Zobrazeno 1 - 10
of 551
pro vyhledávání: '"Djuric, Petar"'
Causal discovery with time series data remains a challenging yet increasingly important task across many scientific domains. Convergent cross mapping (CCM) and related methods have been proposed to study time series that are generated by dynamical sy
Externí odkaz:
http://arxiv.org/abs/2410.23499
Random feature latent variable models (RFLVMs) represent the state-of-the-art in latent variable models, capable of handling non-Gaussian likelihoods and effectively uncovering patterns in high-dimensional data. However, their heavy reliance on Monte
Externí odkaz:
http://arxiv.org/abs/2410.17700
Counterfactual reasoning allows us to explore hypothetical scenarios in order to explain the impacts of our decisions. However, addressing such inquires is impossible without establishing the appropriate mathematical framework. In this work, we intro
Externí odkaz:
http://arxiv.org/abs/2406.19573
A Gaussian Process-based Streaming Algorithm for Prediction of Time Series With Regimes and Outliers
Autor:
Waxman, Daniel, Djurić, Petar M.
Online prediction of time series under regime switching is a widely studied problem in the literature, with many celebrated approaches. Using the non-parametric flexibility of Gaussian processes, the recently proposed INTEL algorithm provides a produ
Externí odkaz:
http://arxiv.org/abs/2406.00570
Autor:
Fontanesi, Gianluca, Guerra, Anna, Guidi, Francesco, Vásquez-Peralvo, Juan A., Shlezinger, Nir, Zanella, Alberto, Lagunas, Eva, Chatzinotas, Symeon, Dardari, Davide, Djurić, Petar M.
In this paper, we consider a scenario with one UAV equipped with a ULA, which sends combined information and sensing signals to communicate with multiple GBS and, at the same time, senses potential targets placed within an interested area on the grou
Externí odkaz:
http://arxiv.org/abs/2405.17015
Autor:
Waxman, Daniel, Djurić, Petar M.
Publikováno v:
Transactions on Machine Learning Research (TMLR), 2024
Practical Bayesian learning often requires (1) online inference, (2) dynamic models, and (3) ensembling over multiple different models. Recent advances have shown how to use random feature approximations to achieve scalable, online ensembling of Gaus
Externí odkaz:
http://arxiv.org/abs/2405.01365
The field of explainable artificial intelligence (XAI) attempts to develop methods that provide insight into how complicated machine learning methods make predictions. Many methods of explanation have focused on the concept of feature attribution, a
Externí odkaz:
http://arxiv.org/abs/2403.07072
Publikováno v:
2023 57th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2023, pp. 1367-1371
In science and engineering, we often work with models designed for accurate prediction of variables of interest. Recognizing that these models are approximations of reality, it becomes desirable to apply multiple models to the same data and integrate
Externí odkaz:
http://arxiv.org/abs/2403.01389
Publikováno v:
IEEE Open Journal of Signal Processing, vol. 5, pp. 393-401, 2024
We introduce Dagma-DCE, an interpretable and model-agnostic scheme for differentiable causal discovery. Current non- or over-parametric methods in differentiable causal discovery use opaque proxies of ``independence'' to justify the inclusion or excl
Externí odkaz:
http://arxiv.org/abs/2401.02930
In this paper, we propose novel Gaussian process-gated hierarchical mixtures of experts (GPHMEs). Unlike other mixtures of experts with gating models linear in the input, our model employs gating functions built with Gaussian processes (GPs). These p
Externí odkaz:
http://arxiv.org/abs/2302.04947