Given only a single picture, people are capable of inferring a mental representation that encodes rich information about the underlying 3D scene. We acquire this skill not through massive labeled datasets of 3D scenes, but through self-supervised observation and interaction.
Most cryptographic protocols in use today are based on number theoretic problems such as integer factoring.
Discrete combinatorial structures such as graphs and Boolean matrices are prevalent in modern computation. The massive size of modern data motivates the design of efficient algorithms for processing these combinatorial datasets.
Solving non-convex optimization problems on probabilistic models of inputs lies at the heart of foundational algorithmic challenges arising in high-dimensional statistical data analysis, beyond-worst-case combinatorial optimization, cryptography, and statistical physics.
From single-box databases, data systems are evolving into multi-tenant compute and storage platforms that host not only structured data analytics but also AI workloads and AI-enhanced system components.
Last decade's deep learning revolution in part began in the area of computer vision. The intrinsic complexity of visual perception problems urged the community to explore effective methods for learning abstractions from data.
Understanding the implications underlying a text is critical to assessing its impact.
Autonomous exploration and data-efficient learning are important ingredients for helping machine learning handle the complexity and variety of real-world interactions.
Typically, reinforcement learning (RL) methods rely on trial-and-error interaction with the environment from scratch to discover effective behaviors.
Machine learning algorithms are widely used for decision-making in societally high-stakes settings from child welfare and criminal justice to healthcare and consumer lending. Recent history has illuminated numerous examples where these algorithms proved unreliable or inequitable.