Are all models wrong? Fundamental limits in distribution-free empirical model falsification

• URL: http://arxiv.org/abs/2502.06765v1
• Date: Mon, 10 Feb 2025 18:44:30 GMT
• Title: Are all models wrong? Fundamental limits in distribution-free empirical model falsification
• Authors: Manuel M. Müller, Yuetian Luo, Rina Foygel Barber,
• Abstract summary: We establish a model-agnostic, fundamental hardness result for the problem of constructing a lower bound on the best test error achievable over a model class. We examine its implications on specific model classes such as tree-based methods and linear regression.
• Score: 5.059120569845977
• License:
• Abstract: In statistics and machine learning, when we train a fitted model on available data, we typically want to ensure that we are searching within a model class that contains at least one accurate model -- that is, we would like to ensure an upper bound on the model class risk (the lowest possible risk that can be attained by any model in the class). However, it is also of interest to establish lower bounds on the model class risk, for instance so that we can determine whether our fitted model is at least approximately optimal within the class, or, so that we can decide whether the model class is unsuitable for the particular task at hand. Particularly in the setting of interpolation learning where machine learning models are trained to reach zero error on the training data, we might ask if, at the very least, a positive lower bound on the model class risk is possible -- or are we unable to detect that "all models are wrong"? In this work, we answer these questions in a distribution-free setting by establishing a model-agnostic, fundamental hardness result for the problem of constructing a lower bound on the best test error achievable over a model class, and examine its implications on specific model classes such as tree-based methods and linear regression.

Related papers

• Universality in Transfer Learning for Linear Models [18.427215139020625]
  We study the problem of transfer learning in linear models for both regression and binary classification. We provide an exact and rigorous analysis and relate generalization errors (in regression) and classification errors (in binary classification) for the pretrained and fine-tuned models.
  arXiv  Detail & Related papers  (2024-10-03T03:09:09Z)
• Plan To Predict: Learning an Uncertainty-Foreseeing Model for Model-Based Reinforcement Learning [32.24146877835396]
  We propose emphPlan To Predict (P2P), a framework that treats the model rollout process as a sequential decision making problem. We show that P2P achieves state-of-the-art performance on several challenging benchmark tasks.
  arXiv  Detail & Related papers  (2023-01-20T10:17:22Z)
• Dataless Knowledge Fusion by Merging Weights of Language Models [51.8162883997512]
  Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. This creates a barrier to fusing knowledge across individual models to yield a better single model. We propose a dataless knowledge fusion method that merges models in their parameter space.
  arXiv  Detail & Related papers  (2022-12-19T20:46:43Z)
• Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers [66.36045164286854]
  We analyze a set of existing bias features and demonstrate there is no single model that works best for all the cases. By choosing an appropriate bias model, we can obtain a better robustness result than baselines with a more sophisticated model design.
  arXiv  Detail & Related papers  (2022-10-28T17:52:10Z)
• Attaining Class-level Forgetting in Pretrained Model using Few Samples [18.251805180282346]
  In the future, some classes may become restricted due to privacy/ethical concerns. We propose a novel approach to address this problem without affecting the model's prediction power for the remaining classes.
  arXiv  Detail & Related papers  (2022-10-19T15:36:01Z)
• Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning [92.89846887298852]
  Consider making a prediction over new test data without any opportunity to learn from a training set of labelled data. Give access to a set of expert models and their predictions alongside some limited information about the dataset used to train them.
  arXiv  Detail & Related papers  (2022-10-11T10:20:31Z)
• Sufficiently Accurate Model Learning for Planning [119.80502738709937]
  This paper introduces the constrained Sufficiently Accurate model learning approach. It provides examples of such problems, and presents a theorem on how close some approximate solutions can be. The approximate solution quality will depend on the function parameterization, loss and constraint function smoothness, and the number of samples in model learning.
  arXiv  Detail & Related papers  (2021-02-11T16:27:31Z)
• Distilling Interpretable Models into Human-Readable Code [71.11328360614479]
  Human-readability is an important and desirable standard for machine-learned model interpretability. We propose to train interpretable models using conventional methods, and then distill them into concise, human-readable code. We describe a piecewise-linear curve-fitting algorithm that produces high-quality results efficiently and reliably across a broad range of use cases.
  arXiv  Detail & Related papers  (2021-01-21T01:46:36Z)
• On Statistical Efficiency in Learning [37.08000833961712]
  We address the challenge of model selection to strike a balance between model fitting and model complexity. We propose an online algorithm that sequentially expands the model complexity to enhance selection stability and reduce cost. Experimental studies show that the proposed method has desirable predictive power and significantly less computational cost than some popular methods.
  arXiv  Detail & Related papers  (2020-12-24T16:08:29Z)
• Learning from others' mistakes: Avoiding dataset biases without modeling them [111.17078939377313]
  State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended task. Previous work has demonstrated effective methods to circumvent these issues when knowledge of the bias is available. We show a method for training models that learn to ignore these problematic correlations.
  arXiv  Detail & Related papers  (2020-12-02T16:10:54Z)
• Bootstrapped model learning and error correction for planning with uncertainty in model-based RL [1.370633147306388]
  A natural aim is to learn a model that reflects accurately the dynamics of the environment. This paper explores the problem of model misspecification through uncertainty-aware reinforcement learning agents. We propose a bootstrapped multi-headed neural network that learns the distribution of future states and rewards.
  arXiv  Detail & Related papers  (2020-04-15T15:41:21Z)

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.