Learning mixtures of gaussians using the DDPM objective
Recent works have shown that diffusion models can learn essentially any distribution
provided one can perform score estimation. Yet it remains poorly understood under what …
provided one can perform score estimation. Yet it remains poorly understood under what …
[图书][B] High-dimensional probability: An introduction with applications in data science
R Vershynin - 2018 - books.google.com
High-dimensional probability offers insight into the behavior of random vectors, random
matrices, random subspaces, and objects used to quantify uncertainty in high dimensions …
matrices, random subspaces, and objects used to quantify uncertainty in high dimensions …
Classification with fairness constraints: A meta-algorithm with provable guarantees
Developing classification algorithms that are fair with respect to sensitive attributes of the
data is an important problem due to the increased deployment of classification algorithms in …
data is an important problem due to the increased deployment of classification algorithms in …
Robust estimators in high-dimensions without the computational intractability
We study high-dimensional distribution learning in an agnostic setting where an adversary is
allowed to arbitrarily corrupt an ε-fraction of the samples. Such questions have a rich history …
allowed to arbitrarily corrupt an ε-fraction of the samples. Such questions have a rich history …
[PDF][PDF] Tensor decompositions for learning latent variable models.
This work considers a computationally and statistically efficient parameter estimation method
for a wide class of latent variable models—including Gaussian mixture models, hidden …
for a wide class of latent variable models—including Gaussian mixture models, hidden …
Agnostic estimation of mean and covariance
We consider the problem of estimating the mean and covariance of a distribution from iid
samples in the presence of a fraction of malicious noise. This is in contrast to much recent …
samples in the presence of a fraction of malicious noise. This is in contrast to much recent …
[图书][B] Foundations of data science
A Blum, J Hopcroft, R Kannan - 2020 - books.google.com
This book provides an introduction to the mathematical and algorithmic foundations of data
science, including machine learning, high-dimensional geometry, and analysis of large …
science, including machine learning, high-dimensional geometry, and analysis of large …
Statistical query lower bounds for robust estimation of high-dimensional gaussians and gaussian mixtures
I Diakonikolas, DM Kane… - 2017 IEEE 58th Annual …, 2017 - ieeexplore.ieee.org
We describe a general technique that yields the first Statistical Query lower bounds for a
range of fundamental high-dimensional learning problems involving Gaussian distributions …
range of fundamental high-dimensional learning problems involving Gaussian distributions …
Provable bounds for learning some deep representations
We give algorithms with provable guarantees that learn a class of deep nets in the
generative model view popularized by Hinton and others. Our generative model is an n …
generative model view popularized by Hinton and others. Our generative model is an n …
[PDF][PDF] Multi-objective reinforcement learning using sets of pareto dominating policies
K Van Moffaert, A Nowé - The Journal of Machine Learning Research, 2014 - jmlr.org
Many real-world problems involve the optimization of multiple, possibly conflicting
objectives. Multi-objective reinforcement learning (MORL) is a generalization of standard …
objectives. Multi-objective reinforcement learning (MORL) is a generalization of standard …