arXiv research

PrAda-GAN improves synthetic data generation under differential privacy.

New estimators outperform maximum likelihood without hyper-parameter estimation.

A new framework for private Bayesian tests maintains interpretability and computational efficiency.

Adaptive variational Bayes framework improves inference adaptively.

Bayesian inference improves neural network pruning efficiency.

Bayesian algorithm improves word representations using semantic taxonomy.

This paper proposes a systematic framework to design a classification model that yields a classifier which optimizes a utility function based on prior knowledge. Specifically, as the data size grows, we prove that the produced classifier asymptotically converges to the optimal classifier, an extended version of the Bay…

Proposes a new trust framework for AI models to maximize utility.

We develop an empirical Bayes (EB) algorithm for the matrix completion problems. The EB algorithm is motivated from the singular value shrinkage estimator for matrix means by Efron and Morris (1972). Since the EB algorithm is essentially the EM algorithm applied to a simple model, it does not require heuristic paramete…

Paper estimates FPR of Bayes classifier using soft labels.

XNB classifier improves model interpretability by selecting class-specific features.

In machine learning, classification models need to be trained in order to predict class labels. When the training data contains personal information about individuals, collecting training data becomes difficult due to privacy concerns. Local differential privacy is a definition to measure the individual privacy when th…

Paper proposes a VB method for TS-SBP mixture models with reduced computational cost.

Radio tomographic imaging (RTI) is an emerging technology for localization of physical objects in a geographical area covered by wireless networks. With attenuation measurements collected at spatially distributed sensors, RTI capitalizes on spatial loss fields (SLFs) measuring the absorption of radio frequency waves at…

In the Bayesian approach to sequential decision making, exact calculation of the (subjective) utility is intractable. This extends to most special cases of interest, such as reinforcement learning problems. While utility bounds are known to exist for this problem, so far none of them were particularly tight. In this pa…

New framework shows cross-attention improves multi-modal in-context learning.

In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ratios, and their application to model selection. The theory is presented along with a discussion of analytic, approximate and numerical techniques. Specific attention is paid to the Laplace approximation, variational Ba…

Paper uses optimal transport for Bayesian filtering, deriving new EnKF and FPF formulations.

Inference for the stochastic blockmodel is currently of burgeoning interest in the statistical community, as well as in various application domains as diverse as social networks, citation networks, brain connectivity networks (connectomics), etc. Recent theoretical developments have shown that spectral embedding of gra…

qEUBO optimizes decision-making with noisy feedback.

AMP method reconstructs rank-one matrices from noisy data efficiently.

Modified Meta-TS for linear contextual bandits reduces regret.

ST-GAN predicts stock trends using financial news and data.

New uniqueness concept for adversarial Bayes classifier.

Empirical Bayes rates via variational approximations and prior decomposition.

The study compares clustering risk in Hidden Markov and i.i.d. models, showing the Bayes classifier is nearly optimal.

Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by these results, researchers have looked to overcome naive Bayes primary weakness - attribute independence - and improve the performance of the algo…

Bayesian methods improve inference for cumulative probit models on large datasets.

Researchers prove NP-hardness of learning parameter-bounded Bayes nets.

PAC-Bayes theory improves ICP efficiency and coverage.

Deep learning improves Bayes factor computation for likelihood-free models.

Meta-learning bounds derived using PAC-Bayes theory for improved generalization.

A new method improves Bayesian inference for multimodal posteriors.

PAC-Bayes framework fails on simple 1D linear classification task.

In meta-learning an agent extracts knowledge from observed tasks, aiming to facilitate learning of novel future tasks. Under the assumption that future tasks are 'related' to previous tasks, the accumulated knowledge should be learned in a way which captures the common structure across learned tasks, while allowing the…

Proposes using external data to improve predictions in medical applications with limited samples.

PAC-Bayes bound requires prior to place mass on high-performing predictors.

Paper introduces Generalized Naive Bayes for better data fitting.

Smart Bayes integrates generative and discriminative features for improved classification.

New method reduces computational cost for estimating PAC-Bayes bounds.

This work bounds classification error in machine learning for low Bayes error conditions.

PPT optimizes transformer behavior by steering its latent posterior using prior samples.

Improved Naive Bayes classifier with neural network models.

Study calculates Bayes risk for semi-supervised learning with uncertain labels.

We extend a recently proposed 1-nearest-neighbor based multiclass learning algorithm and prove that our modification is universally strongly Bayes-consistent in all metric spaces admitting any such learner, making it an "optimistically universal" Bayes-consistent learner. This is the first learning algorithm known to e…

In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. More precisely, the classifier should strive for an optimal balance between the correctness (the true class is among the candidates) and the precision (the ca…

Unified derivation of PAC-Bayes and MI bounds for general VC classes with fast rates.

Bayes classifier cannot be learned from noisy labels without knowing noise distribution.