12 citations · 22 across the 9 of their papers we have counts for
9 papers
Constrained Sampling with Primal-Dual Langevin Monte Carlo
Luiz F. O. Chamon, Mohammad Reza Karimi, Anna Korba
This work considers the problem of sampling from a probability distribution known up to a normalization constant while satisfying a set of statistical constraints specified by the…
Enhancing Language Learning through Technology: Introducing a New English-Azerbaijani (Arabic Script) Parallel Corpus
Jalil Nourmohammadi Khiarak, Ammar Ahmadi, Taher Ak-bari Saeed +7
This paper introduces a pioneering English-Azerbaijani (Arabic Script) parallel corpus, designed to bridge the technological gap in language learning and machine translation (MT) f…
Sinkhorn Flow: A Continuous-Time Framework for Understanding and Generalizing the Sinkhorn Algorithm
Mohammad Reza Karimi, Ya-Ping Hsieh, Andreas Krause
Many problems in machine learning can be formulated as solving entropy-regularized optimal transport on the space of probability measures. The canonical approach involves the Sinkh…
Riemannian stochastic optimization methods avoid strict saddle points
Ya-Ping Hsieh, Mohammad Reza Karimi, Andreas Krause +1
Many modern machine learning applications - from online principal component analysis to covariance matrix identification and dictionary learning - can be formulated as minimization…
Isotropic Gaussian Processes on Finite Spaces of Graphs
Viacheslav Borovitskiy, Mohammad Reza Karimi, Vignesh Ram Somnath +1
We propose a principled way to define Gaussian process priors on various sets of unweighted graphs: directed or undirected, with or without loops. We endow each of these sets with…
A Dynamical System View of Langevin-Based Non-Convex Sampling
Mohammad Reza Karimi, Ya-Ping Hsieh, Andreas Krause
Non-convex sampling is a key challenge in machine learning, central to non-convex optimization in deep learning as well as to approximate probabilistic inference. Despite its signi…
Riemannian stochastic approximation algorithms
Mohammad Reza Karimi, Ya-Ping Hsieh, Panayotis Mertikopoulos +1
We examine a wide class of stochastic approximation algorithms for solving (stochastic) nonlinear problems on Riemannian manifolds. Such algorithms arise naturally in the study of…
Online Active Model Selection for Pre-trained Classifiers
Mohammad Reza Karimi, Nezihe Merve Gürel, Bojan Karlaš +3
Given pre-trained classifiers and a stream of unlabeled data examples, how can we actively decide when to query a label so that we can distinguish the best model from the rest…
Stochastic Submodular Maximization: The Case of Coverage Functions
Mohammad Reza Karimi, Mario Lucic, Hamed Hassani +1
Stochastic optimization of continuous objectives is at the heart of modern machine learning. However, many important problems are of discrete nature and often involve submodular ob…