When two different parties use the same learning rule on their own data, how can we test
whether the distributions of the two outcomes are similar? In this paper, we study the …

We provide efficient replicable algorithms for the problem of learning large-margin
halfspaces. Our results improve upon the algorithms provided by Impagliazzo, Lei, Pitassi …

Different from fine-tuning models pre-trained on a large-scale dataset of preset classes,
class-incremental learning (CIL) aims to recognize novel classes over time without forgetting …

We study the fundamental problem of transfer learning where a learning algorithm collects
data from some source distribution $ P $ but needs to perform well with respect to a different …

Learning fine-grained embeddings from coarse labels is a challenging task due to limited
label granularity supervision, ie, lacking the detailed distinctions required for fine-grained …

We study computational aspects of algorithmic replicability, a notion of stability introduced by
Impagliazzo, Lei, Pitassi, and Sorrell [2022]. Motivated by a recent line of work that …

In this work, we study how to efficiently obtain perfect samples from a discrete distribution
$\mathcal {D} $ given access only to pairwise comparisons of elements of its support …

We consider the problem of estimating the parameters of a Markov Random Field with hard-
constraints using a single sample. As our main running examples, we use the k-SAT and the …

As research interests in medical image analysis become increasingly fine-grained, the cost
for extensive annotation also rises. One feasible way to reduce the cost is to annotate with …

The workhorse of machine learning is stochastic gradient descent. To access stochastic
gradients, it is common to consider iteratively input/output pairs of a training dataset …