We propose a theoretical framework for studying behavior cloning of complex expert
demonstrations using generative modeling. Our framework invokes low-level controllers …

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 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 …

Finding a good approximation of the top eigenvector of a given dxd matrix A is a basic and
important computational problem, with many applications. We give two different quantum …

There is a growing concern that generative AI models will generate outputs closely
resembling the copyrighted materials for which they are trained. This worry has intensified …

The notion of replicable algorithms was introduced by Impagliazzo, Lei, Pitassi, and Sorrell
(STOC'22) to describe randomized algorithms that are stable under the resampling of their …

The challenge of producing accurate statistics while respecting the privacy of the individuals
in a sample is an important area of research. We study minimax lower bounds for classes of …

We propose a theoretical framework for studying the imitation of stochastic, non-Markovian,
potentially multi-modal (ie" complex") expert demonstrations in nonlinear dynamical …

We introduce a fundamental lemma called the Poisson matching lemma, and apply it to
prove one-shot achievability results for various settings, namely channels with state …

One-shot channel simulation (or channel synthesis) has seen increasing applications in
lossy compression, differential privacy and machine learning. In this setting, an encoder …