Active Learning for Accurate Estimation of Linear Models
By Carlos Riquelme, Mohammad Ghavamzadeh, Alessandro Lazaric. 2017
11 citations recorded by the source corpus.
We explore the sequential decision-making problem where the goal is to estimate a number of linear models uniformly well, given a shared budget of random contexts independently sampled from a known distribution. For each incoming context, the decision-maker selects one of the linear models and receives an observation that is corrupted by the unknown noise level of that model. We present Trace-UCB, an adaptive allocation algorithm that learns the models' noise levels while balancing contexts accordingly across them, and prove bounds for its simple regret in both expectation and high-probability. We extend the algorithm and its bounds to the high dimensional setting, where the number of linear models times the dimension of the contexts is more than the total budget of samples. Simulations with real data suggest that Trace-UCB is remarkably robust, outperforming a number of baselines even when its assumptions are violated.
Subfields: Artificial Intelligence, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- George Tucker
- Jasper Snoek
- Carlos Riquelme
- Sergey Levine
- Frank Hutter
- Ruslan Salakhutdinov
- Quoc V. Le
- Chelsea Finn
In Management Science and Operations Research
- Jasper Snoek
- George Tucker
- Carlos Riquelme
- Sergey Levine
- Shixiang Shane Gu
- Ofir Nachum
- Aviral Kumar
- Carl E. Rasmussen
Citations in the corpus
- Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling (2018, methodology): “Following Riquelme et al. (2017), we take d = 32 of the ratings as the context of the user, and k = 8 as the arms.”