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

• URL: http://arxiv.org/abs/2411.10496v1
• Date: Fri, 15 Nov 2024 06:54:25 GMT
• Title: Guided Learning: Lubricating End-to-End Modeling for Multi-stage Decision-making
• Authors: Jian Guo, Saizhuo Wang, Yiyan Qi,
• Abstract summary: We propose Guided Learning 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. For decision scenarios lacking explicit supervisory labels, we incorporate a utility function that quantifies the reward'' of the throughout decision.
• Score: 7.106919452604968
• License:
• 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.

Related papers

• Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework [58.362064122489166]
  This paper introduces the Cross-modal Few-Shot Learning task, which aims to recognize instances from multiple modalities when only a few labeled examples are available. We propose a Generative Transfer Learning framework consisting of two stages: the first involves training on abundant unimodal data, and the second focuses on transfer learning to adapt to novel data. Our finds demonstrate that GTL has superior performance compared to state-of-the-art methods across four distinct multi-modal datasets.
  arXiv  Detail & Related papers  (2024-10-14T16:09:38Z)
• Building Decision Making Models Through Language Model Regime [17.61892714225144]
  We propose a novel approach for decision making problems leveraging the generalization capabilities of large language models (LLMs) LLMs demonstrate remarkable success in generalizing across varied language tasks, inspiring a new strategy for training decision making models. Experiments in e-commerce domains such as advertising and search optimization have shown that LTU approach outperforms traditional supervised learning regimes.
  arXiv  Detail & Related papers  (2024-08-12T12:04:14Z)
• 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)
• Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms [34.593772931446125]
  monograph focuses on the exploration of various model-based and model-free approaches for Constrained within the context of average reward Markov Decision Processes (MDPs) The primal-dual policy gradient-based algorithm is explored as a solution for constrained MDPs.
  arXiv  Detail & Related papers  (2024-06-17T12:46:02Z)
• Safe Multi-agent Learning via Trapping Regions [89.24858306636816]
  We apply the concept of trapping regions, known from qualitative theory of dynamical systems, to create safety sets in the joint strategy space for decentralized learning. We propose a binary partitioning algorithm for verification that candidate sets form trapping regions in systems with known learning dynamics, and a sampling algorithm for scenarios where learning dynamics are not known.
  arXiv  Detail & Related papers  (2023-02-27T14:47:52Z)
• Hierarchically Structured Task-Agnostic Continual Learning [0.0]
  We take a task-agnostic view of continual learning and develop a hierarchical information-theoretic optimality principle. We propose a neural network layer, called the Mixture-of-Variational-Experts layer, that alleviates forgetting by creating a set of information processing paths. Our approach can operate in a task-agnostic way, i.e., it does not require task-specific knowledge, as is the case with many existing continual learning algorithms.
  arXiv  Detail & Related papers  (2022-11-14T19:53:15Z)
• FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation [69.34011200590817]
  We introduce FiLM-Ensemble, a deep, implicit ensemble method based on the concept of Feature-wise Linear Modulation. By modulating the network activations of a single deep network with FiLM, one obtains a model ensemble with high diversity. We show that FiLM-Ensemble outperforms other implicit ensemble methods, and it comes very close to the upper bound of an explicit ensemble of networks.
  arXiv  Detail & Related papers  (2022-05-31T18:33:15Z)
• Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning [52.74071439183113]
  We study the predict-then-optimize framework in the context of sequential decision problems (formulated as MDPs) solved via reinforcement learning. Two significant computational challenges arise in applying decision-focused learning to MDPs.
  arXiv  Detail & Related papers  (2021-06-06T23:53:31Z)
• A Practical Guide to Multi-Objective Reinforcement Learning and Planning [24.81310809455139]
  This paper serves as a guide to the application of multi-objective methods to difficult problems. It identifies the factors that may influence the nature of the desired solution. It illustrates by example how these influence the design of multi-objective decision-making systems.
  arXiv  Detail & Related papers  (2021-03-17T11:07:28Z)
• Importance Weighted Policy Learning and Adaptation [89.46467771037054]
  We study a complementary approach which is conceptually simple, general, modular and built on top of recent improvements in off-policy learning. The framework is inspired by ideas from the probabilistic inference literature and combines robust off-policy learning with a behavior prior. Our approach achieves competitive adaptation performance on hold-out tasks compared to meta reinforcement learning baselines and can scale to complex sparse-reward scenarios.
  arXiv  Detail & Related papers  (2020-09-10T14:16:58Z)
• Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey [1.2031796234206134]
  Model-based reinforcement learning creates an explicit model of the environment dynamics to reduce the need for environment samples. A challenge for deep model-based methods is to achieve high predictive power while maintaining low sample complexity. We propose a taxonomy based on three approaches: using explicit planning on given transitions, using explicit planning on learned transitions, and end-to-end learning of both planning and transitions.
  arXiv  Detail & Related papers  (2020-08-11T08:49:04Z)

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.