Search Results for author:
Found 11 papers, 8 papers with code
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model
We propose a new algorithm for model-based distributional reinforcement learning (RL), and prove that it is minimax-optimal for approximating return distributions with a generative model (up to logarithmic factors), resolving an open question of Zhang et al. (2023).
Distributional Reinforcement Learning
reinforcement-learning
+2
Amortized Planning with Large-Scale Transformers: A Case Study on Chess
This paper uses chess, a landmark planning problem in AI, to assess transformers' performance on a planning task where memorization is futile $\unicode{x2013}$ even at a large scale.
Learning Universal Predictors
Meta-learning has emerged as a powerful approach to train neural networks to learn new tasks quickly from limited data.
Distributional Bellman Operators over Mean Embeddings
We propose a novel algorithmic framework for distributional reinforcement learning, based on learning finite-dimensional mean embeddings of return distributions.
Score-based generative models learn manifold-like structures with constrained mixing
These observations suggest that SBMs can flexibly mix samples with the learned score field while carefully maintaining a manifold-like structure of the data distribution.
TacticAI: an AI assistant for football tactics
The utility of TacticAI is validated by a qualitative study conducted with football domain experts at Liverpool FC.
Language Modeling Is Compression
We show that large language models are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning.
Memory-Based Meta-Learning on Non-Stationary Distributions
Memory-based meta-learning is a technique for approximating Bayes-optimal predictors.
On the failure of variational score matching for VAE models
Score matching (SM) is a convenient method for training flexible probabilistic models, which is often preferred over the traditional maximum-likelihood (ML) approach.
Neural Networks and the Chomsky Hierarchy
Reliable generalization lies at the heart of safe ML and AI.
A neurally plausible model for online recognition and postdiction in a dynamical environment
Humans and other animals are frequently near-optimal in their ability to integrate noisy and ambiguous sensory data to form robust percepts---which are informed both by sensory evidence and by prior expectations about the structure of the environment.
