Algorithms
Time discretization invariance in Machine Learning, applications to reinforcement learning and recurrent neural networks
While computers are well equipped to deal with discrete flows of data, the real world often provides intrinsically continuous time data sequences, e.g. visual, sensory streams, time series, or state variables in continuous control environments.…
Reproducible Codes and Cryptographic Applications
In this talk I will present a work in progress on structured linear block codes. The investigation starts from well-known examples and generalizes them to a wide class of codes that we call reproducible codes.…
Sequential Estimation of Quantiles with Applications to A/B-testing and Best-arm Identification
Consider the problem of sequentially estimating quantiles of any distribution over a complete, fully-ordered set, based on a stream of i.i.d. observations. We propose new, theoretically sound and practically tight confidence sequences for quantiles, that…
Fireside Chat with Peter Stone
For autonomous robots to operate in the open, dynamically changing world, they will need to be able to learn a robust set of skills from relatively little experience. This talk begins by introducing Grounded Simulation…
Algorithmic Improvisation for Dependable and Secure Autonomy
Algorithmic Improvisation, also called control improvisation, is a new framework for automatically synthesizing systems with random but controllable behavior. In this talk, I will present the theory of algorithmic improvisation and show how it can…
