Understanding new tasks through the lens of training data via exponential tilting

• URL: http://arxiv.org/abs/2205.13577v1
• Date: Thu, 26 May 2022 18:38:43 GMT
• Title: Understanding new tasks through the lens of training data via exponential tilting
• Authors: Subha Maity, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun
• Abstract summary: We consider the problem of reweighing the training samples to gain insights into the distribution of the target task. We formulate a distribution shift model based on the exponential tilt assumption and learn train data importance weights. The learned train data weights can then be used for downstream tasks such as target performance evaluation, fine-tuning, and model selection.
• Score: 43.33775132139584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deploying machine learning models to new tasks is a major challenge despite the large size of the modern training datasets. However, it is conceivable that the training data can be reweighted to be more representative of the new (target) task. We consider the problem of reweighing the training samples to gain insights into the distribution of the target task. Specifically, we formulate a distribution shift model based on the exponential tilt assumption and learn train data importance weights minimizing the KL divergence between labeled train and unlabeled target datasets. The learned train data weights can then be used for downstream tasks such as target performance evaluation, fine-tuning, and model selection. We demonstrate the efficacy of our method on Waterbirds and Breeds benchmarks.

Related papers

• Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification [34.37262622415682]
  We propose a new adaptation framework called Data Adaptive Traceback. Specifically, we utilize a zero-shot-based method to extract the most downstream task-related subset of the pre-training data. We adopt a pseudo-label-based semi-supervised technique to reuse the pre-training images and a vision-language contrastive learning method to address the confirmation bias issue in semi-supervised learning.
  arXiv  Detail & Related papers  (2024-07-11T18:01:58Z)
• Distilled Datamodel with Reverse Gradient Matching [74.75248610868685]
  We introduce an efficient framework for assessing data impact, comprising offline training and online evaluation stages. Our proposed method achieves comparable model behavior evaluation while significantly speeding up the process compared to the direct retraining method.
  arXiv  Detail & Related papers  (2024-04-22T09:16:14Z)
• Diffusion-based Neural Network Weights Generation [85.6725307453325]
  We propose an efficient and adaptive transfer learning scheme through dataset-conditioned pretrained weights sampling. Specifically, we use a latent diffusion model with a variational autoencoder that can reconstruct the neural network weights.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• ALP: Action-Aware Embodied Learning for Perception [60.64801970249279]
  We introduce Action-Aware Embodied Learning for Perception (ALP) ALP incorporates action information into representation learning through a combination of optimizing a reinforcement learning policy and an inverse dynamics prediction objective. We show that ALP outperforms existing baselines in several downstream perception tasks.
  arXiv  Detail & Related papers  (2023-06-16T21:51:04Z)
• TIDo: Source-free Task Incremental Learning in Non-stationary Environments [0.0]
  Updating a model-based agent to learn new target tasks requires us to store past training data. Few-shot task incremental learning methods overcome the limitation of labeled target datasets. We propose a one-shot task incremental learning approach that can adapt to non-stationary source and target tasks.
  arXiv  Detail & Related papers  (2023-01-28T02:19:45Z)
• An Exploration of Data Efficiency in Intra-Dataset Task Transfer for Dialog Understanding [65.75873687351553]
  This study explores the effects of varying quantities of target task training data on sequential transfer learning in the dialog domain. Unintuitively, our data shows that often target task training data size has minimal effect on how sequential transfer learning performs compared to the same model without transfer learning.
  arXiv  Detail & Related papers  (2022-10-21T04:36:46Z)
• Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification [8.998976678920236]
  We propose a tAsk-auGmented actIve meta-LEarning (AGILE) method to efficiently adapt Deep Neural Networks to new tasks. AGILE combines a meta-learning algorithm with a novel task augmentation technique which we use to generate an initial adaptive model. We show that the proposed task-augmented meta-learning framework can learn to classify new cell types after a single gradient step.
  arXiv  Detail & Related papers  (2020-07-09T18:03:12Z)
• Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks [27.44348371795822]
  We develop a statistical minimax framework to characterize the limits of transfer learning. We derive a lower-bound for the target generalization error achievable by any algorithm as a function of the number of labeled source and target data.
  arXiv  Detail & Related papers  (2020-06-16T22:49:26Z)
• Learning Diverse Representations for Fast Adaptation to Distribution Shift [78.83747601814669]
  We present a method for learning multiple models, incorporating an objective that pressures each to learn a distinct way to solve the task. We demonstrate our framework's ability to facilitate rapid adaptation to distribution shift.
  arXiv  Detail & Related papers  (2020-06-12T12:23:50Z)

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.