A method solves weakly supervised multiclass MIL problems.
problem Weakly supervised multiclass MIL problems in image recognition.
method Maximizing exact likelihood fitting given observations.
result Our method can learn all convolutional neural networks.
The paper extends multiple instance learning to multiclass and regression problems.
problem Learning from aggregate observations where supervision is given to sets of instances.
method Probabilistic framework for various aggregate observations, including classification and regression.
result The proposed estimator has nice convergence properties under mild assumptions.
Enhances MIL performance in scarce data scenarios using topological inductive biases.
problem Low performance of MIL in data-scarce scenarios.
method Incorporates topological inductive biases into MIL framework.
result Average performance improvements of 15.3% for synthetic datasets, 2.8% for benchmarks, and 5.5% for rare anemia classification.
Dropout improves MIL performance on noisy WSI classification.
problem Noisy feature embeddings and weak supervision in MIL for WSI classification.
method Empirical exploration of dropout in MIL, proposing MIL-Dropout.
result MIL-Dropout boosts MIL performance with minimal computational cost.
Convolutional neural networks (CNN) have achieved state of the art performance on both classification and segmentation tasks. Applying CNNs to microscopy images is challenging due to the lack of datasets labeled at the single cell level. We extend the application of CNNs to microscopy image classification and segmentat…
We present a comprehensive study of the use of generative modeling approaches for Multiple-Instance Learning (MIL) problems. In MIL a learner receives training instances grouped together into bags with labels for the bags only (which might not be correct for the comprised instances). Our work was motivated by the task …
New GP-based MIL method using Hyperbolic Secant distribution.
problem Probabilistic MIL for medical image analysis.
method Formulate VGPMIL using Pólya-Gamma random variables.
result Competitive or superior predictive performance and efficiency.
Deep-MIL models fail to respect key MIL assumption, leading to incorrect learning.
problem Deep-MIL models learn anti-correlated instances, violating the standard MIL assumption.
method Proposed algorithmic unit tests to identify violations of MIL assumptions.
result Five prominent deep-MIL models fail algorithmic unit tests, revealing incorrect learning.
Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as learning the Bernoulli distribution of the bag label where the bag label probability is fully parameterized by neural networks. Furthermore, we …
Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In this setting, supervised learning cannot be applied directly. Often, specialized MIL methods learn by making additional assumptions about the relationship of the ba…
In the supervised learning setting termed Multiple-Instance Learning (MIL), the examples are bags of instances, and the bag label is a function of the labels of its instances. Typically, this function is the Boolean OR. The learner observes a sample of bags and the bag labels, but not the instance labels that determine…
Graph neural networks improve MIL performance without losing interpretability.
problem Learning bag-level labels from bags of instances.
method Proposed a GNN-based algorithm treating bags as graphs to learn embeddings and predict labels.
result Achieves state-of-the-art performance on MIL datasets.
Paper proposes a method to generate instance labels from weakly supervised data.
problem Weakly supervised instance labeling in medical image analysis.
method Uses multiple instance learning (MIL) and knowledge distillation to generate instance-level predictions.
result Significantly outperforms state-of-the-art MIL methods in instance-level prediction.
Improves key instance detection in MIL models by using neural network inversion with sparseness constraint.
problem Limited key instance detection performance in attention-based deep MIL models due to skewed attention scores.
method Sparse network inversion with a sparseness constraint incorporated into neural network inversion, solved by proximal gradient method.
result Significantly improved key instance detection performance while maintaining bag-level prediction performance.
Data discretization is an important step in the process of machine learning, since it is easier for classifiers to deal with discrete attributes rather than continuous attributes. Over the years, several methods of performing discretization such as Boolean Reasoning, Equal Frequency Binning, Entropy have been proposed,…
Paper tackles distribution changes in MIL without test data.
problem Real-world MIL data violates i.i.d. assumption.
method Proposes a framework connecting MIL to causal effect estimation.
result Validated approach on various datasets.
We introduce a graphical framework for multiple instance learning (MIL) based on Markov networks. This framework can be used to model the traditional MIL definition as well as more general MIL definitions. Different levels of ambiguity -- the portion of positive instances in a bag -- can be explored in weakly supervise…
Unified framework for various learning problems via Multiple-Instance Learning.
problem Various learning problems including multi-class, complementarily labeled, multi-label, and multi-task learning.
method Reduction of various learning problems to Multiple-Instance Learning (MIL) with theoretical guarantees.
result Unified and simplified framework for designing and analyzing algorithms for various learning problems.
We address the problem of \emph{instance label stability} in multiple instance learning (MIL) classifiers. These classifiers are trained only on globally annotated images (bags), but often can provide fine-grained annotations for image pixels or patches (instances). This is interesting for computer aided diagnosis (CAD…
We describe a novel weakly supervised deep learning framework that combines both the discriminative and generative models to learn meaningful representation in the multiple instance learning (MIL) setting. MIL is a weakly supervised learning problem where labels are associated with groups of instances (referred as bags…
A model finds interpretable prototypes for MIL datasets.
problem Finding interpretable prototypes for multiple instance learning.
method Permutation invariant maximally predictive prototype generator.
result The model outperforms existing approaches in accuracy and efficiency.
Proposes a new MIL formulation using infinitely many shapelets.
problem Weakness of single shapelet classifiers and lack of theoretical guarantee for multiple shapelets.
method Formulates a new MIL approach with infinitely many shapelets and provides an efficient algorithm.
result Empirical study shows effectiveness in MIL and Shapelet Learning.
Many objects in the real world are difficult to describe by a single numerical vector of a fixed length, whereas describing them by a set of vectors is more natural. Therefore, Multiple instance learning (MIL) techniques have been constantly gaining on importance throughout last years. MIL formalism represents each obj…
A simple neural network approach for Multiple Instance Learning.
problem Weak supervision in machine learning where labels are available for groups of examples called bags.
method Bag-level ranking loss function for any neural architecture.
result Our method is effective and comparable to existing methods in practical scenarios.
New method improves weakly-supervised action localization.
problem Locating action segments in videos with limited labels.
method Explicitly models key instance assignment as hidden variable using EM framework.
result Achieves state-of-the-art performance on THUMOS14 and ActivityNet1.2 benchmarks.
Paper tackles rDR classification and lesion segmentation using self-supervised equivariant learning and attention-based MIL.
problem Classifying rDR and segmenting lesions from image-level labels.
method Integrates self-supervised equivariant attention mechanism (SEAM) with attention-based multi-instance learning (MIL).
result Achieved AU ROC of 0.958 on Eyepacs dataset, outperforming state-of-the-art.
Paper addresses instance-level label prediction in MIL, improving performance compared to existing methods.
problem Lack of instance-level label prediction in existing MIL approaches restricts their applicability.
method Proposes a novel algorithm with instance-level loss function, unbiasedly and consistently estimated.
result Empirically validated superior instance-level and bag-level performance compared to state-of-the-art methods.
Solves dual imbalance in detecting sparse anomalies in MIL.
problem Detecting scarce and sparse anomalous samples in MIL.
method Reformulates MIL as a fine-grained PU learning problem, addressing imbalance at both macro and micro levels.
result Demonstrates effectiveness of BFGPU framework on synthetic and real-world datasets.
New formulation of MIL using shapelets for better classifier of bags.
problem Finding a good classifier of bags based on shapelets.
method Formulation using all possible shapelets, reduced to DC programs, and heuristic options.
result Richer class of classifiers with theoretical justification and empirical validation.
Boosting weak learners to strong ones from aggregate labels is possible for LLP but not for MIL.
problem Boosting weak learners to strong ones from aggregate labels in learning from label proportions (LLP).
method Using a weak learner on large enough bags to obtain a strong learner for small bags in polynomial time.
result Boosting is possible for LLP but not for MIL.
Novel method for multiclass ROC curves using multidimensional Gini index.
problem Multiclass performance evaluation, especially for imbalanced datasets.
method Extends ROC curve methodology to multiclass settings using multidimensional Gini index.
result Validated through case studies in health care and finance.
New research determines the optimal sample complexity for multiclass and list learning.
problem Determining the optimal sample complexity for multiclass classification.
method Algebraic characterization of multiclass hypothesis classes in terms of their DS dimension.
result Proves a longstanding conjecture and determines the optimal dependence of sample complexity on DS dimension.
Unified binary and multiclass margin-based classification methods.
problem No consensus on multiclass loss functions analogous to binary margin loss.
method Showed multiclass loss functions can be expressed in relative margin form.
result Extended classification-calibration result to multiclass.
Paper proposes a new model for fine-grained review classification using weak supervision.
problem Fine-grained analysis of reviews is challenging due to segment-level labels.
method Employed Multiple Instance Learning (MIL) with sigmoid attention mechanism for weak supervision.
result Proposed model outperforms state-of-the-art models in segment-level sentiment classification.
FOLKLORE algorithm speeds up online multiclass logistic regression.
problem Efficiently solving online multiclass logistic regression without high computational cost.
method Developed FOLKLORE algorithm with improved runtime and regret bound.
result First practical algorithm for online multiclass logistic regression.
API technique improves multiclass GAM interpretability.
problem Interpretability breakdown in multiclass GAMs.
method Developed two axioms and API technique for multiclass GAMs.
result API technique transforms multiclass GAMs to be visually interpretable.
Proposes a robust framework for multiclass classification.
problem General multiclass classification with adversarial robustness.
method Dual formulation as convex optimization with adversarial surrogate loss.
result Competitive performance in multiclass classification problems.
Enhanced word embeddings boost multiclass text classification accuracy.
problem Improving multiclass text classification accuracy using pre-trained embeddings.
method Proposed word-class embeddings (WCEs) to enhance pre-trained word embeddings.
result WCEs significantly improve multiclass text classification accuracy.
Study shows exponential error reduction in multiclass classification without bias-variance trade-off.
problem Multiclass classification with margin conditions.
method Analysis of classification error under hard-margin conditions.
result Exponential decrease in classification error without bias-variance trade-off.
New method debiases word embeddings for multiclass settings like race and religion.
problem Word embeddings in online texts perpetuate human stereotypes, including race and religion.
method Proposes a novel methodology to debias word embeddings in multiclass settings.
result Demonstrates robust multiclass debiasing that maintains NLP task efficacy.
The paper analyzes how overparameterized models can generalize well in multiclass classification.
problem Generalization in multiclass classification with overparameterized models.
method Survival/contamination analysis framework adapted for multiclass classification.
result Multiclass classification can generalize well even with many classes, unlike regression tasks.
Automated graphics testing detects novel corruptions without manual labeling.
problem Detecting novel visual corruptions in graphics unit testing without manual labeling.
method Reproduces driver bugs to generate corruptions and uses Multiple Instance Learning (MIL) methods.
result Significantly outperforms unsupervised methods and discovers novel corruptions.
New methods detect unfairness in multiclass classifiers using DCP.
problem Detecting unfairness in multiclass classifiers.
method Generalizes DCP to multiclass, provides optimization methods.
result Detects classifiers treating a significant fraction of the population unfairly.
New method for multiclass classification reduces error bounds.
problem High-dimensional multiclass classification with sparse coefficients.
method Sparse multinomial logistic regression with convex penalties.
result Plug-in classifiers achieve minimax generalization error bounds.
Study online multiclass classification under bandit feedback, extending previous results.
problem Online multiclass classification with bandit feedback, focusing on label space unboundedness.
method Extend Daniely and Helbertal's results, show necessity and sufficiency of Bandit Littlestone dimension for learnability.
result Sequential uniform convergence is necessary but not sufficient for bandit online learnability.
This work extends implicit bias analysis to multiclass classification using a new loss framework.
problem The implicit bias of gradient descent on multiclass data without explicit regularization.
method Employing the PERM framework to introduce a multiclass extension of the exponential tail property.
result Extended implicit bias result to multiclass classification using a new loss framework.
Paper studies multiclass classifiers from binary classifiers, proving methods and demonstrating advantages.
problem Constructing efficient multiclass classifiers from binary ones.
method Two methods: one vs. all and hierarchical classification, with a new leverage-hierarchical method introduced.
result Proves upper bounds and exact formulas for multiclass regret in terms of binary regrets.
New method boosts multiclass classification speed and accuracy.
problem Multiclass classification problem
method Factorized MultiClass Boosting (decomposing into regression tasks, solving with CART trees)
result Significantly faster and same quality as state-of-the-art solutions