Operational forecasting often requires predicting collections of related, multivariate time
series data that are high-dimensional in nature. This can be tackled by fitting a single …

An agent that efficiently accumulates knowledge to develop increasingly sophisticated skills
over a long lifetime could advance the frontier of artificial intelligence capabilities. The …

Thompson sampling has proven effective across a wide range of stationary bandit
environments. However, as we demonstrate in this paper, it can perform poorly when …

Real-world applications of contextual bandits often exhibit non-stationarity due to
seasonality, serendipity, and evolving social trends. While a number of non-stationary …

Despite the subject of non-stationary bandit learning having attracted much recent attention,
we have yet to identify a formal definition of non-stationarity that can consistently distinguish …

The trade-off between the cost of acquiring and processing data, and uncertainty due to a
lack of data is fundamental in machine learning. A basic instance of this trade-off is the …

Finding an optimal solution is computationally hard for most combinatorial optimization
problems. Therefore the use of heuristics methods aims at finding, if not optimal, near …

Wireless devices are often able to communicate on several alternative channels; for
example, cellular phones may use several frequency bands and are equipped with base …

Current recommender systems predominantly employ supervised learning algorithms, which
often fail to optimize for long-term user engagement. This short-sighted approach highlights …

Wireless devices are often able to communicate on several alternative channels; for
example, cellular phones may use several frequency bands and are equipped with base …