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Found 34 papers, 8 papers with code
Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias
We further show that if spurious features have a small enough noise-to-signal ratio, the network's output on the majority of examples in a class will be almost exclusively determined by the spurious features and will be nearly invariant to the core feature.
Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression
However, supervised CL is prone to collapsing representations of subclasses within a class by not capturing all their features, and unsupervised CL may suppress harder class-relevant features by focusing on learning easy class-irrelevant features; both significantly compromise representation quality.
Eliminating Spurious Correlations from Pre-trained Models via Data Mixing
Machine learning models pre-trained on large datasets have achieved remarkable convergence and robustness properties.
Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning
Spurious correlations that degrade model generalization or lead the model to be right for the wrong reasons are one of the main robustness concerns for real-world deployments.
A Self-supervised Framework for Improved Data-Driven Monitoring of Stress via Multi-modal Passive Sensing
Recent advances in remote health monitoring systems have significantly benefited patients and played a crucial role in improving their quality of life.
High Probability Bounds for Stochastic Continuous Submodular Maximization
This implies that for a particular run of the algorithms, the solution may be much worse than the provided guarantee in expectation.
Robust Contrastive Language-Image Pretraining against Adversarial Attacks
Contrastive vision-language representation learning has achieved state-of-the-art performance for zero-shot classification, by learning from millions of image-caption pairs crawled from the internet.
Contrastive Learning under Heterophily
Effectively, the high-pass filter captures the dissimilarity between nodes in a neighborhood and the low-pass filter captures the similarity between neighboring nodes. Contrasting the two filtered views allows HLCL to learn rich node representations for graphs, under heterophily and homophily. Empirically, HLCL outperforms state-of-the-art graph CL methods on benchmark heterophily datasets and large-scale real-world datasets by up to 10%.
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least
Interestingly, we also discover the subsets that contribute the most to contrastive learning are those that contribute the least to supervised learning.
Generating High Fidelity Synthetic Data via Coreset selection and Entropic Regularization
Generative models have the ability to synthesize data points drawn from the data distribution, however, not all generated samples are high quality.
Not All Poisons are Created Equal: Robust Training against Data Poisoning
Data poisoning causes misclassification of test time target examples by injecting maliciously crafted samples in the training data.
Data-Efficient Augmentation for Training Neural Networks
To address this, we propose a rigorous technique to select subsets of data points that when augmented, closely capture the training dynamics of full data augmentation.
The Final Ascent: When Bigger Models Generalize Worse on Noisy-Labeled Data
We show that under a sufficiently large noise-to-sample size ratio, generalization error eventually increases with model size.
Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attacks
We make the key observation that attacks introduce local sharp regions of high training loss, which when minimized, results in learning the adversarial perturbations and makes the attack successful.
Adaptive Second Order Coresets for Data-efficient Machine Learning
We propose AdaCore, a method that leverages the geometry of the data to extract subsets of the training examples for efficient machine learning.
Investigating Why Contrastive Learning Benefits Robustness Against Label Noise
Self-supervised Contrastive Learning (CL) has been recently shown to be very effective in preventing deep networks from overfitting noisy labels.
CrossWalk: Fairness-enhanced Node Representation Learning
The potential for machine learning systems to amplify social inequities and unfairness is receiving increasing popular and academic attention.
Coresets for Robust Training of Deep Neural Networks against Noisy Labels
Modern neural networks have the capacity to overfit noisy labels frequently found in real-world datasets.
Coresets for Robust Training of Neural Networks against Noisy Labels
Modern neural networks have the capacity to overfit noisy labels frequently found in real-world datasets.
Ranked #36 on
Image Classification
on mini WebVision 1.0
Coresets for Accelerating Incremental Gradient Methods
But because at each epoch the gradients are computed only on the subset S, we obtain a speedup that is inversely proportional to the size of S. Our subset selection algorithm is fully general and can be applied to most IG methods.
Selection via Proxy: Efficient Data Selection for Deep Learning
By removing hidden layers from the target model, using smaller architectures, and training for fewer epochs, we create proxies that are an order of magnitude faster to train.
Coresets for Data-efficient Training of Machine Learning Models
Here we develop CRAIG, a method to select a weighted subset (or coreset) of training data that closely estimates the full gradient by maximizing a submodular function.
Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples
In a number of situations, collecting a function value for every data point may be prohibitively expensive, and random sampling ignores any structure in the underlying data.
On the Fairness of Time-Critical Influence Maximization in Social Networks
As we show in this paper, the time-criticality of the information could further exacerbate the disparity of influence across groups.
Social and Information Networks Computers and Society
Select Via Proxy: Efficient Data Selection For Training Deep Networks
In our approach, we first train a small proxy model quickly, which we then use to estimate the utility of individual training data points, and then select the most informative ones for training the large target model.
Dynamic Network Model from Partial Observations
We propose a novel framework for providing a non-parametric dynamic network model--based on a mixture of coupled hierarchical Dirichlet processes-- based on data capturing cascade node infection times.
Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten"
How can we summarize a dynamic data stream when elements selected for the summary can be deleted at any time?
Streaming Non-monotone Submodular Maximization: Personalized Video Summarization on the Fly
The need for real time analysis of rapidly producing data streams (e. g., video and image streams) motivated the design of streaming algorithms that can efficiently extract and summarize useful information from massive data "on the fly".
Data Structures and Algorithms Information Retrieval
Fast Distributed Submodular Cover: Public-Private Data Summarization
The goal is to provide a succinct summary of massive dataset, ideally as small as possible, from which customized summaries can be built for each user, i. e. it can contain elements from the public data (for diversity) and users' private data (for personalization).
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains
Submodular continuous functions are a category of (generally) non-convex/non-concave functions with a wide spectrum of applications.
Distributed Submodular Cover: Succinctly Summarizing Massive Data
In this paper, we formalize this challenge as a submodular cover problem.
Distributed Submodular Maximization
Such problems can often be reduced to maximizing a submodular set function subject to various constraints.
Lazier Than Lazy Greedy
Is it possible to maximize a monotone submodular function faster than the widely used lazy greedy algorithm (also known as accelerated greedy), both in theory and practice?
Distributed Submodular Maximization: Identifying Representative Elements in Massive Data
Such problems can often be reduced to maximizing a submodular set function subject to cardinality constraints.
