A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Proposes CBMI for missing data imputation using labels and input.
Proposes MGPLL for PL learning with non-random noise.
Paper proposes a method to adapt classifiers using complementary labels instead of true labels.
How to accurately and efficiently label data on a mobile device is critical for the success of training machine learning models on mobile devices. Auto-labeling data on mobile devices is challenging, because data is usually incrementally generated and there is possibility of having unknown labels. Furthermore, the rich…
Proposes a data augmentation method to improve multi-label learning performance.
We describe a nonparametric topic model for labeled data. The model uses a mixture of random measures (MRM) as a base distribution of the Dirichlet process (DP) of the HDP framework, so we call it the DP-MRM. To model labeled data, we define a DP distributed random measure for each label, and the resulting model genera…
A method to generate multi-label data from single positive annotations.
Annotating large unlabeled datasets can be a major bottleneck for machine learning applications. We introduce a scheme for inferring labels of unlabeled data at a fraction of the cost of labeling the entire dataset. Our scheme, bounded expectation of label assignment (BELA), greedily queries an oracle (or human labeler…
Doubly robust self-training improves semi-supervised learning by balancing labeled and pseudo-labeled data.
DPBD simplifies labeling functions through interactive demonstrations.
INN method refines clean labeled data from noisy labels.
In many applications the process of generating label information is expensive and time consuming. We present a new method that combines active and semi-supervised deep learning to achieve high generalization performance from a deep convolutional neural network with as few known labels as possible. In a setting where a …
New method for semi-supervised learning in federated learning with and without labels at clients.
A similarity label indicates whether two instances belong to the same class while a class label shows the class of the instance. Without class labels, a multi-class classifier could be learned from similarity-labeled pairwise data by meta classification learning. However, since the similarity label is less informative …
MCAL reduces labeling costs by 6x for auto-labeling data sets.
Semantic labeling for numerical values is a task of assigning semantic labels to unknown numerical attributes. The semantic labels could be numerical properties in ontologies, instances in knowledge bases, or labeled data that are manually annotated by domain experts. In this paper, we refer to semantic labeling as a r…
A new algorithm detects changepoints in labeled and unlabeled data.
Unlike images or videos data which can be easily labeled by human being, sensor data annotation is a time-consuming process. However, traditional methods of human activity recognition require a large amount of such strictly labeled data for training classifiers. In this paper, we present an attention-based convolutiona…
Active WeaSuL uses active learning to improve weak supervision for better model performance.
Annotation of training data is the major bottleneck in the creation of text classification systems. Active learning is a commonly used technique to reduce the amount of training data one needs to label. A crucial aspect of active learning is determining when to stop labeling data. Three potential sources for informing …
TBAL reduces manual annotation but requires validated data.
Clarinet uses complementary labels to train classifiers with less source data.
Noisy labeled data represent a rich source of information that often are easily accessible and cheap to obtain, but label noise might also have many negative consequences if not accounted for. How to fully utilize noisy labels has been studied extensively within the framework of standard supervised machine learning ove…
A federated method for feature selection in multi-label data.
New method selects data for labeling in RKHS to improve regression accuracy.
Conditional Random Fields (CRF) are frequently applied for labeling and segmenting sequence data. Morency et al. (2007) introduced hidden state variables in a labeled CRF structure in order to model the latent dynamics within class labels, thus improving the labeling performance. Such a model is known as Latent-Dynamic…
Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods. One efficient strategy to deal with this problem is to employ resampling techniques before training the classifier. Existing multilabel sampling methods alleviate the (global) im…
New framework combines semi-supervised data programming with subset selection for improved text classification.
Class2Simi reduces noise in noisy label learning by transforming noisy class labels into noisy similarity labels.
Often domain adaptation is performed using a discriminator (domain classifier) to learn domain-invariant feature representations so that a classifier trained on labeled source data will generalize well to unlabeled target data. A line of research stemming from semi-supervised learning uses pseudo labeling to directly g…
A new method for weakly supervised learning that improves model accuracy.
Unsupervised Domain Adaptation (DA) is used to automatize the task of labeling data: an unlabeled dataset (target) is annotated using a labeled dataset (source) from a related domain. We cast domain adaptation as the problem of finding stable labels for target examples. A new definition of label stability is proposed, …
Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization. Classification of such data with a large number of labels and latent dependencies among them is a challenging task, and it becomes even more challenging when the data is received onl…
Practically, we are often in the dilemma that the labeled data at hand are inadequate to train a reliable classifier, and more seriously, some of these labeled data may be mistakenly labeled due to the various human factors. Therefore, this paper proposes a novel semi-supervised learning paradigm that can handle both l…
Study evaluates graph-based semi-supervised learning under noisy label conditions.
An active learner is given a hypothesis class, a large set of unlabeled examples and the ability to interactively query labels to an oracle of a subset of these examples; the goal of the learner is to learn a hypothesis in the class that fits the data well by making as few label queries as possible. This work addresses…
Paper tackles informative labels in semi-supervised learning, proposing debiasing methods.
Semi-supervised learning benefits from informative missing labels, improving classifier performance.
Extends risk control to adaptive data collection, anytime-valid guarantees.
As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but inferring labels from this metadata introduces errors due to unreliable inputs, and limitations in the mapping. There is, however, little r…
SMART is an open source web application designed to help data scientists and research teams efficiently build labeled training data sets for supervised machine learning tasks. SMART provides users with an intuitive interface for creating labeled data sets, supports active learning to help reduce the required amount of …
Graph-based multi-label classifier extends CULP for multi-label data.
Proposes methods to improve multi-label learning by addressing local label imbalance.
Majority Vote is optimal for reliable data labeling under certain conditions.
FinALBERT predicts stock prices using labelled Stocktwits data.
Paper proposes a method to recover accurate labels from partially valid data in multi-label learning.
DP models misspecify LF dependencies, leading to significant performance errors.
ActiveLab improves classifier accuracy with fewer annotations by re-labeling.