Err
Err
Related papers
- Dissecting Representation Misalignment in Contrastive Learning via Influence Function [15.28417468377201]
We introduce the Extended Influence Function for Contrastive Loss (ECIF), an influence function crafted for contrastive loss.
ECIF considers both positive and negative samples and provides a closed-form approximation of contrastive learning models.
Building upon ECIF, we develop a series of algorithms for data evaluation, misalignment detection, and misprediction trace-back tasks.
arXiv Detail & Related papers (2024-11-18T15:45:41Z) - Exploiting the Data Gap: Utilizing Non-ignorable Missingness to Manipulate Model Learning [13.797822374912773]
Adversarial Missingness (AM) attacks are motivated by maliciously engineering non-ignorable missingness mechanisms.
In this work we focus on associational learning in the context of AM attacks.
We formulate the learning of the adversarial missingness mechanism as a bi-level optimization.
arXiv Detail & Related papers (2024-09-06T17:10:28Z) - Understanding Uncertainty-based Active Learning Under Model Mismatch [16.361254095103615]
Uncertainty-based Active Learning (UAL) operates by querying the label(s) of pivotal samples from an unlabeled pool selected based on the prediction uncertainty.
The efficacy of UAL depends on the model capacity as well as the adopted uncertainty-based acquisition function.
arXiv Detail & Related papers (2024-08-24T23:37:08Z) - DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation [18.77296551727931]
We propose DECIDER, a novel approach that leverages priors from large language models (LLMs) and vision-language models (VLMs) to detect failures in image models.
DECIDER consistently achieves state-of-the-art failure detection performance, significantly outperforming baselines in terms of the overall Matthews correlation coefficient.
arXiv Detail & Related papers (2024-08-01T07:08:11Z) - Learning Latent Graph Structures and their Uncertainty [63.95971478893842]
Graph Neural Networks (GNNs) use relational information as an inductive bias to enhance the model's accuracy.
As task-relevant relations might be unknown, graph structure learning approaches have been proposed to learn them while solving the downstream prediction task.
arXiv Detail & Related papers (2024-05-30T10:49:22Z) - Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals [91.59906995214209]
We propose a new evaluation method, Counterfactual Attentiveness Test (CAT)
CAT uses counterfactuals by replacing part of the input with its counterpart from a different example, expecting an attentive model to change its prediction.
We show that GPT3 becomes less attentive with an increased number of demonstrations, while its accuracy on the test data improves.
arXiv Detail & Related papers (2023-11-16T06:27:35Z) - Stubborn Lexical Bias in Data and Models [50.79738900885665]
We use a new statistical method to examine whether spurious patterns in data appear in models trained on the data.
We apply an optimization approach to *reweight* the training data, reducing thousands of spurious correlations.
Surprisingly, though this method can successfully reduce lexical biases in the training data, we still find strong evidence of corresponding bias in the trained models.
arXiv Detail & Related papers (2023-06-03T20:12:27Z) - PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning [58.85063149619348]
We propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows.
Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets.
arXiv Detail & Related papers (2023-01-25T16:34:43Z) - Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active
Learning [1.6752182911522522]
We show that uncertainty sampling is sufficient to achieve exploration versus exploitation in graph-based active learning.
In particular, we use a recently developed algorithm, Poisson ReWeighted Laplace Learning (PWLL) for the classifier.
We present experimental results on a number of graph-based image classification problems.
arXiv Detail & Related papers (2022-10-27T22:07:53Z) - Accurate and Robust Feature Importance Estimation under Distribution
Shifts [49.58991359544005]
PRoFILE is a novel feature importance estimation method.
We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
arXiv Detail & Related papers (2020-09-30T05:29:01Z) - Estimating Structural Target Functions using Machine Learning and
Influence Functions [103.47897241856603]
We propose a new framework for statistical machine learning of target functions arising as identifiable functionals from statistical models.
This framework is problem- and model-agnostic and can be used to estimate a broad variety of target parameters of interest in applied statistics.
We put particular focus on so-called coarsening at random/doubly robust problems with partially unobserved information.
arXiv Detail & Related papers (2020-08-14T16:48:29Z)
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.