Label ranking is a complex prediction task where the goal is to map instances to a total order
over a finite set of predefined labels. An interesting aspect of this problem is that it subsumes …

The computational cost of training with softmax cross entropy loss grows linearly with the
number of classes. For the settings where a large number of classes are involved, a …

We study the problem of episodic reinforcement learning in continuous state-action spaces
with unknown rewards and transitions. Specifically, we consider the setting where the …

We investigate penalized maximum log-likelihood estimation for exponential family
distributions whose natural parameter resides in a reproducing kernel Hilbert space. Key to …

Dual encoder models are ubiquitous in modern classification and retrieval. Crucial for
training such dual encoders is an accurate estimation of gradients from the partition function …

In dense retrieval, deep encoders provide embeddings for both inputs and targets, and the
softmax function is used to parameterize a distribution over a large number of candidate …

A key challenge in contrastive learning is to generate negative samples from a large sample
set to contrast with positive samples, for learning better encoding of the data. These negative …

We consider a class of structured prediction problems for which the assumptions made by
state-of-the-art algorithms fail. To deal with exponentially sized output sets, these algorithms …

Statistical computational learning is the branch of Machine Learning that defines and
analyzes the performance of learning algorithms using two metrics: sample complexity and …

We consider an active search problem in intensionally specified structured spaces. The
ultimate goal in this setting is to discover structures from structurally different partitions of a …