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.
ITCA optimizes label combination for ambiguous outcomes in multi-class classification.
This paper considers the challenge of evaluating a set of classifiers, as done in shared task evaluations like the KDD Cup or NIST TREC, without expert labels. While expert labels provide the traditional cornerstone for evaluating statistical learners, limited or expensive access to experts represents a practical bottl…
Multi-label text classification is a popular machine learning task where each document is assigned with multiple relevant labels. This task is challenging due to high dimensional features and correlated labels. Multi-label text classifiers need to be carefully regularized to prevent the severe over-fitting in the high …
Multi-label classification (MLC) assigns multiple labels to each sample. Prior studies show that MLC can be transformed to a sequence prediction problem with a recurrent neural network (RNN) decoder to model the label dependency. However, training a RNN decoder requires a predefined order of labels, which is not direct…
TIMELY improves consistency in labeling blood cell images.
Unified model combines feature and label propagation for semi-supervised classification.
A new method combines experts' opinions to train regression models with noisy labels.
In the era of big data, a large amount of noisy and incomplete data can be collected from multiple sources for prediction tasks. Combining multiple models or data sources helps to counteract the effects of low data quality and the bias of any single model or data source, and thus can improve the robustness and the perf…
Proposes a constrained labeling method for weakly supervised learning.
AV-CPL uses continuous pseudo-labels for AVSR combining labeled and unlabeled data.
In this paper we revisit the idea of pseudo-labeling in the context of semi-supervised learning where a learning algorithm has access to a small set of labeled samples and a large set of unlabeled samples. Pseudo-labeling works by applying pseudo-labels to samples in the unlabeled set by using a model trained on the co…
Extreme multi-label classification (XMC) refers to supervised multi-label learning involving hundreds of thousand or even millions of labels. In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space…
Unified framework DDNs for multi-label classification, improving inference efficiency.
Introduces SoRR for aggregating losses in supervised learning.
A method for collecting human supervision that combines rules and instance labels.
Despite the success of ensemble classification methods in multi-class classification problems, ensemble methods based on approaches other than bagging have not been widely explored for multi-label classification problems. The Kalman Filter-based Heuristic Ensemble (KFHE) is an ensemble method that exploits the sensor f…
Binary PheNorm extends phenotype labeling for EHRs using binary silver labels.
Paper tackles instance-dependent label noise by approximating it with part-dependent noise.
Study on federated learning with private label sets, showing privacy benefits without significant accuracy loss.
DoubleMatch combines pseudo-labeling with self-supervision for SSL.
Improving a semi-supervised image segmentation task has the option of adding more unlabelled images, labelling the unlabelled images or combining both, as neither image acquisition nor expert labelling can be considered trivial in most clinical applications. With a laparoscopic liver image segmentation application, we …
New method for semi-supervised learning in federated learning with and without labels at clients.
Exploiting dependencies between labels is considered to be crucial for multi-label classification. Rules are able to expose label dependencies such as implications, subsumptions or exclusions in a human-comprehensible and interpretable manner. However, the induction of rules with multiple labels in the head is particul…
We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of sequence cl…
Multi-label classification aims to classify instances with discrete non-exclusive labels. Most approaches on multi-label classification focus on effective adaptation or transformation of existing binary and multi-class learning approaches but fail in modelling the joint probability of labels or do not preserve generali…
This work analyzes two methods for combining multiple binary labels in bipartite ranking.
The increase in the number of Internet users and the strong interaction brought by Web 2.0 made the Opinion Mining an important task in the area of natural language processing. Although several methods are capable of performing this task, few use multi-label classification, where there is a group of true labels for eac…
Combines foundation models with weak supervision to improve NLP and video tasks.
New framework combines semi-supervised data programming with subset selection for improved text classification.
Adaptive sampling detects local concept drift with limited labels.
pRSL combines probabilistic rules to improve multi-label classification.
In multi-label text classification, each textual document can be assigned with one or more labels. Due to this nature, the multi-label text classification task is often considered to be more challenging compared to the binary or multi-class text classification problems. As an important task with broad applications in b…
Paper introduces SUEL model for integrating predictors without labeled data.
QActor optimizes learning from noisy labeled data streams by querying experts for clean labels.
There is growing interest in multi-label image classification due to its critical role in web-based image analytics-based applications, such as large-scale image retrieval and browsing. Matrix completion has recently been introduced as a method for transductive (semi-supervised) multi-label classification, and has seve…
A new method MixGDA combines mixup and gradient-based data augmentation for SSL.
Unified approach combines prediction-powered inference and variance reduction for semi-supervised optimization.
Inspector Gadget uses crowdsourcing and data programming to label industrial images efficiently.
Study improves fair opinion aggregation by balancing voter attributes.
Doubly robust self-training improves semi-supervised learning by balancing labeled and pseudo-labeled data.
Enhances deep networks robustness with data mollification and label smoothing.
GOLFS selects features for clustering by combining global and local information.
We study the robustness to symmetric label noise of GNNs training procedures. By combining the nonlinear neural message-passing models (e.g. Graph Isomorphism Networks, GraphSAGE, etc.) with loss correction methods, we present a noise-tolerant approach for the graph classification task. Our experiments show that test a…
Dynamic classifier chains with XGBoost reduces multi-label classification costs and improves label dependency handling.
New method selects data for labeling in RKHS to improve regression accuracy.
A new sampling method balances multi-label datasets by preserving category frequency order.
This paper considers a semi-supervised learning framework for weakly labeled polyphonic sound event detection problems for the DCASE 2019 challenge's task4 by combining both the tri-training and adversarial learning. The goal of the task4 is to detect onsets and offsets of multiple sound events in a single audio clip. …
Collecting labeled data is costly and thus a critical bottleneck in real-world classification tasks. To mitigate this problem, we propose a novel setting, namely learning from complementary labels for multi-class classification. A complementary label specifies a class that a pattern does not belong to. Collecting compl…