Learning Implicitly with Noisy Data in Linear Arithmetic

• URL: http://arxiv.org/abs/2010.12619v2
• Date: Tue, 7 Sep 2021 16:58:57 GMT
• Title: Learning Implicitly with Noisy Data in Linear Arithmetic
• Authors: Alexander P. Rader, Ionela G. Mocanu, Vaishak Belle and Brendan Juba
• Abstract summary: We extend implicit learning in PAC-Semantics to handle intervals and threshold uncertainty in the language of linear arithmetic. We show that our implicit approach to learning optimal linear programming objective constraints significantly outperforms an explicit approach in practice.
• Score: 94.66549436482306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust learning in expressive languages with real-world data continues to be a challenging task. Numerous conventional methods appeal to heuristics without any assurances of robustness. While probably approximately correct (PAC) Semantics offers strong guarantees, learning explicit representations is not tractable, even in propositional logic. However, recent work on so-called "implicit" learning has shown tremendous promise in terms of obtaining polynomial-time results for fragments of first-order logic. In this work, we extend implicit learning in PAC-Semantics to handle noisy data in the form of intervals and threshold uncertainty in the language of linear arithmetic. We prove that our extended framework keeps the existing polynomial-time complexity guarantees. Furthermore, we provide the first empirical investigation of this hitherto purely theoretical framework. Using benchmark problems, we show that our implicit approach to learning optimal linear programming objective constraints significantly outperforms an explicit approach in practice.

Related papers

• Statistical Inference for Temporal Difference Learning with Linear Function Approximation [62.69448336714418]
  Temporal Difference (TD) learning, arguably the most widely used for policy evaluation, serves as a natural framework for this purpose. In this paper, we study the consistency properties of TD learning with Polyak-Ruppert averaging and linear function approximation, and obtain three significant improvements over existing results.
  arXiv  Detail & Related papers  (2024-10-21T15:34:44Z)
• DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications [59.01527054553122]
  Linear temporal logic (LTL) has recently been adopted as a powerful formalism for specifying complex, temporally extended tasks in reinforcement learning (RL) Existing approaches suffer from several shortcomings: they are often only applicable to finite-horizon fragments, are restricted to suboptimal solutions, and do not adequately handle safety constraints. In this work, we propose a novel learning approach to address these concerns. Our method leverages the structure of B"uchia, which explicitly represent the semantics of automat- specifications, to learn policies conditioned on sequences of truth assignments that lead to satisfying the desired formulae.
  arXiv  Detail & Related papers  (2024-10-06T21:30:38Z)
• A Controlled Study on Long Context Extension and Generalization in LLMs [85.4758128256142]
  Broad textual understanding and in-context learning require language models that utilize full document contexts. Due to the implementation challenges associated with directly training long-context models, many methods have been proposed for extending models to handle long contexts. We implement a controlled protocol for extension methods with a standardized evaluation, utilizing consistent base models and extension data.
  arXiv  Detail & Related papers  (2024-09-18T17:53:17Z)
• Interpretable Anomaly Detection via Discrete Optimization [1.7150329136228712]
  We propose a framework for learning inherently interpretable anomaly detectors from sequential data. We show that this problem is computationally hard and develop two learning algorithms based on constraint optimization. Using a prototype implementation, we demonstrate that our approach shows promising results in terms of accuracy and F1 score.
  arXiv  Detail & Related papers  (2023-03-24T16:19:15Z)
• Making Linear MDPs Practical via Contrastive Representation Learning [101.75885788118131]
  It is common to address the curse of dimensionality in Markov decision processes (MDPs) by exploiting low-rank representations. We consider an alternative definition of linear MDPs that automatically ensures normalization while allowing efficient representation learning. We demonstrate superior performance over existing state-of-the-art model-based and model-free algorithms on several benchmarks.
  arXiv  Detail & Related papers  (2022-07-14T18:18:02Z)
• 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)
• Stochastic convex optimization for provably efficient apprenticeship learning [1.0609815608017066]
  We consider large-scale Markov decision processes (MDPs) with an unknown cost function. We employ convex optimization tools to address the problem of imitation learning, which consists of learning a policy from a finite set of expert demonstrations.
  arXiv  Detail & Related papers  (2021-12-31T19:47:57Z)
• Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations [1.0609815608017066]
  We consider large-scale Markov decision processes with an unknown cost function and address the problem of learning a policy from a finite set of expert demonstrations. Existing inverse reinforcement learning methods come with strong theoretical guarantees, but are computationally expensive. We introduce a novel bilinear saddle-point framework using Lagrangian duality to bridge the gap between theory and practice.
  arXiv  Detail & Related papers  (2021-12-28T05:47:24Z)
• A Boosting Approach to Reinforcement Learning [59.46285581748018]
  We study efficient algorithms for reinforcement learning in decision processes whose complexity is independent of the number of states. We give an efficient algorithm that is capable of improving the accuracy of such weak learning methods.
  arXiv  Detail & Related papers  (2021-08-22T16:00:45Z)

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.