3 citations · 3 across the 15 of their papers we have counts for
4 papers · 1 filter
Retraining with Predicted Hard Labels Provably Increases Model Accuracy
Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto +5
The performance of a model trained with noisy labels is often improved by simply \textit{retraining} the model with its \textit{own predicted hard labels} (i.e., 1/0 labels). Yet,…
Optimistic Rates for Learning from Label Proportions
Gene Li, Lin Chen, Adel Javanmard +1
We consider a weakly supervised learning problem called Learning from Label Proportions (LLP), where examples are grouped into ``bags'' and only the average label within each bag i…
PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses
Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni
This work studies algorithms for learning from aggregate responses. We focus on the construction of aggregation sets (called bags in the literature) for event-level loss functions.…
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
Adel Javanmard, Lin Chen, Vahab Mirrokni +2
Due to the rise of privacy concerns, in many practical applications the training data is aggregated before being shared with the learner, in order to protect privacy of users' sens…