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Found 11 papers, 1 papers with code
$\mathbf{(N,K)}$-Puzzle: A Cost-Efficient Testbed for Benchmarking Reinforcement Learning Algorithms in Generative Language Model
Recent advances in reinforcement learning (RL) algorithms aim to enhance the performance of language models at scale.
How Can LLM Guide RL? A Value-Based Approach
Specifically, we develop an algorithm named LINVIT that incorporates LLM guidance as a regularization factor in value-based RL, leading to significant reductions in the amount of data needed for learning, particularly when the difference between the ideal policy and the LLM-informed policy is small, which suggests that the initial policy is close to optimal, reducing the need for further exploration.
Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis
Recent advancements in text-to-image (T2I) generative models have shown remarkable capabilities in producing diverse and imaginative visuals based on text prompts.
Let Models Speak Ciphers: Multiagent Debate through Embeddings
Although natural language is an obvious choice for communication due to LLM's language understanding capability, the token sampling step needed when generating natural language poses a potential risk of information loss, as it uses only one token to represent the model's belief across the entire vocabulary.
The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models
Macroscopic data aggregated from microscopic events are pervasive in machine learning, such as country-level COVID-19 infection statistics based on city-level data.
Learning Positive Functions with Pseudo Mirror Descent
The nonparametric learning of positive-valued functions appears widely in machine learning, especially in the context of estimating intensity functions of point processes.
Predictive Approximate Bayesian Computation via Saddle Points
Approximate Bayesian computation (ABC) is an important methodology for Bayesian inference when the likelihood function is intractable.
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models
We propose a nonparametric method for detecting nonlinear causal relationship within a set of multidimensional discrete time series, by using sparse additive models (SpAMs).
Nonparametric Hawkes Processes: Online Estimation and Generalization Bounds
In this paper, we design a nonparametric online algorithm for estimating the triggering functions of multivariate Hawkes processes.
Online Learning for Multivariate Hawkes Processes
We develop a nonparametric and online learning algorithm that estimates the triggering functions of a multivariate Hawkes process (MHP).
Efficient Neighborhood Selection for Gaussian Graphical Models
This paper addresses the problem of neighborhood selection for Gaussian graphical models.
