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Found 11 papers, 3 papers with code
Estimating the history of a random recursive tree
This paper studies the problem of estimating the order of arrival of the vertices in a random recursive tree.
Parameter-free projected gradient descent
We consider the problem of minimizing a convex function over a closed convex set, with Projected Gradient Descent (PGD).
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.
Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit
For two-layer neural networks, it has been understood via these asymptotics that the nature of the trained model radically changes depending on the scale of the initial random weights, ranging from a kernel regime (for large initial variance) to a feature learning regime (for small initial variance).
Localization in 1D non-parametric latent space models from pairwise affinities
We consider the problem of estimating latent positions in a one-dimensional torus from pairwise affinities.
A Unified Approach to Fair Online Learning via Blackwell Approachability
We provide a setting and a general approach to fair online learning with stochastic sensitive and non-sensitive contexts.
Pair-Matching: Links Prediction with Adaptive Queries
This paper shows that sublinear regret is achievable in the case where the graph is generated according to a Stochastic Block Model (SBM) with two communities.
Partial recovery bounds for clustering with the relaxed $K$means
We investigate the clustering performances of the relaxed $K$means in the setting of sub-Gaussian Mixture Model (sGMM) and Stochastic Block Model (SBM).
PECOK: a convex optimization approach to variable clustering
The problem of variable clustering is that of grouping similar components of a $p$-dimensional vector $X=(X_{1},\ldots, X_{p})$, and estimating these groups from $n$ independent copies of $X$.
Statistics Theory Statistics Theory
Model Assisted Variable Clustering: Minimax-optimal Recovery and Algorithms
We quantify the difficulty of clustering data generated from a G-block covariance model in terms of cluster proximity, measured with respect to two related, but different, cluster separation metrics.
Aggregation of predictors for nonstationary sub-linear processes and online adaptive forecasting of time varying autoregressive processes
It is obtained by aggregating a finite number of well chosen predictors, each of them enjoying an optimal minimax convergence rate under specific smoothness conditions on the TVAR coefficients.
