1 citations · 2 across the 4 of their papers we have counts for
8 papers
Conditional Flow Models Convert Epistemic Uncertainty into Apparent Aleatoric Variance
Navodita Sharma, Abhishek Shetty, Max Simchowitz +1
Conditional Flow Models (CFMs) are increasingly being used in place of deterministic predictors across machine learning, including for tasks that were classically formulated as reg…
HRTF Upsampling and Personalization via Neural Operators
Meenakshi Krishnan, You Zhang, Ramani Duraiswami +1
Head-related transfer function (HRTF) spatial upsampling from sparse measurements is a fundamental challenge in personalized binaural audio. We formulate HRTF upsampling as an oper…
Memory Without Curation: Learning Multi-Stream Latent Memory for Robot Policies
Yilin Wu, Rutav Shah, Shuaiyi Huang +5
It is widely appreciated that robotic policies require long-term memory to perform complex tasks. However, directly conditioning policies on all past observations leaves policy lea…
Workspace Models: Lightweight Robotic Memory via Saliency-Driven Supervision
Nitish Dashora, Douglas Chen, Idan Shenfeld +3
Complex robotic manipulation tasks frequently require a long-term memory of past events and actions. As conditioning on full histories renders policies prone to spurious correlatio…
Diffusion Policy Policy Optimization
Allen Z. Ren, Justin Lidard, Lars L. Ankile +6
We introduce Diffusion Policy Policy Optimization, DPPO, an algorithmic framework including best practices for fine-tuning diffusion-based policies (e.g. Diffusion Policy) in conti…
RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability
Chuning Zhu, Max Simchowitz, Siri Gadipudi +1
Visual model-based RL methods typically encode image observations into low-dimensional representations in a manner that does not eliminate redundant information. This leaves them s…
Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective
Max Simchowitz, Abhishek Gupta, Kaiqing Zhang
Obtaining rigorous statistical guarantees for generalization under distribution shift remains an open and active research area. We study a setting we call combinatorial distributio…
Learning to Extrapolate: A Transductive Approach
Aviv Netanyahu, Abhishek Gupta, Max Simchowitz +2
Machine learning systems, especially with overparameterized deep neural networks, can generalize to novel test instances drawn from the same distribution as the training data. Howe…