Meta-learning, or learning-to-learn, seeks to design algorithms that can utilize previous experience to rapidly learn new skills or adapt to new environments. Representation learning---a key tool for performing meta-learning---learns a data representation that can transfer knowledge across multiple tasks, which is essential in regimes where data is scarce. Despite a recent surge of interest in the practice of meta-learning, the theoretical underpinnings of meta-learning algorithms are lacking, especially in the context of learning transferable representations. In this paper, we focus on the problem of multi-task linear regression---in which multiple linear regression models share a common, low-dimensional linear representation. Here, we provide provably fast, sample-efficient algorithms to address the dual challenges of (1) learning a common set of features from multiple, related tasks, and (2) transferring this knowledge to new, unseen tasks. Both are central to the general problem of meta-learning. Finally, we complement these results by providing information-theoretic lower bounds on the sample complexity of learning these linear features, showing that our algorithms are optimal up to logarithmic factors.

Waverly Labs' Ambassador, an over-the-ear translation device, can support up to 20 languages and 42 dialects. The greatest obstacle to international understanding is the barrier of language," wrote British scholar and author Christopher Dawson in November 1957, believing that relying on live, human translators to accurately capture and reflect a speaker's meaning, inflection, and emotion was too great of a challenge to overcome. More than 60 years later, Dawson's theory may finally be proven outdated, thanks to the development of powerful, portable real-time translation devices. The convergence of natural language processing technology, machine learning algorithms, and powerful portable chipsets has led to the development of new devices and applications that allow real-time, two-way translation of speech and text. Language translation devices are capable of listening to an audio source in one language, translating what is being said into another language, and then translating a ...

Artificial intelligence offers Britain the opportunity to have a world-leading school system. We have a good education system, but it is not innovative nor exciting. Nor is it in tune with the post-Brexit world. AI is transforming every aspect of the human experience. Britain is making considerable progress in applying it to healthcare, to the professions and to industry, but despite good progress schooling remains the Cinderella of AI.

This paper considers if a reward function learned via inverse reinforcement from a human expert can be used as a feedback intervention to alter future human performance as desired (i.e., human to human apprenticeship learning). To learn reward functions two new algorithms are developed: a kernel-based inverse reinforcement learning algorithm and a Monte Carlo reinforcement learning algorithm. The algorithms are benchmarked against well-known alternatives within their respective corpus and are shown to outperform in terms of efficiency and optimality. To test the feedback intervention two randomized experiments are performed with 3,256 human participants. The experimental results demonstrate with significance that the rewards learned from "expert" individuals are effective as feedback interventions. In addition to the algorithmic contributions and successful experiments, the paper also describes three reward function modifications to improve reward function feedback interventions for humans.

While robot learning has demonstrated promising results for enabling robots to automatically acquire new skills, a critical challenge in deploying learning-based systems is scale: acquiring enough data for the robot to effectively generalize broadly. Imitation learning, in particular, has remained a stable and powerful approach for robot learning, but critically relies on expert operators for data collection. In this work, we target this challenge, aiming to build an imitation learning system that can continuously improve through autonomous data collection, while simultaneously avoiding the explicit use of reinforcement learning, to maintain the stability, simplicity, and scalability of supervised imitation. To accomplish this, we cast the problem of imitation with autonomous improvement into a multi-task setting. We utilize the insight that, in a multi-task setting, a failed attempt at one task might represent a successful attempt at another task. This allows us to leverage the robot's own trials as demonstrations for tasks other than the one that the robot actually attempted. Using an initial dataset of multi-task demonstration data, the robot autonomously collects trials which are only sparsely labeled with a binary indication of whether the trial accomplished any useful task or not. We then embed the trials into a learned latent space of tasks, trained using only the initial demonstration dataset, to draw similarities between various trials, enabling the robot to achieve one-shot generalization to new tasks. In contrast to prior imitation learning approaches, our method can autonomously collect data with sparse supervision for continuous improvement, and in contrast to reinforcement learning algorithms, our method can effectively improve from sparse, task-agnostic reward signals.

Metric learning has been successful in learning new metrics adapted to numerical datasets. However, its development on categorical data still needs further exploration. In this paper, we propose a method, called CPML for \emph{categorical projected metric learning}, that tries to efficiently~(i.e. less computational time and better prediction accuracy) address the problem of metric learning in categorical data. We make use of the Value Distance Metric to represent our data and propose new distances based on this representation. We then show how to efficiently learn new metrics. We also generalize several previous regularizers through the Schatten $p$-norm and provides a generalization bound for it that complements the standard generalization bound for metric learning. Experimental results show that our method provides

We consider the problem of meta-learning, or learning-to-learn [Thrun and Pratt, 1998], in which the goal is to use the data from numerous training tasks to reduce the sample complexity of an unseen but related test task. Although there is a long history of successful methods in meta-learning and the related areas of multi-task and lifelong learning [Evgeniou and Pontil, 2004, Ruvolo and Eaton, 2013], recent approaches have been developed with the diversity and scale of modern applications in mind. This has given rise to simple, model-agnostic methods that focus on learning a good initialization for some gradient-based method such as stochastic gradient descent (SGD), to be run on samples from a new task [Finn et al., 2017, Nichol et al., 2018]. These methods have found widespread applications in a variety of areas such as computer vision [Nichol et al., 2018], reinforcement learning [Finn et al., 2017], and federated learning [McMahan et al., 2017]. Inspired by their popularity, several recent learning-theoretic analyses of meta-learning have followed suit, eschewing customization to specific hypothesis classes such as halfspaces [Maurer and Pontil, 2013, Balcan et al., 2015] and instead favoring the convex-case study of gradient-based algorithms that could potentially be applied to deep neural networks [Denevi et al., 2019, Khodak et al., 2019].

Knowledge distillation introduced in the deep learning context is a method to transfer knowledge from one architecture to another. In particular, when the architectures are identical, this is called self-distillation. The idea is to feed in predictions of the trained model as new target values for retraining (and iterate this loop possibly a few times). It has been empirically observed that the self-distilled model often achieves higher accuracy on held out data. Why this happens, however, has been a mystery: the self-distillation dynamics does not receive any new information about the task and solely evolves by looping over training. To the best of our knowledge, there is no rigorous understanding of why this happens. This work provides the first theoretical analysis of self-distillation. We focus on fitting a nonlinear function to training data, where the model space is Hilbert space and fitting is subject to L2 regularization in this function space. We show that self-distillation iterations modify regularization by progressively limiting the number of basis functions that can be used to represent the solution. This implies (as we also verify empirically) that while a few rounds of self-distillation may reduce over-fitting, further rounds may lead to under-fitting and thus worse performance.

Collaborative work often benefits from having teams or organizations with heterogeneous members. In this paper, we present a method to form such diverse teams from people arriving sequentially over time. We define a monotone submodular objective function that combines the diversity and quality of a team and propose an algorithm to maximize the objective while satisfying multiple constraints. This allows us to balance both how diverse the team is and how well it can perform the task at hand. Using crowd experiments, we show that, in practice, the algorithm leads to large gains in team diversity. Using simulations, we show how to quantify the additional cost of forming diverse teams and how to address the problem of simultaneously maximizing diversity for several attributes (e.g., country of origin, gender). Our method has applications in collaborative work ranging from team formation, the assignment of workers to teams in crowdsourcing, and reviewer allocation to journal papers arriving sequentially. Our code is publicly accessible for further research.

Gaby Ecanow loves listening to music, but never considered writing her own until taking 6.S191 (Introduction to Deep Learning). By her second class, the second-year MIT student had composed an original Irish folk song with the help of a recurrent neural network, and was considering how to adapt the model to create her own Louis the Child-inspired dance beats. "It was cool," she says. "It didn't sound at all like a machine had made it." This year, 6.S191 kicked off as usual, with students spilling into the aisles of Stata Center's Kirsch Auditorium during Independent Activities Period (IAP).