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Found 9 papers, 3 papers with code
Online Dynamic Acknowledgement with Learned Predictions
This elegant model studies the trade-off between acknowledgement cost and waiting experienced by requests.
Online Learning and Bandits with Queried Hints
For stochastic MAB, we also consider a stronger model where a probe reveals the reward values of the probed arms, and show that in this case, $k=3$ probes suffice to achieve parameter-independent constant regret, $O(n^2)$.
Algorithms with Prediction Portfolios
For each of these problems we introduce new algorithms that take advantage of multiple predictors, and prove bounds on the resulting performance.
Parsimonious Learning-Augmented Caching
Learning-augmented algorithms -- in which, traditional algorithms are augmented with machine-learned predictions -- have emerged as a framework to go beyond worst-case analysis.
Online Knapsack with Frequency Predictions
There has been recent interest in using machine-learned predictions to improve the worst-case guarantees of online algorithms.
Faster Matchings via Learned Duals
Second, once the duals are feasible, they may not be optimal, so we show that they can be used to quickly find an optimal solution.
A Relational Gradient Descent Algorithm For Support Vector Machine Training
We consider gradient descent like algorithms for Support Vector Machine (SVM) training when the data is in relational form.
Approximate Aggregate Queries Under Additive Inequalities
In contrast, we show that the situation with two additive inequalities is quite different, by showing that it is NP-hard to evaluate simple aggregation queries, with two additive inequalities, with any bounded relative error.
On Coresets for Regularized Loss Minimization
Our main result is that if the regularizer's effect does not become negligible as the norm of the hypothesis scales, and as the data scales, then a uniform sample of modest size is with high probability a coreset.
