Article List

We present novel bounds for estimating discrete probability distributions under the $\ell_\infty$ norm. These are nearly optimal in various precise se...

As machine learning models continue to grow more complex, poor calibration significantly limits the reliability of their predictions. Temperature scal...

We propose a new density estimation algorithm. Given $n$ i.i.d. observations from a distribution belonging to a class of densities on $\mathbb{R}^d$,...

We consider the probabilistic simplex-constrained sparse recovery problem. The commonly used Lasso-type penalty for promoting sparsity is ineffective...

Diffusion models have recently emerged as a powerful framework for generative modeling. They consist of a forward process that perturbs input data wit...

We study the variational inference problem of minimizing a regularized Rényi divergence over an exponential family. We propose to solve this problem w...

Word embeddings, which are a mapping of words into continuous vectors, are widely used in modern Natural Language Processing (NLP) systems. However, t...

Hidden Markov models (HMMs) are flexible tools for clustering dependent data coming from unknown populations, allowing nonparametric modelling of the...

Temporal point process (TPP) is an important tool for modeling and predicting irregularly timed events across various domains. Recently, the recurrent...

We study the classification problem under the two-component anisotropic sub-Gaussian mixture model in high dimensions and in the non-asymptotic settin...

Recently, several universal methods have been proposed for online convex optimization, and attain minimax rates for multiple types of convex functions...

We provide the first known algorithm that provably achieves $\varepsilon$-optimality within $\widetilde{O}(1/\varepsilon)$ function evaluations for th...

We study the often overlooked phenomenon, first noted in Breiman (2001), that random forests appear to reduce bias compared to bagging. Motivated by a...

We introduce skglm, an open-source Python package for regularized Generalized Linear Models. Thanks to its composable nature, it supports combining da...

The training of modern machine learning models often consists in solving high-dimensional non-convex optimisation problems that are subject to large-s...

Network data are often sampled with auxiliary information or collected through the observation of a complex system over time, leading to multiple netw...

We develop an approach for Bayesian learning of spatiotemporal dynamical mechanistic models. Such learning consists of statistical emulation of the me...

Symmetries (transformations by group actions) are present in many datasets, and leveraging them holds considerable promise for improving predictions i...