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Found 7 papers, 2 papers with code
Adaptive Collaboration Strategy for LLMs in Medical Decision Making
Our novel framework, Medical Decision-making Agents (MDAgents) aims to address this gap by automatically assigning the effective collaboration structure for LLMs.
Do LLM Agents Have Regret? A Case Study in Online Learning and Games
To better understand the limits of LLM agents in these interactive environments, we propose to study their interactions in benchmark decision-making settings in online learning and game theory, through the performance metric of \emph{regret}.
Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences
Customizing robotic behaviors to be aligned with diverse human preferences is an underexplored challenge in the field of embodied AI.
Multi-Player Zero-Sum Markov Games with Networked Separable Interactions
We study a new class of Markov games, \emph(multi-player) zero-sum Markov Games} with \emph{Networked separable interactions} (zero-sum NMGs), to model the local interaction structure in non-cooperative multi-agent sequential decision-making.
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective
Our theoretical results show that regardless of the choice of the mixing strategy, MSDA behaves as a pixel-level regularization of the underlying training loss and a regularization of the first layer parameters.
REST: Performance Improvement of a Black Box Model via RL-based Spatial Transformation
Our work aims to improve the robustness by adding a REST module in front of any black boxes and training only the REST module without retraining the original black box model in an end-to-end manner, i. e. we try to convert the real-world data into training distribution which the performance of the black-box model is best suited for.
Sampling-based Bayesian Inference with gradient uncertainty
In this paper, we show that predictive uncertainty can be efficiently estimated when we incorporate the concept of gradients uncertainty into posterior sampling.
