no code implementations • 20 Nov 2023 • Max Zhu, Siniša Stanivuk, Andrija Petrovic, Mladen Nikolic, Pietro Lio
We present a method to integrate Large Language Models (LLMs) and traditional tabular data classification techniques, addressing LLMs challenges like data serialization sensitivity and biases.
no code implementations • 16 Nov 2023 • Max Zhu, Katarzyna Kobalczyk, Andrija Petrovic, Mladen Nikolic, Mihaela van der Schaar, Boris Delibasic, Petro Lio
Despite the prevalence of tabular datasets, few-shot learning remains under-explored within this domain.
no code implementations • 8 Jun 2023 • Aleksa Bisercic, Mladen Nikolic, Mihaela van der Schaar, Boris Delibasic, Pietro Lio, Andrija Petrovic
Drawing upon the reasoning capabilities of LLMs, TEMED-LLM goes beyond traditional extraction techniques, accurately inferring tabular features, even when their names are not explicitly mentioned in the text.
no code implementations • 25 Sep 2019 • Marko Vasic, Andrija Petrovic, Kaiyuan Wang, Mladen Nikolic, Rishabh Singh, Sarfraz Khurshid
We propose MoET, a more expressive, yet still interpretable model based on Mixture of Experts, consisting of a gating function that partitions the state space, and multiple decision tree experts that specialize on different partitions.
no code implementations • 25 Sep 2019 • Andrija Petrovic, Mladen Nikolic, Milos Jovanovic, Boris Delibasic
The extended method of local variational approximation of sigmoid function is used for solving empirical Bayes in GCRFBCb variant, whereas MAP value of latent variables is the basis for learning and inference in the GCRFBCnb variant.
2 code implementations • 16 Jun 2019 • Marko Vasic, Andrija Petrovic, Kaiyuan Wang, Mladen Nikolic, Rishabh Singh, Sarfraz Khurshid
By training Mo\"ET models using an imitation learning procedure on deep RL agents we outperform the previous state-of-the-art technique based on decision trees while preserving the verifiability of the models.