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Found 10 papers, 3 papers with code
A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making
Medical Decision-Making (MDM) is a multi-faceted process that requires clinicians to assess complex multi-modal patient data patient, often collaboratively.
A Hypothesis on Black Swan in Unchanging Environments
Black swan events are statistically rare occurrences that carry extremely high risks.
RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation
We propose two approaches based on reward and preference aggregation, respectively: the former utilizes both utilitarianism and Leximin approaches to aggregate individual reward models, with sample complexity guarantees; the latter directly aggregates the human feedback in the form of probabilistic opinions.
MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making
MDAgents achieved the best performance in seven out of ten benchmarks on tasks requiring an understanding of medical knowledge and multi-modal reasoning, showing a significant improvement of up to 4. 2% (p < 0. 05) compared to previous methods' best performances.
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.
