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  • On Learning in the Presence of Underrepresented Groups
    Let me introduce to you our latest work, which has been accepted by ICML 2023: Change is Hard: A Closer Look at Subpopulation Shift Machine learning models have shown great potential in many applications, but they often perform poorly on subgroups that are underrepresented in the training data Understanding the variation in mechanisms that cause such subpopulation shifts, and how algorithms
  • Change is Hard: A Closer Look at Subpopulation Shift
    Machine learning models often perform poorly on subgroups that are underrepresented in the training data Yet, little is understood on the variation in mechanisms that cause subpopulation shifts, and how algorithms generalize across such diverse shifts at scale In this work, we provide a fine-grained analysis of subpopulation shift
  • Is it always better to use the whole dataset to train the final model?
    $\begingroup$ +1'd for the effort, even though I don't fully agree :) e g when you mention "In terms of expected performance, using all of the data is no worse than using some of the data, and potentially better " I don't see the reasoning behind it On the other hand, the 2nd point that you mention seems very important, cross validation! so essentially you train validate with all samples
  • Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift
    based incremental learning method that sequentially adapts classifiers to new subpopulations using margin-enforce loss, aiming to balance acquisition and forgetting in the presence of subpopulation shift Just train twice (JTT) (Liu et al , 2021) trains the model twice, with the second stage mini-mizing the loss over training examples from a
  • Benchmarks for Subpopulation Shift – gradient science
    Paper Code Hierarchies In our new paper, we develop a framework for simulating realistic subpopulation shifts between training and deployment conditions for machine learning models Evaluating standard models on the resulting benchmarks reveals that these models are highly sensitive to such shifts Moreover, training models to be invariant to existing families of synthetic data perturbations
  • Model-based Metrics: Sample-efficient Estimates of Predictive Model . . .
    Machine learning models − now commonly developed to screen, diagnose, or predict health conditions − are evaluated with a variety of performance metrics An important first step in assessing the practical utility of a model is to evaluate its average performance over an entire population of interest
  • Change is Hard: A Closer Look at Subpopulation Shift - arXiv. org
    Subpopulation Shift Machine learning models frequently experience performance degradation under subpopulation shift, where the proportion of some subpopulations differ between the training and test (Cai et al ,2021;Koh et al , 2021) Depending on the definition of such subpopulations, this could lead to vastly different problem settings Prior
  • Confidence-Based Model Selection: When to Take Shortcuts for . . .
    Prior work has studied this phenomenon in the context of human decision-making: and for each subpopulation we expect one model in particular to be the best, then the model assignment that achieves the lowest entropy will achieve highest performance A new perspective on generalization and model simplicity in machine learning arXiv


















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