Setlur, Dibya Ghosh +4
Despite the remarkable capabilities of modern large language models (LLMs), the mechanisms behind their problem-solving abilities remain elusive. In this work, we aim to better und…
Unfamiliar Finetuning Examples Control How Language Models Hallucinate
Katie Kang, Eric Wallace, Claire Tomlin +2
Large language models are known to hallucinate when faced with unfamiliar queries, but the underlying mechanism that govern how models hallucinate are not yet fully understood. In…
Deep Neural Networks Tend To Extrapolate Predictably
Katie Kang, Amrith Setlur, Claire Tomlin +1
Conventional wisdom suggests that neural network predictions tend to be unpredictable and overconfident when faced with out-of-distribution (OOD) inputs. Our work reassesses this a…
Multi-Task Imitation Learning for Linear Dynamical Systems
Thomas T. Zhang, Katie Kang, Bruce D. Lee +4
We study representation learning for efficient imitation learning over linear systems. In particular, we consider a setting where learning is split into two phases: (a) a pre-train…
Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control
Katie Kang, Paula Gradu, Jason Choi +3
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-di…
Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robots
Katie Kang, Gregory Kahn, Sergey Levine
Deep reinforcement learning algorithms require large and diverse datasets in order to learn successful policies for perception-based mobile navigation. However, gathering such data…
Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight
Katie Kang, Suneel Belkhale, Gregory Kahn +2
Deep reinforcement learning provides a promising approach for vision-based control of real-world robots. However, the generalization of such models depends critically on the quanti…