A brief note on learning problem with global perspectives

• URL: http://arxiv.org/abs/2601.05441v1
• Date: Fri, 09 Jan 2026 00:20:36 GMT
• Title: A brief note on learning problem with global perspectives
• Authors: Getachew K. Befekadu,
• Abstract summary: This note considers the problem of learning with dynamic-optimizing principal-agent setting.<n>We present a coherent mathematical argument which is necessary for characterizing the learning process behind this abstract principal-agent learning framework.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This brief note considers the problem of learning with dynamic-optimizing principal-agent setting, in which the agents are allowed to have global perspectives about the learning process, i.e., the ability to view things according to their relative importances or in their true relations based-on some aggregated information shared by the principal. Whereas, the principal, which is exerting an influence on the learning process of the agents in the aggregation, is primarily tasked to solve a high-level optimization problem posed as an empirical-likelihood estimator under conditional moment restrictions model that also accounts information about the agents' predictive performances on out-of-samples as well as a set of private datasets available only to the principal. In particular, we present a coherent mathematical argument which is necessary for characterizing the learning process behind this abstract principal-agent learning framework, although we acknowledge that there are a few conceptual and theoretical issues still need to be addressed.

Related papers

• Towards Principled Unsupervised Multi-Agent Reinforcement Learning [49.533774397707056]
  We present a scalable, decentralized, trust-region policy search algorithm to address the problem in practical settings.<n>We show that optimizing for a specific objective, namely mixture entropy, provides an excellent trade-off between tractability and performances.
  arXiv  Detail & Related papers  (2025-02-12T12:51:36Z)
• Aggregation Artifacts in Subjective Tasks Collapse Large Language Models' Posteriors [74.04775677110179]
  In-context Learning (ICL) has become the primary method for performing natural language tasks with Large Language Models (LLMs)<n>In this work, we examine whether this is the result of the aggregation used in corresponding datasets, where trying to combine low-agreement, disparate annotations might lead to annotation artifacts that create detrimental noise in the prompt.<n>Our results indicate that aggregation is a confounding factor in the modeling of subjective tasks, and advocate focusing on modeling individuals instead.
  arXiv  Detail & Related papers  (2024-10-17T17:16:00Z)
• Coding for Intelligence from the Perspective of Category [66.14012258680992]
  Coding targets compressing and reconstructing data, and intelligence. Recent trends demonstrate the potential homogeneity of these two fields. We propose a novel problem of Coding for Intelligence from the category theory view.
  arXiv  Detail & Related papers  (2024-07-01T07:05:44Z)
• REVEAL-IT: REinforcement learning with Visibility of Evolving Agent poLicy for InTerpretability [23.81322529587759]
  REVEAL-IT is a novel framework for explaining the learning process of an agent in complex environments. We visualize the policy structure and the agent's learning process for various training tasks. A GNN-based explainer learns to highlight the most important section of the policy, providing a more clear and robust explanation of the agent's learning process.
  arXiv  Detail & Related papers  (2024-06-20T11:29:26Z)
• Post Hoc Explanations of Language Models Can Improve Language Models [43.2109029463221]
  We present a novel framework, Amplifying Model Performance by Leveraging In-Context Learning with Post Hoc Explanations (AMPLIFY) We leverage post hoc explanation methods which output attribution scores (explanations) capturing the influence of each of the input features on model predictions. Our framework, AMPLIFY, leads to prediction accuracy improvements of about 10-25% over a wide range of tasks.
  arXiv  Detail & Related papers  (2023-05-19T04:46:04Z)
• Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey [10.621186518750976]
  Reinforcement Learning (RL) has achieved tremendous development in recent years, but still faces significant obstacles in addressing complex real-life problems.<n>Recently, there has been a rapidly growing interest in the use of Knowledge Representation and Reasoning (KRR) methods to enable more abstract representation and efficient learning in RL.
  arXiv  Detail & Related papers  (2023-04-24T13:35:11Z)
• Leveraging sparse and shared feature activations for disentangled representation learning [112.22699167017471]
  We propose to leverage knowledge extracted from a diversified set of supervised tasks to learn a common disentangled representation. We validate our approach on six real world distribution shift benchmarks, and different data modalities.
  arXiv  Detail & Related papers  (2023-04-17T01:33:24Z)
• Important Object Identification with Semi-Supervised Learning for Autonomous Driving [37.654878298744855]
  We propose a novel approach for important object identification in egocentric driving scenarios. We present a semi-supervised learning pipeline to enable the model to learn from unlimited unlabeled data. Our approach also outperforms rule-based baselines by a large margin.
  arXiv  Detail & Related papers  (2022-03-05T01:23:13Z)
• Which Mutual-Information Representation Learning Objectives are Sufficient for Control? [80.2534918595143]
  Mutual information provides an appealing formalism for learning representations of data. This paper formalizes the sufficiency of a state representation for learning and representing the optimal policy. Surprisingly, we find that two of these objectives can yield insufficient representations given mild and common assumptions on the structure of the MDP.
  arXiv  Detail & Related papers  (2021-06-14T10:12:34Z)
• Fundamental Limits and Tradeoffs in Invariant Representation Learning [99.2368462915979]
  Many machine learning applications involve learning representations that achieve two competing goals. Minimax game-theoretic formulation represents a fundamental tradeoff between accuracy and invariance. We provide an information-theoretic analysis of this general and important problem under both classification and regression settings.
  arXiv  Detail & Related papers  (2020-12-19T15:24:04Z)
• Understanding Global Feature Contributions With Additive Importance Measures [14.50261153230204]
  We explore the perspective of defining feature importance through the predictive power associated with each feature. We introduce two notions of predictive power (model-based and universal) and formalize this approach with a framework of additive importance measures. We then propose SAGE, a model-agnostic method that quantifies predictive power while accounting for feature interactions.
  arXiv  Detail & Related papers  (2020-04-01T19:17:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.