Helix 
Machine learning workflow development is a process of trial-and-error: developers iterate on workflows by testing out small modifications until the desired accuracy is achieved. Unfortunately, existing machine learning systems focus narrowly on model training—a small fraction of the overall development time—and neglect to address iterative development. We propose Helix, a machine learning system that optimizes the execution across iterations—intelligently caching and reusing, or recomputing intermediates as appropriate. Helix captures a wide variety of application needs within its Scala DSL, with succinct syntax defining unified processes for data preprocessing, model specification, and learning. We demonstrate that the reuse problem can be cast as a Max-Flow problem, while the caching problem is NP-Hard. We develop effective lightweight heuristics for the latter. Empirical evaluation shows that Helix is not only able to handle a wide variety of use cases in one unified workflow but also much faster, providing run time reductions of up to 19x over state-of-the-art systems, such as DeepDive or KeystoneML, on four real-world applications in natural language processing, computer vision, social and natural sciences. …
Action-Elimination Deep Q-Network (AE-DQN)
Learning how to act when there are many available actions in each state is a challenging task for Reinforcement Learning (RL) agents, especially when many of the actions are redundant or irrelevant. In such cases, it is sometimes easier to learn which actions not to take. In this work, we propose the Action-Elimination Deep Q-Network (AE-DQN) architecture that combines a Deep RL algorithm with an Action Elimination Network (AEN) that eliminates sub-optimal actions. The AEN is trained to predict invalid actions, supervised by an external elimination signal provided by the environment. Simulations demonstrate a considerable speedup and added robustness over vanilla DQN in text-based games with over a thousand discrete actions. …
Accelerated Gradient Descent (AGD)
In the world of optimization, we have a space and a convex objective function f we wish to minimize. We have seen that gradient descent is a simple greedy algorithm that works to minimize the objective function at some convergence rate (in this post we shall remain in discrete time). But the world is always stranger than we think. Indeed, there is a phenomenon of acceleration in convex optimization, in which we can boost the performance of some gradient-based algorithms by subtly modifying their implementation. In particular, we will discuss accelerated gradient descent, proposed by Yurii Nesterov in 1983, which achieves a faster – and optimal – convergence rate under the same assumption as gradient descent. Acceleration has received renewed research interests in recent years, leading to many proposed interpretations and further generalizations. Nevertheless, there is still a sense of mystery to what acceleration is doing and why it works; these are the questions that we want to understand better. …
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