Proposes a new method for MIR using kernel mean embeddings.
problem Multiple instance regression (MIR) where bags contain multiple instances with a single label.
method Computes kernel mean embeddings of predicted label distributions and learns a regressor from these embeddings.
result Better results than baseline instance-MIR across all datasets, state-of-the-art on two.
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.
A new method for MIR in remote sensing without assuming a prime instance per bag.
problem Multiple Instance Regression in remote sensing with high variability.
method Treats each bag as a set of instances and learns to map each bag to its unique label using all instances.
result Outperforms previous state-of-the-art on three real-world datasets.
In this work, we consider a manufactory process which can be described by a multiple-instance logistic regression model. In order to compute the maximum likelihood estimation of the unknown coefficient, an expectation-maximization algorithm is proposed, and the proposed modeling approach can be extended to identify the…
We solve ElasticNet regularization tuning across multiple instances with provable guarantees.
problem Tuning ElasticNet regularization coefficients across multiple problem instances.
method Characterized ElasticNet loss as a piecewise-rational function, derived structural complexity bounds, and showed generalization and online learning guarantees.
result First general learning-theoretic guarantees for ElasticNet tuning without strong data distribution assumptions.
Multiple Additive Regression Trees (MART), an ensemble model of boosted regression trees, is known to deliver high prediction accuracy for diverse tasks, and it is widely used in practice. However, it suffers an issue which we call over-specialization, wherein trees added at later iterations tend to impact the predicti…
Recently neural networks and multiple instance learning are both attractive topics in Artificial Intelligence related research fields. Deep neural networks have achieved great success in supervised learning problems, and multiple instance learning as a typical weakly-supervised learning method is effective for many app…
With the rapid development of social media sharing, people often need to manage the growing volume of multimedia data such as large scale video classification and annotation, especially to organize those videos containing human activities. Recently, manifold regularized semi-supervised learning (SSL), which explores th…
Efficiently predict LLM benchmarks using feature selection and regression.
problem Predicting full benchmark scores with minimal question subsets.
method Multiple regression with feature selection, using kernel ridge regression and mRMR.
result Improved prediction accuracy and ranking correlation across various benchmarks.
We solve principal component regression (PCR), up to a multiplicative accuracy 1+γ, by reducing the problem to O~(γ−1) black-box calls of ridge regression. Therefore, our algorithm does not require any explicit construction of the top principal components, and is suitable for large-scale PCR instances. In…
A new method for decision-focused learning reduces computational cost.
problem Efficiently solving combinatorial problems with uncertain parameters.
method Reframed as cost-sensitive multi-output regression, with novel loss components.
result Comparable downstream task quality with reduced computational cost.
Active learning method reduces labeling cost for regression models with aggregated data.
problem Reducing labeling cost for training regression models with aggregated data.
method Sequentially selects sets to be labeled using mutual information quantifying model parameter uncertainty.
result Achieves better predictive performance with fewer labeled sets.
A novel framework for regression with multiple experts, addressing challenges in infinite and continuous label spaces.
problem Challenges in regression with multiple experts due to the infinite and continuous nature of the label space.
method Introduces a novel framework for regression with deferral, analyzing both single-stage and two-stage scenarios with new surrogate loss functions.
result Proves H-consistency bounds for both single-stage and two-stage methods, providing stronger guarantees than Bayes consistency. Bayesian approach for multivariate density regression of complex data.
problem Regression of multivariate density-valued responses on predictors.
method Bayesian inference using sliced Wasserstein barycenter and SW distance.
result Accurate fits and reliable predictions for complex data.
Develops multi-task learning models for ordinal regression with heterogeneous data.
problem Tackling ordinal regression for heterogeneous, non-IID data.
method Sparse and deep multi-task learning approaches.
result Proposed MTOR models improve prediction performance.
Paper introduces impact curves for evaluating binarized regression models with varying costs.
problem Evaluating binarized regression models with varying costs and instance-specific utility.
method Proposes impact curves to optimize binary decisions across different utilities.
result Impact curves identify conditions where one model is favored over another and quantify model improvement.
A multiple instance dictionary learning method using functions of multiple instances (DL-FUMI) is proposed to address target detection and two-class classification problems with inaccurate training labels. Given inaccurate training labels, DL-FUMI learns a set of target dictionary atoms that describe the most distincti…
EvoNUDGE uses graph neural networks to improve genetic programming performance.
problem Efficiency in evolutionary computation for problem solving.
method Graph neural network to elicit additional knowledge from symbolic regression problems.
result EvoNUDGE significantly outperforms conventional and neural genetic programming methods.
Paper uses financial news for stock trend forecasting using deep multiple instance learning.
problem Forecasting stock trends from financial news articles.
method Developed a flexible and adaptive multi-instance learning model for bags of instances (financial news articles) on trading days.
result Outstanding trend prediction accuracy compared to state-of-the-art approaches.
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.
SGD implicitly regularizes linear regression problems better than ridge regression for many cases.
problem Understanding implicit regularization in linear regression problems.
method Comparing SGD and ridge regression on a broad class of least squares problems.
result SGD generalizes no worse than ridge regression for many problem instances, sometimes better.
New hierarchical model improves on standard practice for high-dimensional data.
problem Poor statistical performance in high-dimensional hierarchical models.
method Model effects as exchangeable across covariates and correlated across datasets.
result Empirical Bayes estimator outperforms classic approach in high-dimensional settings.
A multiple instance dictionary learning approach, Dictionary Learning using Functions of Multiple Instances (DL-FUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI estimates a "heartbea…
ICE improves classification performance by leveraging internal patterns among instances.
problem Inconsistent results for different MCS algorithms on specific problems.
method ICE groups training data into overlapping clusters, builds classifiers for each cluster, and predicts class labels by averaging predictions from top-performing models.
result ICE provides a stable improvement on a significant proportion of datasets over existing MCS methods.
A new method for deep multiple instance learning using self-attention.
problem Classifying bags of instances with dependencies.
method Introducing Self-Attention-based aggregation operation for bags of instances.
result SA-AbMILP outperforms other models in various datasets.
In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors. In this paper we address the problem of how these bags can best be represented. Two standard approaches are to use (dis)similarities between bags and prototype bags, or between bags and prototyp…
Unsupervised image-to-image translation has gained considerable attention due to the recent impressive progress based on generative adversarial networks (GANs). However, previous methods often fail in challenging cases, in particular, when an image has multiple target instances and a translation task involves significa…
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.
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.
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.
Motivation: A major challenge in the development of machine learning based methods in computational biology is that data may not be accurately labeled due to the time and resources required for experimentally annotating properties of proteins and DNA sequences. Standard supervised learning algorithms assume accurate in…
We study a multiclass multiple instance learning (MIL) problem where the labels only suggest whether any instance of a class exists or does not exist in a training sample or example. No further information, e.g., the number of instances of each class, relative locations or orders of all instances in a training sample, …
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 methods detect targets from imprecisely labeled hyperspectral data.
problem Challenges in acquiring labeled hyperspectral data.
method Multi-Target MI-ACE and MI-SMF methods that learn target signatures from imprecisely labeled samples.
result Effective at learning target signatures and performing target detection.
Survey on Evidential Deep Learning for uncertainty estimation in deep neural networks.
problem Uncertainty estimation in deep neural networks with overhead or limited diversity.
method Evidential Deep Learning, parameterizing distributions over distributions.
result Single model and forward pass uncertainty estimation with a unified notation.
New method improves learning from multiple correlated data trajectories.
problem Learning from multiple correlated data trajectories without mixing assumptions.
method Hellinger localization framework for maximum likelihood estimation.
result Instance-optimal bounds that scale with full data budget under broad conditions.
Paper presents novel online MTL methods using WRLS and OSLSSVR.
problem Online Multi-Task Learning (MTL) Regression Problems
method Develops recursive versions of WRLS and OSLSSVR for MTL.
result Achieves exact and approximate recursions with quadratic cost.
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.
We propose a new formulation of Multiple-Instance Learning (MIL). In typical MIL settings, a unit of data is given as a set of instances called a bag and the goal is to find a good classifier of bags based on similarity from a single or finitely many "shapelets" (or patterns), where the similarity of the bag from a sha…
Last SGD iterate bounds for overparameterized linear regression.
problem Analyzing the last iterate risk bounds of SGD with decaying stepsize for overparameterized linear regression.
method Problem-dependent analysis of last iterate risk bounds of SGD with geometrically decaying stepsize.
result Proved nearly matching upper and lower bounds on the excess risk for last iterate SGD with geometrically decaying stepsize.
New algorithms for regression with adversarial responses on various metric spaces.
problem Regression with adversarial responses under non-i.i.d. sequences.
method Proves universal consistency for a wide range of non-stationary processes.
result Achieves universal consistency for a broader class of sequences than stationary processes.
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.
New algorithm identifies best arm in semiparametric bandits with near optimal efficiency.
problem Fixed-confidence Best Arm Identification in semiparametric bandits with unknown baseline shift.
method Phase-elimination algorithm based on orthogonalized regression design.
result Nearly optimal high-probability sample-complexity upper bound established.
Improved stochastic optimization outperforms standard methods.
problem Optimizing smooth, strongly convex functions with noisy data.
method Variance reduction strategy called VISOR.
result VISOR achieves optimal sample complexity and oracle complexity.
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 consider a refinement of differential privacy --- per instance differential privacy (pDP), which captures the privacy of a specific individual with respect to a fixed data set. We show that this is a strict generalization of the standard DP and inherits all its desirable properties, e.g., composition, invariance to …
Paper proposes M3Lcmf for better M3L performance.
problem Complex objects with diverse instances and multiple labels.
method Collaborative matrix factorization with heterogeneous network.
result M3Lcmf outperforms other solutions in benchmark datasets.
Study improves generalization bounds for linear regression across tasks.
problem Improving generalization in high-dimensional regression problems.
method Distribution-dependent bounds on generalization error for L1, L2, and elastic net regularization.
result Generalization bounds improve with data distribution niceness and do not degrade with feature dimension.