Abstract: Ultra Large-scale Linear Programming refers to class of problems where number of linear inequality constraints grows exponentially w.r.t. the number of variables.
Abstract: Matroids are combinatorial objects that generalize the notion of linear independence. They have several applications in design and analysis of algorithms.
Abstract: An Algebraic Branching Program (ABP) is a layered graph where each edge is labeled by an affine linear form and the first and the last layer have one vertex each, called the “start” and the “end” vertex respectively.
Abstract: Mathematical proofs when written in conventional ways often contain imprecise definitions, unstated background assumptions, and inferential gaps in reasoning.
Abstract: Smart Contracts handle and transfer assets of considerable value. Thus, it is crucial that their implementation be secure against attacks which aim at stealing or tampering the assets.
Abstract: Anomaly detection is a ubiquitous problem in machine learning. Here one is given a large population of points, we may not have much knowledge about their structure a priori.
Abstract: Directed Causal Graphs (DAGs) capture causal relationships amongst a set of variables and they specify how interventional distributions relate to observational ones.
Abstract: Consider the following basic problem in sparse linear regression -- an algorithm gets labeled samples of the form (x, <w.x> + \eps) where w is an unknown n-imensional vector, x is drawn from a background distributi