A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
A new method detects interactions in neural networks using topological analysis.
A new method detects interactions in machine learning models.
Method detects interactions for better CTR prediction.
A graph neural network detects beneficial feature interactions for recommender systems.
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…
Surrogate-based analysis of interactions via local effect smooths
AnomalyDAE detects anomalies in networks by learning cross-modality interactions.
EHBOS enhances HBOS by capturing feature interactions, improving anomaly detection.
In this paper, we propose a hybrid bankcard response model, which integrates decision tree based chi-square automatic interaction detection (CHAID) into logistic regression. In the first stage of the hybrid model, CHAID analysis is used to detect the possibly potential variable interactions. Then in the second stage, t…
Unified approach detects traffic conflicts across various interactions.
New method detects biomarker-treatment interactions in clinical trials.
Epistasis (gene-gene interaction) is crucial to predicting genetic disease. Our work tackles the computational challenges faced by previous works in epistasis detection by modeling it as a one-step Markov Decision Process where the state is genome data, the actions are the interacted genes, and the reward is an interac…
Remembering our day-to-day social interactions is challenging even if you aren't a blue memory challenged fish. The ability to automatically detect and remember these types of interactions is not only beneficial for individuals interested in their behavior in crowded situations, but also of interest to those who analyz…
Automated tests detect interactions in unstructured data.
New method combines hypergraph structure and node attributes for better community detection.
We propose a new method for assessing agents' influence in financial network structures, which takes into consideration the intensity of interactions. A distinctive feature of this approach is that it considers not only direct interactions of agents of the first level and indirect interactions of the second level, but …
New method detects communities in hypergraphs by embedding them into a vector space.
Automates finding interactions in GLMs using neural networks.
Bayesian method detects mesoscale structures in pathway data networks.
Detection of interactions between treatment effects and patient descriptors in clinical trials is critical for optimizing the drug development process. The increasing volume of data accumulated in clinical trials provides a unique opportunity to discover new biomarkers and further the goal of personalized medicine, but…
Estimating global pairwise interaction effects, i.e., the difference between the joint effect and the sum of marginal effects of two input features, with uncertainty properly quantified, is centrally important in science applications. We propose a non-parametric probabilistic method for detecting interaction effects of…
This paper presents a self-supervised method for visual detection of the active speaker in a multi-person spoken interaction scenario. Active speaker detection is a fundamental prerequisite for any artificial cognitive system attempting to acquire language in social settings. The proposed method is intended to compleme…
A framework detects nonlinear and interaction effects in epidemiological data with uncertainty quantification.
New tests detect high-order interactions without permutations.
SIAN bridges simple models to neural networks by identifying necessary feature combinations.
Generative model reveals hidden interaction preferences in networks.
Interactions among people or objects are often dynamic in nature and can be represented as a sequence of networks, each providing a snapshot of the interactions over a brief period of time. An important task in analyzing such evolving networks is change-point detection, in which we both identify the times at which the …
A framework infers hyperedges and overlapping communities in hypergraphs.
Game aims to improve social interactions for teenagers with ASD.
Relational Graph Neural Networks improve fraud detection in Super-Apps.
Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as components of larger molecular machines. Understanding how these high-order in…
Hybrid machine learning improves gallstone risk prediction.
GUIDE detects anomalies in attributed networks by reconstructing node attributes and higher-order structures.
To investigate the universal structure of interactions in financial dynamics, we analyze the cross-correlation matrix C of price returns of the Chinese stock market, in comparison with those of the American and Indian stock markets. As an important emerging market, the Chinese market exhibits much stronger correlations…
Improved Random Forests detect pure interactions better.
GADGET framework decomposes global feature effects using recursive partitioning.
Adversarial perturbations and RIS interaction vectors improve covert communication.
New framework for detecting complex interactions in multivariate data.
Anomaly detection plays an important role in modern data-driven security applications, such as detecting suspicious access to a socket from a process. In many cases, such events can be described as a collection of categorical values that are considered as entities of different types, which we call heterogeneous categor…
Archipelago provides interpretable explanations of feature interactions in machine learning models.
New method detects uncertainty in neural networks for out-of-distribution detection.
Developing an explainable outlier detection method for interval-valued data using Shapley value-based approach.
We accelerate Bayesian inference for neutrino physics experiments by 100-60x.
We expand the item response theory to study the case of "cheating students" for a set of exams, trying to detect them by applying a greedy algorithm of inference. This extended model is closely related to the Boltzmann machine learning. In this paper we aim to infer the correct biases and interactions of our model by c…
Extracting and detecting spike activities from the fluorescence observations is an important step in understanding how neuron systems work. The main challenge lies in that the combination of the ambient noise with dynamic baseline fluctuation, often contaminates the observations, thereby deteriorating the reliability o…
The search for higher-order feature interactions that are statistically significantly associated with a class variable is of high relevance in fields such as Genetics or Healthcare, but the combinatorial explosion of the candidate space makes this problem extremely challenging in terms of computational efficiency and p…
One component of precision medicine is to construct prediction models with their predictive ability as high as possible, e.g. to enable individual risk prediction. In genetic epidemiology, complex diseases have a polygenic basis and a common assumption is that biological and genetic features affect the outcome under co…
Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traff…