Prologue: A machine learning sampler

Prologue: Signal Transduction from an Historical Perspective

Introduction to Machine Learning & Case-Based Reasoning

Prologue These notes refer to the course of Machine Learning (course 395), Computing Department, Imperial College London. The goal of this syllabus is to summarize the basics of machine learning and to provide a detailed explanation of case-based reasoning. This chapter introduces the term " machine learning " and defines what do we mean while using this term. This is a very short summary of th...

Effective Function Recovery for COTS Binaries using Interface Verification

Function recovery is a critical step in many binary analysis and instrumentation tasks. Existing approaches rely on commonly used function prologue patterns to recognize function starts, and possibly epilogues for the ends. However, this approach is not robust when dealing with different compilers, compiler versions, and compilation switches. Although machine learning techniques have been propo...

A Gibbs Sampler for Learning DAGs

We propose a Gibbs sampler for structure learning in directed acyclic graph (DAG) models. The standard Markov chain Monte Carlo algorithms used for learning DAGs are random-walk Metropolis-Hastings samplers. These samplers are guaranteed to converge asymptotically but often mix slowly when exploring the large graph spaces that arise in structure learning. In each step, the sampler we propose dr...

Learning When to Reject an Importance Sample

When observations are incomplete or data are missing, approximate inference methods based on importance sampling are often used. Unfortunately, when the target and proposal distributions are dissimilar, the sampling procedure leads to biased estimates or requires a prohibitive number of samples. Our method approximates a multivariate target distribution by sampling from an existing, sequential ...