961 citations
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8 papers · 1 filter
Unbiased Experiments in Congested Networks
Bruce Spang, Veronica Hannan, Shravya Kunamalla +3
When developing a new networking algorithm, it is established practice to run a randomized experiment, or A/B test, to evaluate its performance. In an A/B test, traffic is randomly…
One-step ahead sequential Super Learning from short times series of many slightly dependent data, and anticipating the cost of natural disasters
Geoffrey Ecoto, Aurélien Bibaut, Antoine Chambaz
Suppose that we observe a short time series where each time-t-specific data-structure consists of many slightly dependent data indexed by a and that we want to estimate a feature o…
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning
Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou +2
Empirical risk minimization (ERM) is the workhorse of machine learning, whether for classification and regression or for off-policy policy learning, but its model-agnostic guarante…
Post-Contextual-Bandit Inference
Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou +2
Contextual bandit algorithms are increasingly replacing non-adaptive A/B tests in e-commerce, healthcare, and policymaking because they can both improve outcomes for study particip…
VMAF-based Bitrate Ladder Estimation for Adaptive Streaming
Angeliki V. Katsenou, Fan Zhang, Kyle Swanson +3
In HTTP Adaptive Streaming, video content is conventionally encoded by adapting its spatial resolution and quantization level to best match the prevailing network state and display…
Enhancing VMAF through New Feature Integration and Model Combination
Fan Zhang, Angeliki Katsenou, Christos Bampis +3
VMAF is a machine learning based video quality assessment method, originally designed for streaming applications, which combines multiple quality metrics and video features through…