Big Data applications are typically associated with systems involving large numbers of
users, massive complex software systems, and large-scale heterogeneous computing and …

Stochastic optimization is an umbrella term that includes over a dozen fragmented
communities, using a patchwork of sometimes overlap** notational systems with …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

Motivated by the task of hyperparameter optimization, we introduce the\em non-stochastic
best-arm identification problem. We identify an attractive algorithm for this setting that makes …

What is reinforcement learning? How is reinforcement learning different from stochastic
optimization? And finally, can it be used for applications to quantitative finance for my current …

The stochastic multi-armed bandit model is a simple abstraction that has proven useful in
many different contexts in statistics and machine learning. Whereas the achievable limit in …

We study the problem of exploration in stochastic Multi-Armed Bandits. Even in the simplest
setting of identifying the best arm, there remains a logarithmic multiplicative gap between the …

We give a complete characterization of the complexity of best-arm identification in one-
parameter bandit problems. We prove a new, tight lower bound on the sample complexity …

The paper proposes a novel upper confidence bound (UCB) procedure for identifying the
arm with the largest mean in a multi-armed bandit game in the fixed confidence setting using …

We learn recurrent neural network optimizers trained on simple synthetic functions by
gradient descent. We show that these learned optimizers exhibit a remarkable degree of …