Publications (8)
Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models
Nicholas Rhinehart, Jeff He, Charles Packer +4
Humans have a remarkable ability to make decisions by accurately reasoning about future events, including the future behaviors and states of mind of other agents. Consider driving…
Visually-Aware Personalized Recommendation using Interpretable Image Representations
Charles Packer, Julian McAuley, Arnau Ramisa
Visually-aware recommender systems use visual signals present in the underlying data to model the visual characteristics of items and users' preferences towards them. In the domain…
Sleep-time Compute: Beyond Inference Scaling at Test-time
Kevin Lin, Charlie Snell, Yu Wang +4
Scaling test-time compute has emerged as a key ingredient for enabling large language models (LLMs) to solve difficult problems, but comes with high latency and inference cost. We…
CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting
Jiezhi Yang, Khushi Desai, Charles Packer +4
We propose CARFF, a method for predicting future 3D scenes given past observations. Our method maps 2D ego-centric images to a distribution over plausible 3D latent scene configura…
MemGPT: Towards LLMs as Operating Systems
Charles Packer, Sarah Wooders, Kevin Lin +4
Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversations and document analy…
Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL
Charles Packer, Pieter Abbeel, Joseph E. Gonzalez
Meta-reinforcement learning (meta-RL) has proven to be a successful framework for leveraging experience from prior tasks to rapidly learn new related tasks, however, current meta-R…
Learning Compatibility Across Categories for Heterogeneous Item Recommendation
Ruining He, Charles Packer, Julian McAuley
Identifying relationships between items is a key task of an online recommender system, in order to help users discover items that are functionally complementary or visually compati…
Assessing Generalization in Deep Reinforcement Learning
Charles Packer, Katelyn Gao, Jernej Kos +3
Deep reinforcement learning (RL) has achieved breakthrough results on many tasks, but agents often fail to generalize beyond the environment they were trained in. As a result, deep…