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Found 27 papers, 8 papers with code
Robust Stochastic Optimization via Gradient Quantile Clipping
For strongly convex objectives, we prove that the iteration converges to a concentrated distribution and derive high probability bounds on the final estimation error.
Convergence and concentration properties of constant step-size SGD through Markov chains
We consider the optimization of a smooth and strongly convex objective using constant step-size stochastic gradient descent (SGD) and study its properties through the prism of Markov chains.
Robust Methods for High-Dimensional Linear Learning
We propose statistically robust and computationally efficient linear learning methods in the high-dimensional batch setting, where the number of features $d$ may exceed the sample size $n$.
Robust supervised learning with coordinate gradient descent
This paper considers the problem of supervised learning with linear methods when both features and labels can be corrupted, either in the form of heavy tailed data and/or corrupted rows.
WildWood: a new Random Forest algorithm
We introduce WildWood (WW), a new ensemble algorithm for supervised learning of Random Forest (RF) type.
About contrastive unsupervised representation learning for classification and its convergence
Contrastive representation learning has been recently proved to be very efficient for self-supervised training.
An improper estimator with optimal excess risk in misspecified density estimation and logistic regression
On standard examples, this bound scales as $d/n$ with $d$ the model dimension and $n$ the sample size, and critically remains valid under model misspecification.
ZiMM: a deep learning model for long term and blurry relapses with non-clinical claims data
We consider a long-term (18 months) relapse (urination problems still occur despite surgery), which is blurry since it is observed only through the reimbursement of a specific set of drugs for urination problems.
SCALPEL3: a scalable open-source library for healthcare claims databases
SCALPEL-Extraction provides fast concept extraction from a big table such as the one produced by SCALPEL-Flattening.
Distributed, Parallel, and Cluster Computing Computers and Society
AMF: Aggregated Mondrian Forests for Online Learning
Using a variant of the Context Tree Weighting algorithm, we show that it is possible to efficiently perform an exact aggregation over all prunings of the trees; in particular, this enables to obtain a truly online parameter-free algorithm which is competitive with the optimal pruning of the Mondrian tree, and thus adaptive to the unknown regularity of the regression function.
On the optimality of the Hedge algorithm in the stochastic regime
Moreover, our analysis exhibits qualitative differences with other variants of the Hedge algorithm, such as the fixed-horizon version (with constant learning rate) and the one based on the so-called "doubling trick", both of which fail to adapt to the easier stochastic setting.
Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework
We then compare performances of all methods both in terms of risk prediction and variable selection, with a focus on the use of Elastic-Net regularization technique.
Dual optimization for convex constrained objectives without the gradient-Lipschitz assumption
The minimization of convex objectives coming from linear supervised learning problems, such as penalized generalized linear models, can be formulated as finite sums of convex functions.
Minimax optimal rates for Mondrian trees and forests
Our results include consistency and convergence rates for Mondrian Trees and Forests, that turn out to be minimax optimal on the set of $s$-H\"older function with $s \in (0, 1]$ (for trees and forests) and $s \in (1, 2]$ (for forests only), assuming a proper tuning of their complexity parameter in both cases.
ConvSCCS: convolutional self-controlled case series model for lagged adverse event detection
With the increased availability of large databases of electronic health records (EHRs) comes the chance of enhancing health risks screening.
High-dimensional robust regression and outliers detection with SLOPE
Following a recent set of works providing methods for simultaneous robust regression and outliers detection, we consider in this paper a model of linear regression with individual intercepts, in a high-dimensional setting.
Universal consistency and minimax rates for online Mondrian Forests
We establish the consistency of an algorithm of Mondrian Forests, a randomized classification algorithm that can be implemented online.
Sparse inference of the drift of a high-dimensional Ornstein-Uhlenbeck process
As a by-product, we point out the fact that for the Ornstein-Uhlenbeck process, one does not need an assumption of restricted eigenvalue type in order to derive fast rates for the Lasso, while it is well-known to be mandatory for linear regression for instance.
Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling
Tick is a statistical learning library for Python~3, with a particular emphasis on time-dependent models, such as point processes, and tools for generalized linear models and survival analysis.
Binarsity: a penalization for one-hot encoded features in linear supervised learning
In each group of binary features coming from the one-hot encoding of a single raw continuous feature, this penalization uses total-variation regularization together with an extra linear constraint.
C-mix: a high dimensional mixture model for censored durations, with applications to genetic data
We introduce a mixture model for censored durations (C-mix), and develop maximum likelihood inference for the joint estimation of the time distributions and latent regression parameters of the model.
Uncovering Causality from Multivariate Hawkes Integrated Cumulants
We design a new nonparametric method that allows one to estimate the matrix of integrated kernels of a multivariate Hawkes process.
Mean-field inference of Hawkes point processes
We propose a fast and efficient estimation method that is able to accurately recover the parameters of a d-dimensional Hawkes point-process from a set of observations.
SGD with Variance Reduction beyond Empirical Risk Minimization
We introduce a doubly stochastic proximal gradient algorithm for optimizing a finite average of smooth convex functions, whose gradients depend on numerically expensive expectations.
Learning the intensity of time events with change-points
We prove that this leads to a sharp tuning of the convex relaxation of the segmentation prior, by stating oracle inequalities with fast rates of convergence, and consistency for change-points detection.
Sparse and low-rank multivariate Hawkes processes
We consider the problem of unveiling the implicit network structure of node interactions (such as user interactions in a social network), based only on high-frequency timestamps.
Concentration for matrix martingales in continuous time and microscopic activity of social networks
This paper gives new concentration inequalities for the spectral norm of a wide class of matrix martingales in continuous time.
