PAC-Bayesian framework for fairness in stochastic and deterministic classifiers.
problem Theoretical guarantees on fairness for balancing predictive risk and fairness constraints.
method PAC-Bayesian framework for both stochastic and deterministic classifiers, covering a broad class of fairness measures.
result Derives generalization bounds for fairness, demonstrating tightness with empirical evaluation.
The paper studies a stochastic majority vote approach to improve classifier accuracy.
problem Improving classifier accuracy over ensembles of classifiers.
method Minimizing a PAC-Bayes generalization bound with Dirichlet distributions.
result Achieves state-of-the-art accuracy and tight generalization bounds.
Paper proposes a new method to compare classifiers across multiple datasets.
problem Comparing classifiers over multiple datasets with multiple criteria.
method Adopting decision theory, the paper introduces generalized stochastic dominance for ranking classifiers.
result Generalized stochastic dominance can be used to rank classifiers and statistically tested.
For random graphs distributed according to stochastic blockmodels, a special case of latent position graphs, adjacency spectral embedding followed by appropriate vertex classification is asymptotically Bayes optimal; but this approach requires knowledge of and critically depends on the model dimension. In this paper, w…
Stochastic defense improves natural classifiers against adversarial attacks.
problem Vulnerability of deep networks to adversarial attacks.
method Long-run MCMC sampling with Energy-Based Model for adversarial purification.
result Balancing memoryless and metastable behavior leads to effective purification and robust classification.
Enhances mixture models with classifier-defined weights.
problem Density evaluation and sampling in mixture models.
method Introduces Classifier Weighted Mixtures (CWM) with functional weights.
result Improves expressivity in variational estimation without increasing complexity.
Paper presents an efficient algorithm for learning minimax risk classifiers with large-scale data.
problem Efficient learning of minimax risk classifiers for large-scale data with multiple classes.
method Combination of constraint and column generation for efficient learning.
result 10x speedup for general large-scale data and 100x speedup with many classes.
Study on deep neural networks using concentration inequalities and optimal stopping.
problem Understanding the performance and structure of stochastic deep neural networks.
method Introduced concentration inequalities for SDNN outputs and an EC classifier. Determined the optimal number of layers via an optimal stopping procedure.
result Optimal number of layers for SDNNs determined via an optimal stopping procedure.
Proposes a method to estimate time-dependent probability density functions using binary classifiers.
problem Estimating time-dependent probability density functions of stochastic processes.
method Trains a time-dependent binary classifier to discriminate between realizations of a stochastic process at two nearby time instants.
result Explicitly models and accurately reconstructs complex time-dependent, multi-modal, and near-degenerate densities.
Optimal posterior distributions improve SVM classifiers and parameter selection.
problem Improving SVM classifiers and selecting optimal regularization parameters.
method PAC-Bayesian approach with optimal posterior identification for stochastic classifiers.
result Optimal posteriors yield tight risk bounds and improved SVM performance.
Unified approach adjusts classifiers to meet system-level constraints.
problem Multi-class classification under system-level constraints.
method Post-processing approach using linearly constrained stochastic program and entropic regularization.
result Finite-sample guarantees for risk and constraint satisfaction.
A novel distributed adaptive NN classifier for large data sets.
problem Handling large and distributed data for efficient classification.
method Distributed adaptive nearest neighbor classifier with stochastic tuning parameter selection and early stopping rule.
result Achieves nearly optimal convergence rate under large sub-sample sizes.
Stochastic RNNs classify biological neural network paths with robust error bounds.
problem Classifying biological neural network paths.
method Modelled as a continuous-time stochastic recurrent neural network (RNN) with identity activation function, analysed in the robust regime.
result Generalisation error bound holds with high probability, showing the empirical risk minimiser is the best-in-class hypothesis.
New method compares classifiers using GSD-front, addressing statistical uncertainty and robustness.
problem Comparing classifiers with multiple quality metrics and statistical uncertainty.
method Proposes GSD-front and statistical tests for robust comparisons.
result Reliable method for comparing classifiers with statistical uncertainty and robustness.
A new weighting scheme corrects label ensembles in multi-label classification.
problem Improving the reliability of multi-label classification with imbalanced data.
method Proposed a novel weighting scheme based on fuzzy confusion matrix and information theory.
result The proposed method reduces the vulnerability to imbalanced class distribution and improves classification quality.
This work formalizes guidance in diffusion models and introduces a stochastic control framework.
problem Lack of a solid theoretical foundation for guidance scheduling in diffusion models.
method Introduces a stochastic optimal control framework to cast guidance scheduling as an adaptive optimization problem.
result Establishes a principled foundation for more effective guidance in diffusion models.
Optimizes SGD for text classification using hyper-parameter tuning.
problem Improving performance of SGD in text classification.
method Fine-tuning hyper-parameters using Grid-Search approach.
result Optimized SGD classification leads to better accuracy and execution time.
Localized uncertainty attacks target uncertain regions to create imperceptible adversarial examples.
problem Adversarial examples that are imperceptible to humans and strong under deterministic classifiers.
method Localized uncertainty attacks by perturbing uncertain regions, using predictive uncertainty or surrogate models.
result Localized uncertainty attacks produce strong adversarial examples that retain input similarity.
Efficient algorithms for large-scale multiclass classification with linear classifiers.
problem Training ℓ1-regularized linear classifiers with high dimensionality and many classes. method Combines quasi-bilinear objective, stochastic mirror descent, and non-uniform sampling.
result Proposes a sublinear algorithm for multiclass hinge loss.
Proposes a method to extract robust features that improve classifier robustness.
problem Improving classifier robustness to small perturbations in input space.
method Introduces an additional penalty term in the information bottleneck framework to minimize Fisher information, optimizing a variational bound using stochastic gradient descent.
result Optimally robust features are jointly Gaussian, and the method produces classifiers with increased robustness to perturbations.
Generative model improves EMG pattern recognition accuracy.
problem Stochastic characteristics of EMG signals not fully considered in existing classification methods.
method Scale mixture-based stochastic generative model with variational Bayesian learning.
result Proposed method outperforms conventional classifiers in EMG pattern recognition.
Traditional classifiers can generate high-quality images comparable to generative models.
problem Separation between classifiers and generators in neural networks.
method Optimizing input gradients to produce images, using mask-based stochastic reconstruction, progressive-resolution technique, and distance metric loss.
result Traditional classifiers can generate high-fidelity images of 256imes256 resolution on ImageNet. Draft proposes adapting neural networks to match naive Bayes classifiers.
problem Bridge between neural networks and naive Bayes classifiers.
method Class-conditional compression and disentanglement using variational bounds.
result Latent representations enable naive Bayes classifier performance.
New framework maximizes perturbed samples for inverse classification with budget constraints.
problem Maximizing perturbed samples for desired classification outcomes under budget constraints.
method Gradient methods, stochastic processes, Lagrangian relaxations, Gumbel trick.
result Stochastic process-based algorithms outperform in different budget settings.
Improved image classification using centroids and stochastic sampling.
problem Limited accuracy of nearest-neighbor classification.
method Coarse-graining (replacing images by centroids) and stochastic sampling of centroids.
result Stochastic sampling of centroids improves classification accuracy.
Gradient descent and SGD can converge to max-margin directions in ReLU models.
problem Understanding the implicit bias of gradient methods in ReLU models.
method Characterization of loss function landscape, analysis of GD and SGD convergence, exploration of multi-neuron network learning.
result Gradient descent and SGD can converge to max-margin directions in ReLU models.
This paper improves binary classification methods beyond accuracy, especially in imbalanced datasets.
problem Binary classification performance metrics often fail to reflect real-world consequences, especially in imbalanced datasets.
method Derives a generalized Bayes-optimal classifier from accuracy to any performance metric, removing assumptions and providing finite-sample statistical guarantees.
result Optimal classification performance depends on class imbalance properties, providing new insights and guarantees.
Convolutional linear classifiers generalize better than non-convolutional ones in structured data.
problem Understanding why gradient descent can lead to better generalization in deep learning models.
method Analyzing the generalization performance of two-layer convolutional linear classifiers with gradient descent, considering data distribution and convolutional filter size.
result Convolutional layers improve generalization in structured data compared to non-convolutional layers.
Bayesian classifier predicts journey routes using Markov chains.
problem Predicting the route of ongoing journeys.
method Modeling journey patterns as stochastic processes and updating posterior probabilities with Markov chains.
result High accuracy in route predictions demonstrated on synthetic data.
New method certifies deep graph classifiers with tighter risk bounds.
problem Certifying the reliability of deep graph classifiers.
method Linearized deep assignment flows with random initial conditions, using PAC-Bayes risk certification.
result Computes tighter out-of-sample risk certificates efficiently.
Unified derivation of stochastic order conditions for elliptical distributions.
problem Classifying multivariate elliptical distributions based on stochastic orders.
method Established an identity for comparing expectations of functions of elliptical vectors and used it to derive conditions for stochastic orders.
result Unified derivation of conditions for various stochastic orders in multivariate elliptical distributions.
New methods bound uncertainties in large, noisy data for robust SVM classification.
problem Uncertainty in large, noisy data for robust SVM classification.
method Formulate robust optimization problem with bounding schemes for random features using Random Fourier Features and Nyström methods. Solve with stochastic approximation techniques.
result Efficient solutions for large, noisy data classification.
Hybrid QC system for Bengali questions using smart data balancing.
problem Classifying factoid questions in Bengali.
method Two-stage approach with 1D CNN for coarse classification and SGD for fine classification.
result Effectiveness of smart data balancing technique in improving classification accuracy.
Mutual information bounds generalization error in variational classifiers.
problem Controlling overfitting in variational classifiers.
method Derive bounds on generalization error using mutual information.
result Mutual information bounds the generalization error in variational classifiers.
Motivated by problems of anomaly detection, this paper implements the Neyman-Pearson paradigm to deal with asymmetric errors in binary classification with a convex loss. Given a finite collection of classifiers, we combine them and obtain a new classifier that satisfies simultaneously the two following properties with …
New method reduces bias in neural networks using Wasserstein-2 regularization.
problem Reduces bias in neural network classifiers, especially in image analysis.
method Introduces a Wasserstein-2 regularization term to neural network loss function.
result Improves accuracy and fairness in predictions across different subgroups.
A method for large scale Gaussian process classification has been recently proposed based on expectation propagation (EP). Such a method allows Gaussian process classifiers to be trained on very large datasets that were out of the reach of previous deployments of EP and has been shown to be competitive with related tec…
Proposes a privacy-preserving method for graph embedding.
problem Privacy leakage in adjacency spectral embedding for stochastic blockmodels.
method Differentially private adjacency spectral embedding algorithm for stochastic blockmodels.
result Estimates latent positions close to those by non-private embedding, maintaining accuracy at desired privacy levels.
Paper tackles SCOD problem with optimal strategy and empirical validation.
problem Designing reliable prediction models abstaining from uncertain predictions.
method Bayes classifier for ID data and stochastic linear selector in 2D space.
result POSCOD method outperforms existing OOD methods.
PLIs improve classifier performance by fine-tuning latent representations.
problem Difficult interpretation of high-dimensional latent representations in neural networks.
method Back-propagation of manual changes to low-dimensional embeddings using t-distributed stochastic neighbourhood embeddings.
result Manual separation of class clusters in latent space enhances classifier performance.
Model quantifies market sentiment using news data.
problem Quantifying high-frequency market sentiment for economists.
method Support vector machine classifiers for sentiment analysis; stochastic volatility model for joint evolution.
result News sentiment raises the threshold of volatility reversion.
Study builds a classifier for diffusions with unknown diffusion but known drifts.
problem Multiclass classification of S.D.E. paths with unknown diffusion coefficient.
method Plug-in classifier using nonparametric estimators of drift and diffusion functions.
result Consistent classification procedure with rate of convergence under different assumptions.
Study compares chi-squared divergence and KL-divergence posteriors for PAC-Bayesian bounds.
problem Investigates optimal posteriors for PAC-Bayesian bounds using chi-squared divergence.
method Analyzes bounds for three distance functions, derives FP equations for computation.
result Chi-squared divergence based posteriors have weaker bounds and worse test errors.
Skew Gaussian Processes improve classification performance by allowing asymmetry.
problem Limited use of Gaussian processes in applications requiring asymmetry.
method Propose Skew-Gaussian processes (SkewGPs) as a non-parametric prior over functions, extending the multivariate Unified Skew-Normal distribution to stochastic processes.
result SkewGPs provide better performance than symmetric Gaussian processes in classification tasks.
New metric penalizes misclassifying non-hierarchical items more harshly.
problem Misclassifying non-hierarchical items like skyscrapers vs. poodles.
method Defines an ultrametric tree-based hierarchical loss.
result Hierarchical loss is unreliable for plain stochastic gradient descent.
Local elasticity in neural networks makes predictions resilient to dissimilar updates.
problem Understanding resilience of neural network predictions to updates from dissimilar data.
method Simulation and geometric interpretation using neural tangent kernel.
result Local elasticity persists in neural networks with nonlinear activation functions, not in linear ones.
An inductive probabilistic classification rule must generally obey the principles of Bayesian predictive inference, such that all observed and unobserved stochastic quantities are jointly modeled and the parameter uncertainty is fully acknowledged through the posterior predictive distribution. Several such rules have b…
This work shows neural networks can solve non-convex constraints problems.
problem Training neural networks under non-convex constraints.
method Project stochastic gradient descent with no-regret analysis of online learning.
result Overparameterized neural networks achieve near-optimal and near-feasible solutions.