Artificial Intelligence

Automatic synthesis of web data extraction programs has been explored in a variety of settings, but in practice there remain various usability challenges around robustness, the amount of training effort required, the complexity of programs synthesized, as well as the ease of interaction in limited UI environments. In this work we address these challenges based on a novel program synthesis approach which combines the benefits of deductive (top-down) and enumerative (bottom-up) synthesis strategies. This yields…

Modern cloud systems have a vast number of components that continuously undergo changes. Deploying these frequent updates quickly without breaking the system is challenging. In this paper, we present Gandalf, an end-to-end analytics service for safe deployment in a large-scale system infrastructure. Gandalf enables rapid and robust impact assessment of software rollouts to catch bad rollouts before they cause widespread outages. Gandalf monitors and analyzes various fault signals and correlates each signal against all the…

Skeleton Learning (SL) is the task for learning an undirected graph from the input data that captures their dependency relations. SL plays a pivotal role in causal learning and has attracted growing attention in the research community lately. Due to the high time complexity, anytime SL has emerged which learns a skeleton incrementally and improves it overtime. In this paper, we first propose and advocate the reliability requirement for anytime SL to be practically useful.…

We consider the adaptive influence maximization problem: given a network and a budget k, iteratively select k seeds in the network to maximize the expected number of adopters. In the full-adoption feedback model, after selecting each seed, the seed-picker observes all the resulting adoptions. In the myopic feedback model, the seed-picker only observes whether each neighbor of the chosen seed adopts. Motivated by the extreme success of greedy-based algorithms/heuristics for influence maximization, we propose the…

In this paper, we study the non-stationary online second price auction problem. We assume that the seller is selling the same type of items in T rounds by the second price auction, and she can set the reserve price in each round. In each round, the bidders draw their private values from a joint distribution unknown to the seller. Then, the seller announced the reserve price in this round. Next, bidders with private values higher…

Continuous influence maximization (CIM) generalizes the original influence maximization by  incorporating general marketing strategies: a marketing strategy mix is a vector x = (x_1, … ,x_d) such that for each node v in a social network,  v could be activated as a seed of diffusion with probability h_v(x),  where h_v is a strategy activation function satisfying DR-submodularity. CIM is the task of selecting a strategy mix x with constraint x_1+ … + x_d <= k…

In this paper, we propose Latent Relation Language Models (LRLMs), a class of language models that parameterizes the joint distribution over the words in a document and the entities that occur therein via knowledge graph relations. This model has a number of attractive properties: it not only improves language modeling performance, but is also able to annotate the posterior probability of entity spans for a given text through relations. Experiments demonstrate empirical improvements over both…

We consider a problem of stochastic online learning with general probabilistic graph feedback, where each directed edge in the feedback graph has probability pij . Two cases are covered. (a) The one-step case, where after playing arm i the learner observes a sample reward feedback of arm j with independent probability p_{ij} . (b) The cascade case where after playing arm i the learner observes feedback of all arms j in a probabilistic cascade starting…