arXiv research

Estimates joint causal effects using single-variable interventions on nonlinear models.

Introduces joint Shapley values to measure feature importance in models.

Credit risk analysis improved with a joint model for spatial and temporal effects.

Paper uses non-Euclidean analysis to classify brain structure variations.

Generates counterfactuals in target domain from source domain observations.

VPP learns joint policies for multi-agent RL through interactions.

New method certifies joint adversarial robustness of model ensembles.

This work optimizes induced correlation in joint graph embeddings.

Study characterizes cryospheric spectral feature space using joint PC+t-SNE approach.

EB-VAE combines tumor growth and dropout data for personalized treatment response modeling.

DSL estimates heterogeneous treatment effects over time in survival settings.

Bayesian model for cost-effectiveness analysis with subgroup discovery.

There has recently been considerable interest in completing a low-rank matrix or tensor given only a small fraction (or few linear combinations) of its entries. Related approaches have found considerable success in the area of recommender systems, under machine learning. From a statistical estimation point of view, the…

Unified model learns joint and individual features from brain imaging data.

In this paper, we introduce the problem of jointly learning feed-forward neural networks across a set of relevant but diverse datasets. Compared to learning a separate network from each dataset in isolation, joint learning enables us to extract correlated information across multiple datasets to significantly improve th…

We investigate the joint dynamics of spot and implied volatility from an empirical perspective. We focus on the equity market with the SPX Index our underlying of choice. Using only observable quantities, we extract the instantaneous variance curves implied by the market and study their daily variations jointly with sp…

A novel extrapolation method is proposed for longitudinal forecasting. A hierarchical Gaussian process model is used to combine nonlinear population change and individual memory of the past to make prediction. The prediction error is minimized through the hierarchical design. The method is further extended to joint mod…

This work proposes a new method to estimate joint probability from pairwise marginals, reducing sample complexity.

Proposes a new method to explain complex machine learning models.

This paper focusses on "safe" screening techniques for the LASSO problem. Motivated by the need for low-complexity algorithms, we propose a new approach, dubbed "joint" screening test, allowing to screen a set of atoms by carrying out one single test. The approach is particularized to two different sets of atoms, respe…

Develops a framework to quantify uncertainties in multiple ML models.

RaSE screens variables via random subspaces, identifying joint effects.

Proposes a new model to better handle correlation risk in credit risk calculations.

JoLT uses LLMs to make probabilistic predictions on tabular data.

Joint sparsity regularization in multi-task learning has attracted much attention in recent years. The traditional convex formulation employs the group Lasso relaxation to achieve joint sparsity across tasks. Although this approach leads to a simple convex formulation, it suffers from several issues due to the loosenes…

Estimates multiple related causal graphs with shared causal order.

Novel approach for estimating joint probability densities using tensor decompositions and dictionaries.

This paper proposes a method to select relevant features for multi-label learning.

Common complex diseases are likely influenced by the interplay of hundreds, or even thousands, of genetic variants. Converging evidence shows that genetic variants with low marginal effects (LME) play an important role in disease development. Despite their potential significance, discovering LME genetic variants and as…

First passage models, where corporate assets undergo correlated random walks and a company defaults if its assets fall below a threshold provide an attractive framework for modeling the default process. Typical one year default correlations are small, i.e., of order a few percent, but nonetheless including correlations…

Estimates effects of multiple interventions with hidden confounders using single-variable interventions.

The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.

Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach common a data structure in clinical studies where longitudinal outcomes are recorded alongside event times. Those two processes are often linked and the two outcomes shou…

TransformerLSR models longitudinal, recurrent, and survival data jointly.

New method detects and analyzes correlation in multiple network data.

Improves joint distribution learning for high-dimensional datasets with complex correlations.

A new framework models multi-state events and biomarkers.

We consider the problem of identifying intermediate variables (or mediators) that regulate the effect of a treatment on a response variable. While there has been significant research on this classical topic, little work has been done when the set of potential mediators is high-dimensional (HD). A further complication a…

FreST Loss decorrelates spatio-temporal dependencies in graph signals.

In this article, the long-term behavior of the stock market index of the New York Stock Exchange is studied, for the period 1950 to 2013. Specifically, the CRSP Value-Weighted and CRSP Equal-Weighted index are analyzed in terms of market efficiency, using the standard ratio variance test, considering over 1600 one week…

Estimating the difficulty level of math word problems is an important task for many educational applications. Identification of relevant and irrelevant sentences in math word problems is an important step for calculating the difficulty levels of such problems. This paper addresses a novel application of text categoriza…

Current state-of-the-art nonparametric Bayesian text clustering methods model documents through multinomial distribution on bags of words. Although these methods can effectively utilize the word burstiness representation of documents and achieve decent performance, they do not explore the sequential information of text…

DeepBDC improves few-shot classification by measuring joint distributions of image features.

Optimal online learning for joint pricing and resource allocation.

Cooperation information sharing is important to theories of human learning and has potential implications for machine learning. Prior work derived conditions for achieving optimal Cooperative Inference given strong, relatively restrictive assumptions. We relax these assumptions by demonstrating convergence for any disc…

Learning network representations is a fundamental task for many graph applications such as link prediction, node classification, graph clustering, and graph visualization. Many real-world networks are interpreted as dynamic networks and evolve over time. Most existing graph embedding algorithms were developed for stati…

A new method for aligning datasets without known correspondences.

We analyse time series of CDS spreads for a set of major US and European institutions on a pe- riod overlapping the recent financial crisis. We extend the existing methodology of ε-drawdowns to the one of joint ε-drawups, in order to estimate the conditional probabilities of abrupt co-movements among spreads. We correc…