Search Results for author:
Found 24 papers, 7 papers with code
Optimal detection of sparse principal components in high dimension
We perform a finite sample analysis of the detection levels for sparse principal components of a high-dimensional covariance matrix.
Computational Lower Bounds for Sparse PCA
In the context of sparse principal component detection, we bring evidence towards the existence of a statistical price to pay for computational efficiency.
Statistical and computational trade-offs in estimation of sparse principal components
In this paper, we show that, under a widely-believed assumption from computational complexity theory, there is a fundamental trade-off between statistical and computational performance in this problem.
Detection of Planted Solutions for Flat Satisfiability Problems
We describe the properties of random instances of flat satisfiability, as well of the optimal rates of detection of the associated hypothesis testing problem.
Average-case Hardness of RIP Certification
The restricted isometry property (RIP) for design matrices gives guarantees for optimal recovery in sparse linear models.
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe
We consider the problem of bandit optimization, inspired by stochastic optimization and online learning problems with bandit feedback.
Optimal link prediction with matrix logistic regression
We consider the problem of link prediction, based on partial observation of a large network, and on side information associated to its vertices.
Unsupervised Alignment of Embeddings with Wasserstein Procrustes
A library for Multilingual Unsupervised or Supervised word Embeddings
Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain
We study the problem of hypothesis testing between two discrete distributions, where we only have access to samples after the action of a known reversible Markov chain, playing the role of noise.
Regularized Contextual Bandits
We consider the stochastic contextual bandit problem with additional regularization.
Learning with Differentiable Perturbed Optimizers
Machine learning pipelines often rely on optimization procedures to make discrete decisions (e. g., sorting, picking closest neighbors, or shortest paths).
Stochastic Optimization for Regularized Wasserstein Estimators
We show that this algorithm can be extended to other tasks, including estimation of Wasserstein barycenters.
Fast Differentiable Sorting and Ranking
While numerous works have proposed differentiable proxies to sorting and ranking, they do not achieve the $O(n \log n)$ time complexity one would expect from sorting and ranking operations.
Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design
Our goal in this paper is to propose new group testing algorithms that can operate in a noisy setting (tests can be mistaken) to decide adaptively (looking at past results) which groups to test next, with the goal to converge to a good detection, as quickly, and with as few tests as possible.
Learning with Differentiable Pertubed Optimizers
Machine learning pipelines often rely on optimizers procedures to make discrete decisions (e. g., sorting, picking closest neighbors, or shortest paths).
Shuffle to Learn: Self-supervised learning from permutations via differentiable ranking
In particular, we also improve music understanding by reordering spectrogram patches in the frequency space, as well as video classification by reordering frames along the time axis.
Self-Supervised Learning of Audio Representations from Permutations with Differentiable Ranking
Second, we show that inverting permutations is a meaningful pretext task for learning audio representations in an unsupervised fashion.
Efficient and Modular Implicit Differentiation
In this paper, we propose automatic implicit differentiation, an efficient and modular approach for implicit differentiation of optimization problems.
A unified software/hardware scalable architecture for brain-inspired computing based on self-organizing neural models
This work also demonstrates the distributed and scalable nature of the model through both simulation results and hardware execution on a dedicated FPGA-based platform named SCALP (Self-configurable 3D Cellular Adaptive Platform).
Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization
We consider the problem of minimizing the sum of two convex functions.
Regression as Classification: Influence of Task Formulation on Neural Network Features
Neural networks can be trained to solve regression problems by using gradient-based methods to minimize the square loss.
Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes
Optimal transport is an important tool in machine learning, allowing to capture geometric properties of the data through a linear program on transport polytopes.
Decoding-time Realignment of Language Models
Aligning language models with human preferences is crucial for reducing errors and biases in these models.
Implicit Diffusion: Efficient Optimization through Stochastic Sampling
We present a new algorithm to optimize distributions defined implicitly by parameterized stochastic diffusions.
