Liudmila Prokhorenkova | DeepAI

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∙ 06/23/2023

Neural Algorithmic Reasoning Without Intermediate Supervision

Neural Algorithmic Reasoning is an emerging area of machine learning foc...

0 Gleb Rodionov, et al. ∙

∙ 02/27/2023

Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts

In reliable decision-making systems based on machine learning, models ha...

0 Gleb Bazhenov, et al. ∙

∙ 02/22/2023

A critical look at the evaluation of GNNs under heterophily: are we really making progress?

Node classification is a classical graph representation learning task on...

0 Oleg Platonov, et al. ∙

∙ 09/13/2022

Characterizing Graph Datasets for Node Classification: Beyond Homophily-Heterophily Dichotomy

Homophily is a graph property describing the tendency of edges to connec...

0 Oleg Platonov, et al. ∙

∙ 06/11/2022

Gradient Boosting Performs Low-Rank Gaussian Process Inference

This paper shows that gradient boosting based on symmetric decision tree...

0 Aleksei Ustimenko, et al. ∙

∙ 04/04/2022

Which Tricks are Important for Learning to Rank?

Nowadays, state-of-the-art learning-to-rank (LTR) methods are based on g...

0 Ivan Lyzhin, et al. ∙

∙ 01/22/2022

Good Classification Measures and How to Find Them

Several performance measures can be used for evaluating classification r...

0 Martijn Gösgens, et al. ∙

∙ 07/15/2021

Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks

There has been significant research done on developing methods for impro...

0 Andrey Malinin, et al. ∙

∙ 01/21/2021

Boost then Convolve: Gradient Boosting Meets Graph Neural Networks

Graph neural networks (GNNs) are powerful models that have been successf...

0 Sergei Ivanov, et al. ∙

∙ 10/06/2020

Embedding Words in Non-Vector Space with Unsupervised Graph Learning

It has become a de-facto standard to represent words as elements of a ve...

0 Max Ryabinin, et al. ∙

∙ 07/05/2020

Overlaying Spaces and Practical Applicability of Complex Geometries

Recently, non-Euclidean spaces became popular for embedding structured d...

0 Kirill Shevkunov, et al. ∙

∙ 06/18/2020

Uncertainty in Gradient Boosting via Ensembles

Gradient boosting is a powerful machine learning technique that is parti...

0 Aleksei Ustimenko, et al. ∙

∙ 03/04/2020

StochasticRank: Global Optimization of Scale-Free Discrete Functions

In this paper, we introduce a powerful and efficient framework for the d...

1 Aleksei Ustimenko, et al. ∙

∙ 01/20/2020

SGLB: Stochastic Gradient Langevin Boosting

In this paper, we introduce Stochastic Gradient Langevin Boosting (SGLB)...

1 Aleksei Ustimenko, et al. ∙

∙ 11/12/2019

Systematic Analysis of Cluster Similarity Indices: Towards Bias-free Cluster Validation

There are many cluster similarity indices used to evaluate clustering al...

0 Martijn Gösgens, et al. ∙

∙ 07/01/2019

Graph-based Nearest Neighbor Search: From Practice to Theory

Graph-based approaches are empirically shown to be very successful for a...

0 Liudmila Prokhorenkova, et al. ∙

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