Continual Density Ratio Estimation in an Online Setting

• URL: http://arxiv.org/abs/2103.05276v1
• Date: Tue, 9 Mar 2021 07:56:36 GMT
• Title: Continual Density Ratio Estimation in an Online Setting
• Authors: Yu Chen, Song Liu, Tom Diethe, Peter Flach
• Abstract summary: In online applications with streaming data, awareness of how far the training or test set has shifted away from the original dataset can be crucial to the performance of the model. We propose a novel method, Continual Density Ratio Estimation (CDRE), for estimating density ratios between the initial and current distributions. We demonstrate that CDRE can be more accurate than standard DRE in terms of estimating divergences between distributions, despite not requiring samples from the original distribution.
• Score: 12.516472146904375
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In online applications with streaming data, awareness of how far the training or test set has shifted away from the original dataset can be crucial to the performance of the model. However, we may not have access to historical samples in the data stream. To cope with such situations, we propose a novel method, Continual Density Ratio Estimation (CDRE), for estimating density ratios between the initial and current distributions ($p/q_t$) of a data stream in an iterative fashion without the need of storing past samples, where $q_t$ is shifting away from $p$ over time $t$. We demonstrate that CDRE can be more accurate than standard DRE in terms of estimating divergences between distributions, despite not requiring samples from the original distribution. CDRE can be applied in scenarios of online learning, such as importance weighted covariate shift, tracing dataset changes for better decision making. In addition, (CDRE) enables the evaluation of generative models under the setting of continual learning. To the best of our knowledge, there is no existing method that can evaluate generative models in continual learning without storing samples from the original distribution.

Related papers

• Value Flows [90.1510269525399]
  This paper uses modern, flexible flow-based models to estimate the full future return distributions.<n>Building upon the learned flow models, we estimate the return uncertainty of distinct states using a new flow derivative ODE.<n>Experiments on $37$ state-based and $25$ image-based benchmark tasks demonstrate that Value Flows achieves a $1.3times$ improvement on average in success rates.
  arXiv  Detail & Related papers  (2025-10-09T00:57:40Z)
• Backpropagation-Free Test-Time Adaptation via Probabilistic Gaussian Alignment [16.352863226512984]
  Test-time adaptation (TTA) enhances the zero-shot robustness under distribution shifts by leveraging unlabeled test data during inference.<n>Most methods rely on backpropagation or iterative optimization, which limits scalability and hinders real-time deployment.<n>We propose ADAPT, an Advanced Distribution-Aware and back propagation-free Test-time adaptation method.
  arXiv  Detail & Related papers  (2025-08-21T13:42:49Z)
• DOTA: Distributional Test-Time Adaptation of Vision-Language Models [52.98590762456236]
  Training-free test-time dynamic adapter (TDA) is a promising approach to address this issue. We propose a simple yet effective method for DistributiOnal Test-time Adaptation (Dota) Dota continually estimates the distributions of test samples, allowing the model to continually adapt to the deployment environment.
  arXiv  Detail & Related papers  (2024-09-28T15:03:28Z)
• Distribution Alignment for Fully Test-Time Adaptation with Dynamic Online Data Streams [19.921480334048756]
  Test-Time Adaptation (TTA) enables adaptation and inference in test data streams with domain shifts from the source. We propose a novel Distribution Alignment loss for TTA. We surpass existing methods in non-i.i.d. scenarios and maintain competitive performance under the ideal i.i.d. assumption.
  arXiv  Detail & Related papers  (2024-07-16T19:33:23Z)
• Rejection via Learning Density Ratios [50.91522897152437]
  Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions.<n>We propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance.<n>Our framework is tested empirically over clean and noisy datasets.
  arXiv  Detail & Related papers  (2024-05-29T01:32:17Z)
• DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning [14.952800864366512]
  conservatism should be incorporated into the algorithm to balance accurate offline data and imprecise model data. This paper proposes a milDly cOnservative Model-bAsed offlINe RL algorithm (DOMAIN) without estimating model uncertainty. The results of extensive experiments show that DOMAIN outperforms prior RL algorithms on the D4RL dataset benchmark.
  arXiv  Detail & Related papers  (2023-09-16T08:39:28Z)
• Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent [97.64313409741614]
  We propose to enforce a emphconsistency property which states that predictions of the model on its own generated data are consistent across time. We show that our novel training objective yields state-of-the-art results for conditional and unconditional generation in CIFAR-10 and baseline improvements in AFHQ and FFHQ.
  arXiv  Detail & Related papers  (2023-02-17T18:45:04Z)
• Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach [18.007258270845107]
  We propose LUD-VAE, a deep generative method to learn the joint probability density function from data sampled from marginal distributions. We apply our method to real-world image denoising and super-resolution tasks and train the models using the synthetic data generated by the LUD-VAE.
  arXiv  Detail & Related papers  (2022-04-21T13:27:17Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning [78.83598532168256]
  Marginal-likelihood based model-selection is rarely used in deep learning due to estimation difficulties. Our work shows that marginal likelihoods can improve generalization and be useful when validation data is unavailable.
  arXiv  Detail & Related papers  (2021-04-11T09:50:24Z)
• TraDE: Transformers for Density Estimation [101.20137732920718]
  TraDE is a self-attention-based architecture for auto-regressive density estimation. We present a suite of tasks such as regression using generated samples, out-of-distribution detection, and robustness to noise in the training data.
  arXiv  Detail & Related papers  (2020-04-06T07:32:51Z)
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network [66.26231423824089]
  We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models.
  arXiv  Detail & Related papers  (2020-03-04T12:27:36Z)

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.