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Found 14 papers, 9 papers with code
Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines"
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 their findings.
Hierarchical Forecasting at Scale
We implement our sparse hierarchical loss function within an existing forecasting model at bol, a large European e-commerce platform, resulting in an improved forecasting performance of 2% at the product level.
Improving Retrieval-Augmented Large Language Models via Data Importance Learning
There are exponentially many terms in the multilinear extension, and one key contribution of this paper is a polynomial time algorithm that computes exactly, given a retrieval-augmented model with an additive utility function and a validation set, the data importance of data points in the retrieval corpus using the multilinear extension of the model's utility function.
On the Impact of Outlier Bias on User Clicks
We therefore propose an outlier-aware click model that accounts for both outlier and position bias, called outlier-aware position-based model ( OPBM).
Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines
We present DataScope (ease. ml/datascope), the first system that efficiently computes Shapley values of training examples over an end-to-end ML pipeline, and illustrate its applications in data debugging for ML training.
Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and Items
Our results show that our method provides constant update time efficiency with respect to an additional user basket in the incremental case, and linear efficiency in the decremental case where we delete existing baskets.
Understanding and Mitigating the Effect of Outliers in Fair Ranking
We formalize outlierness in a ranking, show that outliers are present in realistic datasets, and present the results of an eye-tracking study, showing that users scanning order and the exposure of items are influenced by the presence of outliers.
Parameter Efficient Deep Probabilistic Forecasting
However, these methods require a large number of parameters to be learned, which imposes high memory requirements on the computational resources for training such models.
Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression
We propose Probabilistic Gradient Boosting Machines (PGBM), a method to create probabilistic predictions with a single ensemble of decision trees in a computationally efficient manner.
Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers
In this paper, we focus on purchase prediction for both anonymous and identified sessions on an e-commerce platform.
A Comparison of Supervised Learning to Match Methods for Product Search
As recent learning to match methods have made important advances in bridging the vocabulary gap for these traditional IR areas, we investigate their potential in the context of product search.
FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions
FairPrep is based on a developer-centered design, and helps data scientists follow best practices in software engineering and machine learning.
Doubly stochastic large scale kernel learning with the empirical kernel map
With the rise of big data sets, the popularity of kernel methods declined and neural networks took over again.
Factorbird - a Parameter Server Approach to Distributed Matrix Factorization
We present Factorbird, a prototype of a parameter server approach for factorizing large matrices with Stochastic Gradient Descent-based algorithms.
