arXiv research

New method for uncertainty analysis in TabPFN, a state-of-the-art tabular transformer.

This study proposes methods for multi-step-ahead stock price prediction using decomposition and neural networks.

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

New method uses conformal prediction for time series forecasting, accounting for temporal correlation.

A new decomposition explains over-parameterized models' counterintuitive behaviors.

MicroRNAs (miRNAs) play crucial roles in multifarious biological processes associated with human diseases. Identifying potential miRNA-disease associations contributes to understanding the molecular mechanisms of miRNA-related diseases. Most of the existing computational methods mainly focus on predicting whether a miR…

Proposes a method to predict responses from covariates over time.

We propose the Relational Tucker3 (RT) decomposition for multi-relational link prediction in knowledge graphs. We show that many existing knowledge graph embedding models are special cases of the RT decomposition with certain predefined sparsity patterns in its components. In contrast to these prior models, RT decouple…

TATD predicts missing entries in time-evolving tensors by exploiting temporal dependency and sparsity.

We discuss structured Schatten norms for tensor decomposition that includes two recently proposed norms ("overlapped" and "latent") for convex-optimization-based tensor decomposition, and connect tensor decomposition with wider literature on structured sparsity. Based on the properties of the structured Schatten norms,…

Enhances forecasting of complex systems using FKMD.

Paper decomposes C-index to analyze survival prediction model performance.

A new method models financial returns by separating sign and magnitude, improving forecasting accuracy.

NA$_0$CT$^2$ improves tensor regression predictions with $\ell_0$ regularization.

New method quantifies redundant information using information bottleneck.

Tensor decomposition methods are widely used for model compression and fast inference in convolutional neural networks (CNNs). Although many decompositions are conceivable, only CP decomposition and a few others have been applied in practice, and no extensive comparisons have been made between available methods. Previo…

KEDformer improves long-term time series forecasting with seasonal-trend decomposition.

A new tensor-based method for predicting temporal relationships in knowledge bases.

Every cusped, finite-volume hyperbolic three-manifold has a canonical decomposition into ideal polyhedra. We study the canonical decomposition of the hyperbolic manifold obtained by filling some (but not all) of the cusps with solid tori: in a broad range of cases, generic in an appropriate sense, this decomposition ca…

SurvFD and SurvSHAP-IQ provide interpretable survival models by analyzing feature interactions.

In multi-label learning, each sample is associated with several labels. Existing works indicate that exploring correlations between labels improve the prediction performance. However, embedding the label correlations into the training process significantly increases the problem size. Moreover, the mapping of the label …

Generalizes bias-variance decomposition for Bregman divergences.

Bayesian neural networks (BNNs) with latent variables are probabilistic models which can automatically identify complex stochastic patterns in the data. We describe and study in these models a decomposition of predictive uncertainty into its epistemic and aleatoric components. First, we show how such a decomposition ar…

Infinite Tucker Decomposition (InfTucker) and random function prior models, as nonparametric Bayesian models on infinite exchangeable arrays, are more powerful models than widely-used multilinear factorization methods including Tucker and PARAFAC decomposition, (partly) due to their capability of modeling nonlinear rel…

Paper decomposes risk into aleatoric and epistemic uncertainties and generates predictive uncertainty measures.

This work introduces a method to decompose uncertainty in in-context learning for large language models.

The paper studies and mitigates accuracy disparity in regression models.

The paper studies batch decompositions of random datasets with probabilistic similarity constraints.

Model predicts short-term Amazon rainforest fires with high accuracy.

The PARAFAC tensor decomposition has enjoyed an increasing success in exploratory multi-aspect data mining scenarios. A major challenge remains the estimation of the number of latent factors (i.e., the rank) of the decomposition, which yields high-quality, interpretable results. Previously, we have proposed an automate…

In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockc…

New methods for scoring function decomposition improve forecast evaluation.

The paper proposes a method to balance fairness and prediction accuracy by adjusting data representations.

Method provides formal guarantees for decomposing model uncertainty.

The digital revolution of the banking system with evolving European regulations have pushed the major banking actors to innovate by a newly use of their clients' digital information. Given highly sparse client activities, we propose CPOPT-Net, an algorithm that combines the CP canonical tensor decomposition, a multidim…

This paper introduces a new framework for quantifying predictive uncertainty for both data and models that relies on projecting the data into a Gaussian reproducing kernel Hilbert space (RKHS) and transforming the data probability density function (PDF) in a way that quantifies the flow of its gradient as a topological…

Ensembles of neural networks have been shown to give better performance than single networks, both in terms of predictions and uncertainty estimation. Additionally, ensembles allow the uncertainty to be decomposed into aleatoric (data) and epistemic (model) components, giving a more complete picture of the predictive u…

Machine learning can improve 2SLS first stage predictions, but nonlinear methods often introduce bias.

We propose a novel sparse tensor decomposition method, namely Tensor Truncated Power (TTP) method, that incorporates variable selection into the estimation of decomposition components. The sparsity is achieved via an efficient truncation step embedded in the tensor power iteration. Our method applies to a broad family …

Proposes a new method for uncertainty estimation in neural networks.

Paper uses AI methods to forecast Bitcoin prices.

We address primary decomposition conjectures for knot concordance groups, which predict direct sum decompositions into primary parts. We show that the smooth concordance group of topologically slice knots has a large subgroup for which the conjectures are true and there are infinitely many primary parts each of which h…

A CNN-based model improves stock price prediction accuracy.

In this paper, a unified susceptible-exposed-infected-susceptible-aware (SEIS-A) framework is proposed to combine epidemic spreading with individuals' on-line self-consultation behaviors. An epidemic spreading prediction model is established based on the SEIS-A framework. The prediction process contains two phases. In …

Marginal MAP inference involves making MAP predictions in systems defined with latent variables or missing information. It is significantly more difficult than pure marginalization and MAP tasks, for which a large class of efficient and convergent variational algorithms, such as dual decomposition, exist. In this work,…

We provide a unified view of additive explanations for dependent inputs.

We propose a heterogeneous simultaneous graphical dynamic linear model (H-SGDLM), which extends the standard SGDLM framework to incorporate a heterogeneous autoregressive realised volatility (HAR-RV) model. This novel approach creates a GPU-scalable multivariate volatility estimator, which decomposes multiple time seri…

Enhances stock movement prediction using Higher Order Transformers for multimodal time-series data.