35 citations · 124 across the 10 of their papers we have counts for
11 papers
Mixture-Based Correction for Position and Trust Bias in Counterfactual Learning to Rank
Ali Vardasbi, Maarten de Rijke, Ilya Markov
In counterfactual learning to rank (CLTR) user interactions are used as a source of supervision. Since user interactions come with bias, an important focus of research in this fiel…
NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization
Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov +3
As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed to perform paralle…
Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank
Ali Vardasbi, Maarten de Rijke, Ilya Markov
Unbiased CLTR requires click propensities to compensate for the difference between user clicks and true relevance of search results via IPS. Current propensity estimation methods a…
Safe Exploration for Optimizing Contextual Bandits
Rolf Jagerman, Ilya Markov, Maarten de Rijke
Contextual bandit problems are a natural fit for many information retrieval tasks, such as learning to rank, text classification, recommendation, etc. However, existing learning me…
NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization
Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov +3
As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed to perform paralle…
ViTOR: Learning to Rank Webpages Based on Visual Features
Bram van den Akker, Ilya Markov, Maarten de Rijke
The visual appearance of a webpage carries valuable information about its quality and can be used to improve the performance of learning to rank (LTR). We introduce the Visual lear…