arXiv research

Estimates treatment effect using ratio of potential outcomes in MS patients.

A method removes treatment-covariate dependence for counterfactual prediction without adversarial training.

CAPITAL algorithm identifies optimal patient subgroups for better treatment.

Proposes a new decision rule for continuous treatments.

Study compares Cox model and RSF for predicting patient survival, finding RSF superior in certain scenarios.

Algorithm uncovers treatment effect heterogeneity in educational RD designs.

Inpatient care is a large share of total health care spending, making analysis of inpatient utilization patterns an important part of understanding what drives health care spending growth. Common features of inpatient utilization measures include zero inflation, over-dispersion, and skewness, all of which complicate st…

Estimating the effect of a treatment on a given outcome, conditioned on a vector of covariates, is central in many applications. However, learning the impact of a treatment on a continuous temporal response, when the covariates suffer extensively from measurement error and even the timing of the treatments is uncertain…

Method improves treatment effect estimation in randomized experiments.

Adaptive designs achieve strong Neyman regret guarantees for ATE estimation.

Proposes a method for generating prediction intervals in dose-response models using conformal prediction.

Paper tackles CATE estimation with missing treatment info.

GANICE improves GAN-based causal inference by minimizing averaged Wasserstein risk.

New methods combine AI with traditional stats for better treatment effect estimation.

CNN improves causal inference by controlling time-structured covariates.

The paper develops methods to estimate treatment effects in sample selection models.

The paper assesses the risk of negative treatment effects using bounds and inference.

Surrogate-based analysis of interactions via local effect smooths

Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless featur…

A new method detects interactions in neural networks using topological analysis.

iKF method uncovers complex variable interactions for scientific discovery.

Graph neural network predicts vehicle interactions and trajectories for autonomous driving.

A graph neural network detects beneficial feature interactions for recommender systems.

We describe and extract time-ordered multibody interactions from complex systems.

Dropout regularizes against high-order interactions by canceling interaction rates.

Understanding how features interact with each other is of paramount importance in many scientific discoveries and contemporary applications. Yet interaction identification becomes challenging even for a moderate number of covariates. In this paper, we suggest an efficient and flexible procedure, called the interaction …

Multitask Gaussian process (MTGP) is powerful for joint learning of multiple tasks with complicated correlation patterns. However, due to the assembling of additive independent latent functions, all current MTGPs including the salient linear model of coregionalization (LMC) and convolution frameworks cannot effectively…

SocialInteractionGAN generates realistic human interactions from low-dimensional data.

A new method detects interactions in machine learning models.

xDeepInt learns both vector-wise and bit-wise feature interactions.

Most existing knowledge graphs suffer from incompleteness, which can be alleviated by inferring missing links based on known facts. One popular way to accomplish this is to generate low-dimensional embeddings of entities and relations, and use these to make inferences. ConvE, a recently proposed approach, applies convo…

This study explores how feature graphs enhance GNNs' performance in modeling interactions.

Interpreting neural networks is a crucial and challenging task in machine learning. In this paper, we develop a novel framework for detecting statistical interactions captured by a feedforward multilayer neural network by directly interpreting its learned weights. Depending on the desired interactions, our method can a…

We introduce interactive structure discovery, a generic framework that encompasses many interactive learning settings, including active learning, top-k item identification, interactive drug discovery, and others. We adapt a recently developed active learning algorithm of Tosh and Dasgupta (2017) for interactive structu…

Paper introduces IC-index to evaluate interaction prediction methods.

This paper uses Gaussian mixtures to mimic interactions in large language models.

Study reveals self-attention's role in learning and generalizing interactions.

Automated tests detect interactions in unstructured data.

Archipelago provides interpretable explanations of feature interactions in machine learning models.

We study the power of interactivity in local differential privacy. First, we focus on the difference between fully interactive and sequentially interactive protocols. Sequentially interactive protocols may query users adaptively in sequence, but they cannot return to previously queried users. The vast majority of exist…

PIN models feature interactions using a neural network that mimics decision trees.

The paper explains DNNs by quantifying interactions among input variables.

Modeling continuous movement of entities in latent space for interaction timing.

The paper improves dropout's utility by reducing interactions in deep neural networks.

New model quantifies interactions' role in real-world phenomena.

MEI model improves knowledge graph completion by efficiently modeling interactions between embeddings.

Paper proposes FINN for better CTR prediction by modeling feature interactions.

We discuss possible relationships between geometric and topological interactions on one side and physical interactions on the other side.