Search Results for author:
Found 6 papers, 1 papers with code
Fair learning with Wasserstein barycenters for non-decomposable performance measures
In the awareness framework, akin to the classical unconstrained classification case, we show that maximizing accuracy under this fairness constraint is equivalent to solving a corresponding regression problem followed by thresholding at level $1/2$.
The price of unfairness in linear bandits with biased feedback
We also derive gap-dependent upper bounds on the regret, and matching lower bounds for some problem instance. Interestingly, these results reveal a transition between a regime where the problem is as difficult as its unbiased counterpart, and a regime where it can be much harder.
Optimality of variational inference for stochasticblock model with missing links
This provides the first minimax optimal and tractable estimator for the problem of parameter estimation for the stochastic block model with missing links.
Hierarchical transfer learning with applications for electricity load forecasting
The recent abundance of data on electricity consumption at different scales opens new challenges and highlights the need for new techniques to leverage information present at finer scales in order to improve forecasts at wider scales.
Finite Continuum-Armed Bandits
Under natural assumptions on the reward function, we prove that the optimal regret scales as $O(T^{1/3})$ up to poly-logarithmic factors when the budget $T$ is proportional to the number of actions $N$.
Outliers Detection in Networks with Missing Links
The proposed method is statistically sound: we prove that, under fairly general assumptions, our algorithm exactly detects the outliers, and achieves the best known error for the prediction of missing links with polynomial computation cost.
