In this talk, we will discuss a new formalization of replicability for batch and reinforcement learning algorithms, and demonstrate how to solve fundamental tasks in learning...

I will discuss an interdisciplinary approach that combines insights from applied machine learning, human-computer interaction, and application domains like music, multimedia...

In this talk, I will present my work on advancing our theoretical understanding of learning and intelligence through the computational perspective.

I discuss how the future of computing could transform our entire living world into a dynamic space for thought with the power of AR and AI.

I will explore this approximation viewpoint applied to various well-established economic concepts, highlighting its power of uncovering the once-impossible possibilities.

In this talk, I will describe my work towards building a new toolbox of optimal statistical primitives, bridging the theory-practice divide.

The talk is motivated by two questions at the interplay between algorithm design and machine learning.

In this talk, a new framework, termed PAC Privacy, will be introduced to “learn" semantic privacy parameters with high-level confidence.

In this talk, I will discuss two such technical challenges and introduce some of my recent work towards their solution.

The NECV brings together researchers in computer vision and related areas for an informal exchange of ideas through a full day of presentations and posters.