We are living in a golden age of machine learning. Powerful models perform many tasks far better than is possible using traditional software engineering approaches alone. However, developing and deploying these models in existing software systems remains challenging. In this paper, we present SmartChoices, a novel approach to incorporating machine learning into mature software stacks easily, safely, and effectively. We highlight key design decisions and present case studies applying SmartChoices within a range of large-scale industrial systems.

A BSTRACT This paper proposes a novel per-task routing method for multi-task applications. Multi-task neural networks can learn to transfer knowledge across different tasks by using parameter sharing. However, sharing parameters between unrelated tasks can hurt performance. To address this issue, we advocate the use of routing networks to learn flexible parameter sharing, where each group of parameters is shared with a different subset of tasks in order to better leverage tasks relatedness. At the same time, it is known that routing networks are notoriously hard to train. We propose the Gumbel-Matrix routing: a novel multi-task routing method, designed to learn fine-grained patterns of parameter sharing. The routing is learned jointly with the model parameters by standard back-propagation thanks to the Gumbel-Softmax trick. When applied to the Omniglot benchmark, the proposed method reduces the state-of-the-art error rate by 17% . 1 I NTRODUCTION Multi-task learning (Caruana, 1998; 1993) based on neural networks has attracted lots of research interest in the past years and has been successfully applied to several application domains, such as recommender systems (Bansal et al., 2016) and real-time object detection (Girshick, 2015). For instance, a movie recommendation system may optimize not only the likelihood of the user clicking on a suggested movie, but also the likelihood that the user is going to watch it. The most common architecture used in practice for multi-task learning is the so-called shared bottom, where the tasks share parameters in the early layers of the model, which are followed by task-specific heads. However, as our experiments on synthetic data show, when the tasks are unrelated, parameter sharing may actually hurt individual tasks performance. Therefore, resorting to flexible parameter sharing becomes very important.

Neural architecture search has been shown to hold great promise towards the automation of deep learning. However in spite of its potential, neural architecture search remains quite costly. To this point, we propose a novel gradient-based framework for efficient architecture search by sharing information across several tasks. We start by training many model architectures on several related (training) tasks. When a new unseen task is presented, the framework performs architecture inference in order to quickly identify a good candidate architecture, before any model is trained on the new task. At the core of our framework lies a deep value network that can predict the performance of input architectures on a task by utilizing task meta-features and the previous model training experiments performed on related tasks. We adopt a continuous parametrization of the model architecture which allows for efficient gradient-based optimization. Given a new task, an effective architecture is quickly identified by maximizing the estimated performance with respect to the model architecture parameters with simple gradient ascent. It is key to point out that our goal is to achieve reasonable performance at the lowest cost. We provide experimental results showing the effectiveness of the framework despite its high computational efficiency.

We present a new anytime algorithm that achieves near-optimal regret for any instance of finite stochastic partial monitoring. In particular, the new algorithm achieves the minimax regret, within logarithmic factors, for both "easy" and "hard" problems. For easy problems, it additionally achieves logarithmic individual regret. Most importantly, the algorithm is adaptive in the sense that if the opponent strategy is in an "easy region" of the strategy space then the regret grows as if the problem was easy. As an implication, we show that under some reasonable additional assumptions, the algorithm enjoys an O(\sqrt{T}) regret in Dynamic Pricing, proven to be hard by Bartok et al. (2011).