A General Framework for Learning from Weak Supervision

• URL: http://arxiv.org/abs/2402.01922v3
• Date: Wed, 5 Jun 2024 16:03:55 GMT
• Title: A General Framework for Learning from Weak Supervision
• Authors: Hao Chen, Jindong Wang, Lei Feng, Xiang Li, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj,
• Abstract summary: This paper introduces a general framework for learning from weak supervision (GLWS) with a novel algorithm. Central to GLWS is an Expectation-Maximization (EM) formulation, adeptly accommodating various weak supervision sources. We also present an advanced algorithm that significantly simplifies the EM computational demands.
• Score: 93.89870459388185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Weakly supervised learning generally faces challenges in applicability to various scenarios with diverse weak supervision and in scalability due to the complexity of existing algorithms, thereby hindering the practical deployment. This paper introduces a general framework for learning from weak supervision (GLWS) with a novel algorithm. Central to GLWS is an Expectation-Maximization (EM) formulation, adeptly accommodating various weak supervision sources, including instance partial labels, aggregate statistics, pairwise observations, and unlabeled data. We further present an advanced algorithm that significantly simplifies the EM computational demands using a Non-deterministic Finite Automaton (NFA) along with a forward-backward algorithm, which effectively reduces time complexity from quadratic or factorial often required in existing solutions to linear scale. The problem of learning from arbitrary weak supervision is therefore converted to the NFA modeling of them. GLWS not only enhances the scalability of machine learning models but also demonstrates superior performance and versatility across 11 weak supervision scenarios. We hope our work paves the way for further advancements and practical deployment in this field.

Related papers

• The Power of Resets in Online Reinforcement Learning [73.64852266145387]
  We explore the power of simulators through online reinforcement learning with local simulator access (or, local planning) We show that MDPs with low coverability can be learned in a sample-efficient fashion with only $Qstar$-realizability. We show that the notorious Exogenous Block MDP problem is tractable under local simulator access.
  arXiv  Detail & Related papers  (2024-04-23T18:09:53Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Learning Sparse Graphon Mean Field Games [26.405495663998828]
  Graphon mean field games (GMFGs) enable the scalable analysis of MARL problems that are otherwise intractable. Our paper introduces a novel formulation of GMFGs, called LPGMFGs, which leverages the graph theoretical concept of $Lp$ graphons. This especially includes power law networks which are empirically observed in various application areas and cannot be captured by standard graphons.
  arXiv  Detail & Related papers  (2022-09-08T15:35:42Z)
• A Novel Plug-and-Play Approach for Adversarially Robust Generalization [26.29269757430314]
  We propose a robust framework that employs adversarially robust training to safeguard the machine learning models against perturbed testing data. We achieve this by incorporating the worst-case additive adversarial error within a fixed budget for each sample during model estimation.
  arXiv  Detail & Related papers  (2022-08-19T17:02:55Z)
• Automatic Rule Induction for Efficient Semi-Supervised Learning [56.91428251227253]
  Semi-supervised learning has shown promise in allowing NLP models to generalize from small amounts of labeled data. Pretrained transformer models act as black-box correlation engines that are difficult to explain and sometimes behave unreliably. We propose tackling both of these challenges via Automatic Rule Induction (ARI), a simple and general-purpose framework.
  arXiv  Detail & Related papers  (2022-05-18T16:50:20Z)
• Exploring Viable Algorithmic Options for Learning from Demonstration (LfD): A Parameterized Complexity Approach [0.0]
  In this paper, we show how such a systematic exploration of algorithmic options can be done using parameterized complexity analysis. We show that none of our problems can be solved efficiently either in general or relative to a number of (often simultaneous) restrictions on environments, demonstrations, and policies.
  arXiv  Detail & Related papers  (2022-05-10T15:54:06Z)
• On the Difficulty of Generalizing Reinforcement Learning Framework for Combinatorial Optimization [6.935838847004389]
  Combinatorial optimization problems (COPs) on the graph with real-life applications are canonical challenges in Computer Science. The underlying principle of this approach is to deploy a graph neural network (GNN) for encoding both the local information of the nodes and the graph-structured data. We use the security-aware phone clone allocation in the cloud as a classical quadratic assignment problem (QAP) to investigate whether or not deep RL-based model is generally applicable to solve other classes of such hard problems.
  arXiv  Detail & Related papers  (2021-08-08T19:12:04Z)
• Unsupervised Learning for Robust Fitting:A Reinforcement Learning Approach [25.851792661168698]
  We introduce a novel framework that learns to solve robust model fitting. Unlike other methods, our work is agnostic to the underlying input features. We empirically show that our method outperforms existing learning approaches.
  arXiv  Detail & Related papers  (2021-03-05T07:14:00Z)
• An Integer Linear Programming Framework for Mining Constraints from Data [81.60135973848125]
  We present a general framework for mining constraints from data. In particular, we consider the inference in structured output prediction as an integer linear programming (ILP) problem. We show that our approach can learn to solve 9x9 Sudoku puzzles and minimal spanning tree problems from examples without providing the underlying rules.
  arXiv  Detail & Related papers  (2020-06-18T20:09:53Z)
• Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning [96.78504087416654]
  Motivated by the prevailing paradigm of using unsupervised learning for efficient exploration in reinforcement learning (RL) problems, we investigate when this paradigm is provably efficient. We present a general algorithmic framework that is built upon two components: an unsupervised learning algorithm and a noregret tabular RL algorithm.
  arXiv  Detail & Related papers  (2020-03-15T19:23:59Z)

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.