2 citations · 2 across the 3 of their papers we have counts for
5 papers
Constrained user-item allocation for e-commerce marketing campaigns
Maja Lindström, Natalija Glisovic, Jan von Pichowski +2
When running marketing campaigns, retailers must decide which products to promote and which users to target. These decisions are inherently coupled: effective campaigns match users…
A Unified Framework for Tabular Generative Modeling: Loss Functions, Benchmarks, and Improved Multi-objective Bayesian Optimization Approaches
Minh H. Vu, Daniel Edler, Carl Wibom +3
Deep learning (DL) models require extensive data to achieve strong performance and generalization. Deep generative models (DGMs) offer a solution by synthesizing data. Yet current…
Survival of the Cheapest: Cost-Aware Hardware Adaptation for Adversarial Robustness
Charles Meyers, Mohammad Reza Saleh Sedghpour, Tommy Löfstedt +1
Deploying adversarially robust machine learning systems requires continuous trade-offs between robustness, cost, and latency. We present an autonomic decision-support framework pro…
Compressing regularized dynamics improves link prediction with the map equation in sparse networks
Maja Lindström, Christopher Blöcker, Tommy Löfstedt +1
Predicting future interactions or novel links in networks is an indispensable tool across diverse domains, including genetic research, online social networks, and recommendation sy…
A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (TRASHFIRE)
Charles Meyers, Mohammad Reza Saleh Sedghpour, Tommy Löfstedt +1
Machine learning models -- deep neural networks in particular -- have performed remarkably well on benchmark datasets across a wide variety of domains. However, the ease of finding…