The paper proposes a tree model for interval-valued regression.
problem Learning a real-valued function from interval-valued data.
method Minimizing a margin-based discriminative objective function using a tree structure and dynamic programming.
result The proposed algorithm achieves state-of-the-art speed and accuracy.
Develops active learning method for linear optimization with margin-based criterion.
problem Optimizing decisions in linear optimization problems with limited labeled data.
method Smart Predict-then-Optimize (SPO) loss and margin-based active learning algorithm.
result Algorithm achieves significantly fewer labels than naive supervised learning, especially for minimizing SPO loss.
Consider a classification problem where we have both labeled and unlabeled data available. We show that for linear classifiers defined by convex margin-based surrogate losses that are decreasing, it is impossible to construct any semi-supervised approach that is able to guarantee an improvement over the supervised clas…
New margin-based learning guarantees improve generalization bounds.
problem Improving generalization bounds for machine learning models.
method Relative deviation margin bounds using empirical margin loss and Rademacher complexity.
result Distribution-dependent generalization bounds for unbounded loss functions.
New method prevents class collapse in metric learning with margin-based losses.
problem Class collapse in metric learning due to diverse intra-class samples.
method Proposed a sampling method to select nearest same-class samples as positive elements in tuple.
result Demonstrated clear benefits on various fine-grained image retrieval datasets.
IMMIGRATE selects features with interaction terms using margin-based weights.
problem Unclear differentiation of feature interactions from marginal effects.
method Includes and trains weights for interaction terms, applies large margin principle, considers robustness and local/global information.
result Achieves state-of-the-art results on several tasks.
Improves GATs by adding margin-based constraints to prevent over-fitting and over-smoothing.
problem Over-fitting and over-smoothing in GATs.
method Margin-based constraints on attention weights and graph structure.
result Significant improvements over previous GATs on various datasets.
Paper proposes angular loss for better face recognition and object classification.
problem Improving intra-class compactness and preventing overfitting in face recognition and object classification.
method Angular loss function to maximize angular gradient, reducing overfitting and requiring only one adjustable constant.
result Our method outperforms other methods in accuracy, discriminative information, and time-efficiency.
New margin-based regularization and selective sampling improve deep neural network performance.
problem Improving deep neural network performance on various classification tasks.
method Multi-margin regularization (MMR) and minimal margin score (MMS) for selective sampling.
result Improved results on multiple classification tasks across domains.
New bound on neural network generalization error using geometric complexity.
problem Understanding the generalization capabilities of deep neural networks.
method Derive a new upper bound on generalization error using margin-normalized geometric complexity.
result Empirical validation of the bound for ResNet-18 on CIFAR-10 and CIFAR-100 datasets.
Proposes a new method for learning flexible nonparametric kernels.
problem Improving model flexibility in margin-based kernel methods.
method Data-adaptive non-parametric kernel learning framework with two constraints.
result Enhanced model flexibility and improved performance on benchmark data sets.
New algorithms and bounds for contextual bandits using surrogate losses.
problem Efficiently solving contextual bandit problems with margin-based regret bounds.
method Use of surrogate losses (ramp and hinge) to derive new regret bounds and algorithms.
result Derives new margin-based regret bounds and efficient algorithms for contextual bandits.
Analyzes large-margin classifiers under high-dimensional data.
problem Selecting the best classifier among various margin-based methods.
method Investigates asymptotic performance of large-margin classifiers under two component mixture models.
result Analytical results closely match with Monte Carlo simulations.
We present a simple noise-robust margin-based active learning algorithm to find homogeneous (passing the origin) linear separators and analyze its error convergence when labels are corrupted by noise. We show that when the imposed noise satisfies the Tsybakov low noise condition (Mammen, Tsybakov, and others 1999; Tsyb…
The paper improves SVM margin-based generalization bounds.
problem Improving generalization bounds for SVMs.
method Revisiting and improving classic generalization bounds in terms of margins, complementing with a nearly matching lower bound.
result Almost settles the generalization performance of SVMs in terms of margins.
Paper explores connections between loss functions and consistency in binary classification and regression.
problem Consistency in binary classification and regression applications.
method Characterization of conformable loss functions and derivation of a new Huber-type loss function.
result Margin-based loss functions are equivalent to loss functions of squared standardized logistic regression residuals.
Study generalization of voting classifiers using margin-based bounds.
problem Understanding the generalization of ensemble classifiers like voting.
method Proved margin-based generalization bounds using PAC-Bayes theory and Dirichlet posteriors.
result Provided state-of-the-art guarantees on classification tasks.
Paper studies statistical properties of DP data synthesis algorithms based on Bayesian networks.
problem Ensuring differential privacy in synthetic data generation for high-dimensional data.
method Introduces random noise to low-dimensional marginals of a probabilistic graphical model (BN) to achieve differential privacy.
result Establishes a rigorous accuracy guarantee for BN-based DP synthetic data generators using total variation (TV) distance.
New margin bound improves generalization for voting classifiers.
problem Improving generalization bounds for voting classifiers.
method Established a new margin-based generalization bound.
result Derives an optimal weak-to-strong learner with matching theoretical lower bound.
Optimized deferral improves accuracy in imbalanced settings.
problem Imbalance in expert predictions leads to suboptimal performance in two-stage learning to defer.
method Developed novel cost-sensitive learning algorithms and margin-based loss functions tailored for expert imbalance.
result MILD algorithm shows clear improvements over baselines in image classification and LLM routing tasks.
Paper proposes a new method to predict partial rankings from crowdsourced data.
problem Ambiguity in pairwise comparisons leads to incomplete rankings, requiring a better method.
method Margin-based Maximum Likelihood Estimate (MLE) framework for probabilistic partial order learning.
result The proposed MLE method improves accuracy over traditional algorithms.
A new loss function α-loss bridges log-loss and 0-1 loss for binary classification.
problem Improving binary classification performance using a tunable loss function.
method Introducing α-loss, proving its margin-based form and classification-calibration, and providing an upper bound on empirical risk. result Empirical and expected risk difference upper bound for logistic regression-based classification.
We present a method to stop the evaluation of a decision making process when the result of the full evaluation is obvious. This trait is highly desirable for online margin-based machine learning algorithms where a classifier traditionally evaluates all the features for every example. We observe that some examples are e…
PrAda-GAN improves synthetic data generation under differential privacy.
problem Generating synthetic data under differential privacy with marginal-based methods.
method Sequential generator architecture integrating GAN and marginal-based approaches, with adaptive regularization of Bayes network structure.
result PrAda-GAN outperforms existing methods in privacy-utility trade-off on synthetic and real-world datasets.
New algorithm improves online multiclass classification with partial feedback.
problem Online multiclass classification with partial feedback.
method Inspired by complementary labels, a margin-based deterministic approach.
result Our method outperforms existing non-margin-based and stochastic methods.
New lower bounds nearly match existing upper bounds for boosted classifiers.
problem Understanding the generalization performance of boosted classifiers.
method Margin-based lower bounds on boosted classifiers.
result Lower bounds nearly match the kth margin bound, settling the generalization performance of boosted classifiers. New tighter generalization bounds for deep networks like CNNs and ResNets.
problem Establishing tighter bounds for deep neural networks' generalization error.
method Introducing a new characterization of Lipschitz properties and margin-based data-dependent error bounds.
result Significantly tighter generalization bounds for deep neural networks, including CNNs and ResNets.
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.
A new method accelerates deep neural network training using minimal margin score.
problem Training deep neural networks is computationally expensive.
method Introduces minimal margin score (MMS) for selecting samples.
result Significant acceleration in training deep neural networks.
New method improves consistency in preference learning for neural networks.
problem Inconsistent surrogate losses in preference learning for neural networks.
method Formulated a margin-shifted ranking framework and introduced Structure-Aware H-consistency. result Proved superior consistency guarantees for capacity-bounded models using heavy-tailed surrogates.
New methods improve deep learning on imbalanced datasets.
problem Poor performance of deep learning on imbalanced datasets.
method Label-distribution-aware margin (LDAM) loss and a training schedule.
result Combination of methods achieves significant performance gains.
A method detects out-of-distribution inputs using an ensemble of self-supervised classifiers.
problem Detecting out-of-distribution inputs in deep learning applications.
method Train classifiers by leaving out random subsets of training data as OOD data, using margin-based loss.
result Method outperforms state-of-the-art on OOD detection benchmarks.
ELM improves neural model embeddings for long-tail learning.
problem Learning skewed label distributions in neural models.
method Enforces margins in logit space and regularizes embedding distribution.
result ELM reduces generalization gap and tightens tail class embeddings.
Linear-Core Surrogates combine fast optimization and statistical efficiency in classification and structured prediction.
problem The trade-off between smoothness and margin-based losses in classification and structured prediction.
method Linear-Core (LC) Surrogates, a family of convex loss functions that stitch a linear core to a smooth tail.
result LC Surrogates achieve fast linear consistency rates while maintaining differentiability and strict H-consistency bounds. 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.
New bounds show neural networks can resist attacks better with sparse weights.
problem Neural networks are vulnerable to small adversarial perturbations.
method Compression based on effective sparsity and joint sparsity.
result Neural networks with approximately sparse weight matrices have better robustness and generalization.
Optimizes classifiers for varying levels of automation.
problem Supervised learning models often perform worse than human experts on specific instances.
method Focuses on convex margin-based classifiers, showing the problem is NP-hard. For SVMs, the objective function is decomposed into monotone and modular components, allowing efficient algorithms to solve the problem.
result The approach demonstrates that classifiers optimized for varying levels of automation can outperform full automation and human-only models.
Classification is an important statistical learning tool. In real application, besides high prediction accuracy, it is often desirable to estimate class conditional probabilities for new observations. For traditional problems where the number of observations is large, there exist many well developed approaches. Recentl…
New method for structured prediction with latent variables and Gaussian perturbations.
problem Non-convex learning with latent variables in structured prediction.
method Maximum loss over random structured outputs sampled from Gaussian perturbations.
result Tighter upper bound on Gibbs decoder distortion for faster evaluation.
Residuals improve deep neural networks without increasing hypothesis complexity.
problem Understanding how residual connections affect hypothesis complexity and generalization.
method Analyzing the covering number of the hypothesis space and deriving a margin-based generalization bound.
result Residual connections do not increase the hypothesis complexity of neural networks.
New algorithms recover clusters with minimal queries, connecting margins to recoverability.
problem Active cluster recovery with oracle queries for minimal cost.
method Introducing margin-based clustering, designing algorithms for various spaces.
result Achieve O(logn) queries for general pseudometric spaces and convex clusters. Paper establishes comparison theorems for large-margin learning.
problem Data piling issue in high-dimension and low-sample size SVM.
method Large-margin unified machines (LUM) loss functions.
result New comparison theorems for all LUM loss functions.
This paper presents a margin-based multiclass generalization bound for neural networks that scales with their margin-normalized "spectral complexity": their Lipschitz constant, meaning the product of the spectral norms of the weight matrices, times a certain correction factor. This bound is empirically investigated for…
Enhances ordinal embedding with less data by focusing on margin distribution.
problem Insufficient labeled data for ordinal embedding.
method Proposes Distributional Margin based Ordinal Embedding (DMOE) to improve generalization with less data.
result Demonstrates improved generalization performance with less labeled data.
We derive and analyze a new, efficient, pool-based active learning algorithm for halfspaces, called ALuMA. Most previous algorithms show exponential improvement in the label complexity assuming that the distribution over the instance space is close to uniform. This assumption rarely holds in practical applications. Ins…
We address the problem of aggregating an ensemble of predictors with known loss bounds in a semi-supervised binary classification setting, to minimize prediction loss incurred on the unlabeled data. We find the minimax optimal predictions for a very general class of loss functions including all convex and many non-conv…
Paper proposes ManiF-SMC for effective approximate machine unlearning.
problem Limited unlearning effectiveness and potential to undermine original learning objectives.
method Reformulates approximate unlearning as pushing erased samples towards semantic neighbors in retained data, using a margin-based triplet loss.
result Achieves unlearning effectiveness comparable to state-of-the-art methods while operating purely in representation space.
We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels. The main algorithms for this problem are {\em{disagreement-based active learning}}, …