Modern machine learning models are complex and frequently encode surprising amounts of
information about individual inputs. In extreme cases, complex models appear to memorize …

This chapter gives an overview of proof complexity and connections to SAT solving, focusing
on proof systems such as resolution, Nullstellensatz, polynomial calculus, and cutting planes …

Recent Advances in Multi-Pass Graph Streaming Lower Bounds Page 1 Recent Advances in
Multi-Pass Graph Streaming Lower Bounds Sepehr Assadi Cheriton School of Computer …

Despite the conceptual importance of contextuality in quantum mechanics, there is a hitherto
limited number of applications requiring contextuality but not entanglement. Here, we show …

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

Training and inference with large machine learning models that far exceed the memory
capacity of individual devices necessitates the design of distributed architectures, forcing …

This is a survey of unconditional pseudorandom generators (PRGs). A PRG uses a short,
truly random seed to generate a long," pseudorandom" sequence of bits. To be more …

A hallmark of graph neural networks is their ability to distinguish the isomorphism class of
their inputs. This study derives hardness results for the classification variant of graph …

We prove a lower bound on the space complexity of two-pass semi-streaming algorithms
that approximate the maximum matching problem. The lower bound is parameterized by the …

The growing demands of remote detection and an increasing amount of training data make
distributed machine learning under communication constraints a critical issue. This work …