Artificial Intelligence
Pursuing computing advances to create intelligent machines that complement human reasoning to augment and enrich our experience and competencies.
Highlights
Dialogue as Dataflow: A new approach to conversational AI
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Register to watch Harnessing High-fidelity Simulation for Autonomous Systems Through AirSim
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Register to watch Transparency and Intelligibility Throughout the Machine Learning Life Cycle
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6,129 results found
Publication
Narya: Predictive and Adaptive Failure Mitigation to Avert Production Cloud VM Interruptions
This technical report is an extended version of our OSDI 2020 paper: Predictive and Adaptive Failure Mitigation to Avert Production Cloud VM Interruptions. Abstract: When a failure occurs in production systems, the highest priority is to quickly mitigate it. Despite its importance, failure mitigation is done in a reactive and ad-hoc way: taking some fixed actions only after a severe symptom is observed. For cloud systems, such a strategy is inadequate. In this paper, we…
Publication
Predictive and Adaptive Failure Mitigation to Avert Production Cloud VM Interruptions
When a failure occurs in production systems, the highest priority is to quickly mitigate it. Despite its importance, failure mitigation is done in a reactive and ad-hoc way: taking some fixed actions only after a severe symptom is observed. For cloud systems, such a strategy is inadequate. In this paper, we propose a preventive and adaptive failure mitigation service, NARYA, that is integrated in a production cloud, Microsoft Azure’s compute platform. Narya predicts imminent host…
Publication
How Incidental are the Incidents? Characterizing and Prioritizing Incidents for Large-Scale Online Service Systems
[Experience paper] Although tremendous efforts have been devoted to the quality assurance of online service systems, in reality, these systems still come across many incidents (i.e., unplanned interruptions and outages), which can decrease user satisfaction or cause economic loss. To better understand the characteristics of incidents and improve the incident management process, we perform the first large-scale empirical analysis of incidents collected from 18 real-world online service systems in a multinational technology company M. Surprisingly,…
Publication
Large-Scale Adversarial Training for Vision-and-Language Representation Learning
We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning. VILLA consists of two training stages: (i) task-agnostic adversarial pre-training; followed by (ii) task-specific adversarial finetuning. Instead of adding adversarial perturbations on image pixels and textual tokens, we propose to perform adversarial training in the embedding space of each modality. To enable large-scale training, we adopt the “free” adversarial training strategy and combine it with KL-divergence-based regularization to promote…
Publication
Learning the Linear Quadratic Regulator from Nonlinear Observations
We introduce a new problem setting for continuous control called the LQR with Rich Observations, or RichLQR. In our setting, the environment is summarized by a low-dimensional continuous latent state with linear dynamics and quadratic costs, but the agent operates on high-dimensional, nonlinear observations such as images from a camera. To enable sample-efficient learning, we assume that the learner has access to a class of decoder functions (e.g., neural networks) that is flexible enough to…
Publication
Leveraging Structured Metadata for Improving Question Answering on the Web
We show that leveraging metadata information from web pages can improve the performance of models for answer passage selection/reranking. We propose a neural passage selection model that leverages metadata information with a fine-grained encoding strategy, which learns the representation for metadata predicates in a hierarchical way. The models are evaluated on the MS MARCO (Nguyen et al., 2016) and Recipe-MARCO datasets. Results show that our models significantly outperform baseline models, which do not incorporate metadata.…
Publication
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
We analyze the influence of adversarial training on the loss landscape of machine learning models. To this end, we first provide analytical studies of the properties of adversarial loss functions under different adversarial budgets. We then demonstrate that the adversarial loss landscape is less favorable to optimization, due to increased curvature and more scattered gradients. Our conclusions are validated by numerical analyses, which show that training under large adversarial budgets impede the escape from suboptimal…
Publication
Uncertainty-aware Self-training for Few-shot Text Classification
Recent success of large-scale pre-trained language models crucially hinge on fine-tuning them on large amounts of labeled data for the downstream task, that are typically expensive to acquire. In this work, we study self-training as one of the earliest semi-supervised learning approaches to reduce the annotation bottleneck by making use of large-scale unlabeled data for the target task. Standard self-training mechanism randomly samples instances from the unlabeled pool to pseudo-label and augment labeled data. In…
Publication
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices
We prove a Chernoff-type bound for sums of matrix-valued random variables sampled via a regular (aperiodic and irreducible) finite Markov chain. Specially, consider a random walk on a regular Markov chain and a Hermitian matrix-valued function on its state space. Our result gives exponentially decreasing bounds on the tail distributions of the extreme eigenvalues of the sample mean matrix. Our proof is based on the matrix expander (regular undirected graph) Chernoff bound [Garg et…
Publication
Identifying Linked Incidents in Large-scale Online Service Systems
In large-scale online service systems, incidents occur frequently due to a variety of causes, from updates of software and hardware to changes in operation environment. These incidents could significantly degrade system’s availability and customers’ satisfaction. Some incidents are linked because they are duplicate or inter-related. The linked incidents can greatly help on-call engineers find mitigation solutions and identify the root causes. In this work, we investigate the incidents and their links in a real-world incident…