Search Results for author:
Found 20 papers, 8 papers with code
Smart Help: Strategic Opponent Modeling for Proactive and Adaptive Robot Assistance in Households
Our findings illustrate the potential of AI-imbued assistive robots in improving the well-being of vulnerable groups.
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
We explore how iterative revising a chain of thoughts with the help of information retrieval significantly improves large language models' reasoning and generation ability in long-horizon generation tasks, while hugely mitigating hallucination.
Image Inpainting via Tractable Steering of Diffusion Models
In addition to proposing a new framework for constrained image generation, this paper highlights the benefit of more tractable models and motivates the development of expressive TPMs.
JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models
Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents.
Expressive Modeling Is Insufficient for Offline RL: A Tractable Inference Perspective
A popular paradigm for offline Reinforcement Learning (RL) tasks is to first fit the offline trajectories to a sequence model, and then prompt the model for actions that lead to high expected return.
GROOT: Learning to Follow Instructions by Watching Gameplay Videos
We propose to follow reference videos as instructions, which offer expressive goal specifications while eliminating the need for expensive text-gameplay annotations.
ProAgent: Building Proactive Cooperative Agents with Large Language Models
Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems.
Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits
In this paper, we theoretically and empirically discover that the performance of a PC can exceed that of its teacher model.
Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents
We investigate the challenge of task planning for multi-task embodied agents in open-world environments.
Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction
We study the problem of learning goal-conditioned policies in Minecraft, a popular, widely accessible yet challenging open-ended environment for developing human-level multi-task agents.
Sparse Probabilistic Circuits via Pruning and Growing
The growing operation increases model capacity by increasing the size of the latent space.
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation
To bridge this gap, we study the problem of few-shot adaptation in the context of human-in-the-loop reinforcement learning.
Scaling Up Probabilistic Circuits by Latent Variable Distillation
We propose to overcome such bottleneck by latent variable distillation: we leverage the less tractable but more expressive deep generative models to provide extra supervision over the latent variables of PCs.
Lossless Compression with Probabilistic Circuits
To overcome such problems, we establish a new class of tractable lossless compression models that permit efficient encoding and decoding: Probabilistic Circuits (PCs).
Tractable Regularization of Probabilistic Circuits
Instead, we re-think regularization for PCs and propose two intuitive techniques, data softening and entropy regularization, that both take advantage of PCs' tractability and still have an efficient implementation as a computation graph.
A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference
Circuit representations are becoming the lingua franca to express and reason about tractable generative and discriminative models.
A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries
Circuit representations are becoming the lingua franca to express and reason about tractable generative and discriminative models.
On Effective Parallelization of Monte Carlo Tree Search
Second, and more importantly, we demonstrate how the proposed necessary conditions can be adopted to design more effective parallel MCTS algorithms.
Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration
Off-policy reinforcement learning (RL) is concerned with learning a rewarding policy by executing another policy that gathers samples of experience.
Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search
Monte Carlo Tree Search (MCTS) algorithms have achieved great success on many challenging benchmarks (e. g., Computer Go).
