Inductive Learning
Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization
We consider the penalized distributionally robust optimization (DRO) problem with a closed, convex uncertainty set, a setting that encompasses learning using f -DRO and spectral/ L-risk minimization.
Decision-aware learning for geographical districting
Districting is a complex combinatorial problem that consists in partitioning a geographical area into small districts.
Learning the Latent Causal Structure for Modeling Label Noise
In label-noise learning, the noise transition matrix reveals how an instance transitions from its clean label to its noisy label. Accurately estimating an instance's noise transition matrix is crucial for estimating its clean label.
Data Quality in Imitation Learning
In supervised learning, the question of data quality and curation has been overshadowed in recent years by increasingly more powerful and expressive models that can ingest internet-scale data.
A Appendix A531A.1 Detailed explanation of continuous nature of similarity
In this section, we expand on our observation that similarity between training samples is not binary. Consider the images shown in Figure 6. As a consequence, any similarity between the anchor image and the so-called'negative' examples is completely ignored. Further, all'positive' examples are considered to be The batch size is set to 16000. We train on 4 A100 GPUs.
CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss
The learnt models in the latter domain can then be used for a diverse set of tasks in a zero-shot way, similar to "Contrastive Language-Image
OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning
Semi-supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data. Traditionally, SSL mandates that all classes possess labeled instances.
Label Poisoning is All You Need
In a backdoor attack, an adversary injects corrupted data into a model's training dataset in order to gain control over its predictions on images with a specific attacker-defined trigger. A typical corrupted training example requires altering both the image, by applying the trigger, and the label. Models trained on clean images, therefore, were considered safe from backdoor attacks. However, in some common machine learning scenarios, the training labels are provided by potentially malicious third-parties. This includes crowd-sourced annotation and knowledge distillation. We, hence, investigate a fundamental question: can we launch a successful backdoor attack by only corrupting labels?