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 …

The replicability crisis in the social, behavioral, and data sciences has led to the formulation
of algorithm frameworks for replicability---ie, a requirement that an algorithm produce …

We design replicable algorithms in the context of statistical clustering under the recently
introduced notion of replicability from Impagliazzo et al.[2022]. According to this definition, a …

We initiate the mathematical study of replicability as an algorithmic property in the context of
reinforcement learning (RL). We focus on the fundamental setting of discounted tabular …

We investigate replicable learning algorithms. Informally a learning algorithm is replicable if
the algorithm outputs the same canonical hypothesis over multiple runs with high probability …

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

Algorithmic reproducibility measures the deviation in outputs of machine learning algorithms
upon minor changes in the training process. Previous work suggests that first-order methods …

A common explanation for negative user impacts of content recommender systems is
misalignment between the platform's objective and user welfare. In this work, we show that …

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 …

Replicability is essential in science as it allows us to validate and verify research findings.
Impagliazzo, Lei, Pitassi and Sorrell (22) recently initiated the study of replicability in …