no code implementations • 13 Jul 2023 • Amandeep Singh, Ye Liu, Hema Yoganarasimhan
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.
no code implementations • 28 Jun 2023 • Amandeep Singh, Michael Weber, Markus Lange-Hegermann
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.
no code implementations • 4 May 2023 • Ali Goli, Amandeep Singh
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.
no code implementations • 16 Nov 2022 • Jingwen Zhang, Yifang Chen, Amandeep Singh
To this end, in this paper, we consider the problem of online learning in linear stochastic contextual bandit problems with endogenous covariates.
1 code implementation • 11 Aug 2021 • Filip Ilievski, Pedro Szekely, Gleb Satyukov, Amandeep Singh
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.
no code implementations • PHYSICAL REVIEW LETTERS 2021 • Tao Xin, Liangyu Che, Cheng Xi, Amandeep Singh, Xinfang Nie, Jun Li, Ying Dong and Dawei Lu
Principal component analysis (PCA) is a widely applied but rather time-consuming tool in machine learning techniques.
no code implementations • 10 Feb 2021 • Sai Aparna Aketi, Amandeep Singh, Jan Rabaey
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.
no code implementations • 15 Jan 2021 • Edvard Bakhitov, Amandeep Singh
In this paper, we propose an alternative algorithm called boostIV that builds on the traditional gradient boosting algorithm and corrects for the endogeneity bias.
1 code implementation • 29 May 2020 • Filip Ilievski, Daniel Garijo, Hans Chalupsky, Naren Teja Divvala, Yixiang Yao, Craig Rogers, Rongpeng Li, Jun Liu, Amandeep Singh, Daniel Schwabe, Pedro Szekely
Knowledge graphs (KGs) have become the preferred technology for representing, sharing and adding knowledge to modern AI applications.