In the field of optimal transport, two prominent subfields face each oth...
One of the most popular ML algorithms, AdaBoost, can be derived from the...
We study the phenomenon of in-context learning (ICL) exhibited by
large ...
In this work, we propose a novel approach for layerwise representation
l...
We present the surprising result that randomly initialized neural networ...
Optimizers like Adam and AdaGrad have been very successful in training
l...
We study a local loss construction approach for optimizing neural networ...
Many learning tasks in machine learning can be viewed as taking a gradie...
Conducting pairwise comparisons is a widely used approach in curating hu...
It was conjectured that any neural network of any structure and arbitrar...
Continuous-time mirror descent (CMD) can be seen as the limit case of th...
We introduce "TriMap"; a dimensionality reduction technique based on
tri...
We shed new insights on the two commonly used updates for the online k-P...
In linear regression we wish to estimate the optimum linear least square...
We introduce a temperature into the exponential function and replace the...
We consider online learning with linear models, where the algorithm pred...
Expectation-Maximization (EM) is the fallback method for parameter estim...
In experimental design, we are given a large collection of vectors, each...
We examine connections between combinatorial notions that arise in machi...
Consider linear regression where the examples are generated by an unknow...
We study the following basic machine learning task: Given a fixed set of...
We consider the online path learning problem in a graph with non-additiv...
The problem of identifying voice commands has always been a challenge du...
We first show that the commonly used dimensionality reduction (DR) metho...
The n × d design matrix in a linear regression problem is given, but
the...
We develop a variant of multiclass logistic regression that achieves thr...
We describe a new method called t-ETE for finding a low-dimensional embe...
Most of machine learning deals with vector parameters. Ideally we would ...
One of the main concepts in quantum physics is a density matrix, which i...
We consider on-line density estimation with a parameterized density from...