# Barrier Certificates for Assured Machine Teaching

@article{Ahmadi2019BarrierCF, title={Barrier Certificates for Assured Machine Teaching}, author={Mohamadreza Ahmadi and B. Wu and Yuxin Chen and Yisong Yue and Ufuk Topcu}, journal={2019 American Control Conference (ACC)}, year={2019}, pages={3658-3663} }

Machine teaching can be viewed as optimal control for learning. Given a learner's model, machine teaching aims to determine the optimal training data to steer the learner towards a target hypothesis. In this paper, we are interested in providing assurances for machine teaching algorithms using control theory. In particular, we study a well-established learner's model in the machine teaching literature that is captured by the local preference over a version space. We interpret the problem of…

## 4 Citations

### Safe Policy Synthesis in Multi-Agent POMDPs via Discrete-Time Barrier Functions

- Computer Science, Mathematics2019 IEEE 58th Conference on Decision and Control (CDC)
- 2019

This paper uses barrier functions to design policies for MPOMDPs that ensure safety and forms sufficient and necessary conditions for the safety of a given set based on discrete-time barrier functions (DTBFs) and demonstrates that the formulation also allows for Boolean compositions of DTBFs for representing more complicated safe sets.

### Control Theory Meets POMDPs: A Hybrid Systems Approach

- Computer ScienceIEEE Transactions on Automatic Control
- 2021

A barrier certificate theorem is formulated, wherein it is shown that if there exists a barrier certificate satisfying a set of inequalities along the solutions to the belief update equation of the POMDP, the safety and performance properties are guaranteed to hold.

### Cost-Bounded Active Classification Using Partially Observable Markov Decision Processes

- Computer Science2019 American Control Conference (ACC)
- 2019

This work presents a decision-theoretic framework based on partially observable Markov decision processes (POMDPs) that relies on assigning a classification belief to each candidate MDP model, and designs POMDP strategies leading to classification decisions.

### Locality Sensitive Teaching

- Computer ScienceNeurIPS
- 2021

A novel teaching framework, Locality Sensitive Teaching (LST), based on locality sensitive sampling, which has provable near-constant time complexity, which is exponentially better than the existing baseline and is readily applicable in real-world education scenarios.

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