Guided Learning: Lubricating End-to-End Modeling for Multi-stage Decision-making

Paper: arXiv 2411.10496

Abstract

Multi-stage decision-making is crucial in various real-world artificial intelligence applications, including recommendation systems, autonomous driving, and quantitative investment systems. In quantitative investment, for example, the process typically involves several sequential stages such as factor mining, alpha prediction, portfolio optimization, and sometimes order execution. While state-of-the-art end-to-end modeling aims to unify these stages into a single global framework, it faces significant challenges: (1) training such a unified neural network consisting of multiple stages between initial inputs and final outputs often leads to suboptimal solutions, or even collapse, and (2) many decision-making scenarios are not easily reducible to standard prediction problems. To overcome these challenges, we propose Guided Learning, a novel methodological framework designed to enhance end-to-end learning in multi-stage decision-making. We introduce the concept of a guide’’, a function that induces the training of intermediate neural network layers towards some phased goals, directing gradients away from suboptimal collapse. For decision scenarios lacking explicit supervisory labels, we incorporate a utility function that quantifies the reward’’ of the throughout decision. Additionally, we explore the connections between Guided Learning and classic machine learning paradigms such as supervised, unsupervised, semi-supervised, multi-task, and reinforcement learning. Experiments on quantitative investment strategy building demonstrate that guided learning significantly outperforms both traditional stage-wise approaches and existing end-to-end methods.

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces a structured framework with formal loss functions and gradient manipulation, indicating high math complexity. It reports quantitative experiments on investment strategies but lacks details on datasets, specific backtest metrics, or code, resulting in moderate empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal: Overcome challenges of<br/>end-to-end modeling in multi-stage decision-making"] --> B["Proposed Method: Guided Learning"]
  B --> C["Key Methodology: Introduce 'Guides' &<br/>Utility Functions for phased training"]
  D["Data: Quantitative Investment Datasets<br/>(Market Data, Factors)"] --> E["Computational Process: Guided Learning Framework"]
  C --> E
  E --> F["Outcome: Unified Neural Network"]
  F --> G["Key Findings: Outperforms stage-wise &<br/>existing end-to-end methods"]

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2 of 130 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.