41 citations · 135 across the 16 of their papers we have counts for
19 papers
Small sums of roots of unity: polylogarithmic exceptional sets and conditional separation
Qi Cheng
We prove a polylogarithmic exceptional-set estimate for symmetric five-term sums of roots of unity of arbitrary order. More precisely, at most points of the $N…
ERASE -- A Real-World Aligned Benchmark for Unlearning in Recommender Systems
Pierre Lubitzsch, Maarten de Rijke, Sebastian Schelter
Machine unlearning (MU) enables the removal of selected training data from trained models, to address privacy compliance, security, and liability issues in recommender systems. Exi…
Understanding Visual Saliency of Outlier Items in Product Search
Fatemeh Sarvi, Mohammad Aliannejadi, Sebastian Schelter +1
In two-sided marketplaces, items compete for user attention, which translates to revenue for suppliers. Item exposure, indicated by the amount of attention items receive in a ranki…
Messy Code Makes Managing ML Pipelines Difficult? Just Let LLMs Rewrite the Code!
Sebastian Schelter, Stefan Grafberger
Machine learning (ML) applications that learn from data are increasingly used to automate impactful decisions. Unfortunately, these applications often fall short of adequately mana…
Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines"
Stefan Grafberger, Paul Groth, Sebastian Schelter
Data scientists develop ML pipelines in an iterative manner: they repeatedly screen a pipeline for potential issues, debug it, and then revise and improve its code according to the…
Hierarchical Forecasting at Scale
Olivier Sprangers, Wander Wadman, Sebastian Schelter +1
Existing hierarchical forecasting techniques scale poorly when the number of time series increases. We propose to learn a coherent forecast for millions of time series with a singl…
Improving Retrieval-Augmented Large Language Models via Data Importance Learning
Xiaozhong Lyu, Stefan Grafberger, Samantha Biegel +4
Retrieval augmentation enables large language models to take advantage of external knowledge, for example on tasks like question answering and data imputation. However, the perform…
On the Impact of Outlier Bias on User Clicks
Fatemeh Sarvi, Ali Vardasbi, Mohammad Aliannejadi +2
User interaction data is an important source of supervision in counterfactual learning to rank (CLTR). Such data suffers from presentation bias. Much work in unbiased learning to r…
Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines
Bojan Karlaš, David Dao, Matteo Interlandi +4
Developing modern machine learning (ML) applications is data-centric, of which one fundamental challenge is to understand the influence of data quality to ML training -- "Which tra…
Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and Items
Benjamin Longxiang Wang, Sebastian Schelter
Recommender systems play an important role in helping people find information and make decisions in today's increasingly digitalized societies. However, the wide adoption of such m…
Understanding and Mitigating the Effect of Outliers in Fair Ranking
Fatemeh Sarvi, Maria Heuss, Mohammad Aliannejadi +2
Traditional ranking systems are expected to sort items in the order of their relevance and thereby maximize their utility. In fair ranking, utility is complemented with fairness as…
Parameter Efficient Deep Probabilistic Forecasting
Olivier Sprangers, Sebastian Schelter, Maarten de Rijke
Probabilistic time series forecasting is crucial in many application domains such as retail, ecommerce, finance, or biology. With the increasing availability of large volumes of da…
Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression
Olivier Sprangers, Sebastian Schelter, Maarten de Rijke
Gradient Boosting Machines (GBM) are hugely popular for solving tabular data problems. However, practitioners are not only interested in point predictions, but also in probabilisti…
Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers
Mariya Hendriksen, Ernst Kuiper, Pim Nauts +2
The popularity of e-commerce platforms continues to grow. Being able to understand, and predict customer behavior is essential for customizing the user experience through personali…
FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions
Sebastian Schelter, Yuxuan He, Jatin Khilnani +1
The importance of incorporating ethics and legal compliance into machine-assisted decision-making is broadly recognized. Further, several lines of recent work have argued that crit…
'Dark Germany': Hidden Patterns of Participation in Online Far-Right Protests Against Refugee Housing
Sebastian Schelter, Jérôme Kunegis
The political discourse in Western European countries such as Germany has recently seen a resurgence of the topic of refugees, fueled by an influx of refugees from various Middle E…
Doubly stochastic large scale kernel learning with the empirical kernel map
Nikolaas Steenbergen, Sebastian Schelter, Felix Bießmann
With the rise of big data sets, the popularity of kernel methods declined and neural networks took over again. The main problem with kernel methods is that the kernel matrix grows…
On the Ubiquity of Web Tracking: Insights from a Billion-Page Web Crawl
Sebastian Schelter, Jérôme Kunegis
We perform a large-scale analysis of third-party trackers on the World Wide Web from more than 3.5 billion web pages of the CommonCrawl 2012 corpus. We extract a dataset containing…
Factorbird - a Parameter Server Approach to Distributed Matrix Factorization
Sebastian Schelter, Venu Satuluri, Reza Zadeh
We present Factorbird, a prototype of a parameter server approach for factorizing large matrices with Stochastic Gradient Descent-based algorithms. We designed Factorbird to meet t…