arXiv research

This work introduces a bias-variance decomposition for proper scores, improving uncertainty estimation in predictive models.

New methods for scoring function decomposition improve forecast evaluation.

Paper proposes a new method for training diffusion models using Markov operators.

A novel framework quantifies uncertainty using proper scores for various tasks.

Improves reliability diagrams for probabilistic forecasts.

Unified methods for fast column selection in various applications.

New method disentangles feature importance scores in machine learning.

The paper introduces a new framework to assess generative model uncertainty.

Study federates measurement of demographic disparities from quantile sketches.

The paper decomposes probabilistic scores into reliability, uncertainty, and information loss.

A method uses ITD and XGBoost for precise power transformer fault diagnosis.

New approach speeds up DNA sequence alignment.

Foresight Arena benchmarks AI forecasting on real-world markets, isolating predictive edge.

We implement a Tensor Train layer in the TensorFlow Neural Machine Translation (NMT) model using the t3f library. We perform training runs on the IWSLT English-Vietnamese '15 and WMT German-English '16 datasets with learning rates $\in \{0.0004,0.0008,0.0012\}$, maximum ranks $\in \{2,4,8,16\}$ and a range of core dime…

New analysis reveals gaps in selective classifiers, guiding improvements.

Paper introduces SUEL model for integrating predictors without labeled data.

We illustrate relationships between classical kernel-based dimensionality reduction techniques and eigendecompositions of empirical estimates of reproducing kernel Hilbert space (RKHS) operators associated with dynamical systems. In particular, we show that kernel canonical correlation analysis (CCA) can be interpreted…

Proposes measures for uncertainty quantification using proper scoring rules.

Simplifies denoising score matching for manifold learning.

Improved text-to-image alignment using iterative VQA feedback.

Improved sample complexity for diffusion models without needing empirical risk minimizers.

Where machine-learned predictive risk scores inform high-stakes decisions, such as bail and sentencing in criminal justice, fairness has been a serious concern. Recent work has characterized the disparate impact that such risk scores can have when used for a binary classification task. This may not account, however, fo…

Improved diffusion models using energy distillation and sequential Monte Carlo.

Improved diffusion models for generative tasks without dimensionality constraints.

A new diffusion model generates structured tensors for high-dimensional data.

New geometric analysis shows $L^2$ score error is flawed for diffusion models.

Score matching errors are not sufficient for measuring diffusion model quality.

Proposes a new method for feature selection using Bayesian ID with intervention.

HiJoD detects misinformation using multiple aspects and outperforms state-of-the-art methods.

The paper analyzes the score field of diffusion models using Burgers dynamics.

Kernel-smoothed scores improve diffusion models by reducing memorization.

This is a detailed tutorial paper which explains the Principal Component Analysis (PCA), Supervised PCA (SPCA), kernel PCA, and kernel SPCA. We start with projection, PCA with eigen-decomposition, PCA with one and multiple projection directions, properties of the projection matrix, reconstruction error minimization, an…

New method optimizes portfolios by dynamically integrating ESG constraints.

Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms f…

The study improves theoretical understanding of using multiple synthetic datasets for better model accuracy.

Framework monitors insurance pricing models for drift and recalibration.

Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms f…

New framework for discrete-state diffusion models reduces sample complexity.

We consider the problem of learning mixtures of generalized linear models (GLM) which arise in classification and regression problems. Typical learning approaches such as expectation maximization (EM) or variational Bayes can get stuck in spurious local optima. In contrast, we present a tensor decomposition method whic…

Classifiers and rating scores are prone to implicitly codifying biases, which may be present in the training data, against protected classes (i.e., age, gender, or race). So it is important to understand how to design classifiers and scores that prevent discrimination in predictions. This paper develops computationally…

Class labels have been empirically shown useful in improving the sample quality of generative adversarial nets (GANs). In this paper, we mathematically study the properties of the current variants of GANs that make use of class label information. With class aware gradient and cross-entropy decomposition, we reveal how …

Study improves KRR for non-i.i.d. data, with applications in denoising.

The power of randomized algorithms in numerical methods have led to fast solutions which use the Singular Value Decomposition (SVD) as a core routine. However, given the large data size of modern and the modest runtime of SVD, most practical algorithms would require some form of approximation, such as sketching, when r…

New metric for disentangling multivariate representations, accounting for more complex entanglements.

From linear classifiers to neural networks, image classification has been a widely explored topic in mathematics, and many algorithms have proven to be effective classifiers. However, the most accurate classifiers typically have significantly high storage costs, or require complicated procedures that may be computation…

New method assesses individual training points' privacy risk without retraining.

ForesightFlow detects informed trading on prediction markets using an information leakage score.

The paper proposes a method to construct well-calibrated prediction sets for correlated target variables.