Publications associées à Forêt d'arbres décisionnels

Boosting likelihood learning with event reweighting

Andrea Wulzer, Alfredo Glioti, Siyu Chen

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

Springer2024

Social Opinion Formation and Decision Making Under Communication Trends

Ali H. Sayed, Mert Kayaalp, Virginia Bordignon

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

Piscataway2024

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Martin Jaggi, Vinitra Swamy, Jibril Albachir Frej, Julian Thomas Blackwell

Interpretability for neural networks is a trade-off between three key requirements: 1) faithfulness of the explanation (i.e., how perfectly it explains the prediction), 2) understandability of the explanation by humans, and 3) model performance. Most exist ...

2024

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Florent Gérard Krzakala, Lenka Zdeborová, Hugo Chao Cui

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

2023

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Rongrong Li, Qiaoli Wang, Yu Xu

Water quality prediction in the spatially heterogeneous environment is challenging as the importance of water quality parameters (WQPs) and the performance of prediction models may vary across space. Thus, this study proposed spatially adaptive machine lea ...

ELSEVIER2023

Contextual Stochastic Bilevel Optimization

Daniel Kuhn, Andreas Krause, Yifan Hu, Jie Wang

We introduce contextual stochastic bilevel optimization (CSBO) -- a stochastic bilevel optimization framework with the lower-level problem minimizing an expectation conditioned on some contextual information and the upper-level decision variable. This fram ...

2023

Discriminating physiological from non‐physiological interfaces in structures of protein complexes: A community‐wide study

Bruno Emanuel Ferreira De Sousa Correia, Anthony Marchand, Emmanuel Doram Levy, Xiao Wang

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest ...

2023

End-to-end kernel learning via generative random Fourier features

Fanghui Liu, Jie Yang

Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the first-stage process, learn-ing an optimal feature map is often formulated as a ...

ELSEVIER SCI LTD2023

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Forêt d'arbres décisionnels

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification

Boosting likelihood learning with event reweighting

Social Opinion Formation and Decision Making Under Communication Trends

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Contextual Stochastic Bilevel Optimization

Discriminating physiological from non‐physiological interfaces in structures of protein complexes: A community‐wide study

End-to-end kernel learning via generative random Fourier features

Graph Chatbot

Chattez avec Graph Search

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Boosting likelihood learning with event reweighting

Social Opinion Formation and Decision Making Under Communication Trends

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Contextual Stochastic Bilevel Optimization

End-to-end kernel learning via generative random Fourier features

Discriminating physiological from non‐physiological interfaces in structures of protein complexes: A community‐wide study