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Found 9 papers, 2 papers with code
Choice Models and Permutation Invariance: Demand Estimation in Differentiated Products Markets
We demonstrate how non-parametric estimators like neural nets can easily approximate such functionals and overcome the curse of dimensionality that is inherent in the non-parametric estimation of choice functions.
Interpretable Anomaly Detection in Cellular Networks by Learning Concepts in Variational Autoencoders
This paper addresses the challenges of detecting anomalies in cellular networks in an interpretable way and proposes a new approach using variational autoencoders (VAEs) that learn interpretable representations of the latent space for each Key Performance Indicator (KPI) in the dataset.
Can LLMs Capture Human Preferences?
We explore the viability of Large Language Models (LLMs), specifically OpenAI's GPT-3. 5 and GPT-4, in emulating human survey respondents and eliciting preferences, with a focus on intertemporal choices.
Causal Bandits: Online Decision-Making in Endogenous Settings
To this end, in this paper, we consider the problem of online learning in linear stochastic contextual bandit problems with endogenous covariates.
User-friendly Comparison of Similarity Algorithms on Wikidata
While the similarity between two concept words has been evaluated and studied for decades, much less attention has been devoted to algorithms that can compute the similarity of nodes in very large knowledge graphs, like Wikidata.
Experimental Quantum Principal Component Analysis via Parametrized Quantum Circuits
Principal component analysis (PCA) is a widely applied but rather time-consuming tool in machine learning techniques.
Sparse-Push: Communication- & Energy-Efficient Decentralized Distributed Learning over Directed & Time-Varying Graphs with non-IID Datasets
Current deep learning (DL) systems rely on a centralized computing paradigm which limits the amount of available training data, increases system latency, and adds privacy and security constraints.
Causal Gradient Boosting: Boosted Instrumental Variable Regression
In this paper, we propose an alternative algorithm called boostIV that builds on the traditional gradient boosting algorithm and corrects for the endogeneity bias.
KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis
Knowledge graphs (KGs) have become the preferred technology for representing, sharing and adding knowledge to modern AI applications.
