30 citations · 74 across the 8 of their papers we have counts for
8 papers
STREET: A Multi-Task Structured Reasoning and Explanation Benchmark
Danilo Ribeiro, Shen Wang, Xiaofei Ma +10
We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark. Unlike most existing question-answering (QA) datasets, we expect mo…
Robust Consensus Clustering and its Applications for Advertising Forecasting
Deguang Kong, Miao Lu, Konstantin Shmakov +1
Consensus clustering aggregates partitions in order to find a better fit by reconciling clustering results from different sources/executions. In practice, there exist noise and out…
Do not Waste Money on Advertising Spend: Bid Recommendation via Concavity Changes
Deguang Kong, Konstantin Shmakov, Jian Yang
In computational advertising, a challenging problem is how to recommend the bid for advertisers to achieve the best return on investment (ROI) given budget constraint. This paper p…
Demystifying Advertising Campaign Bid Recommendation: A Constraint target CPA Goal Optimization
Deguang Kong, Konstantin Shmakov, Jian Yang
In cost-per-click (CPC) or cost-per-impression (CPM) advertising campaigns, advertisers always run the risk of spending the budget without getting enough conversions. Moreover, the…
Language Agnostic Multilingual Information Retrieval with Contrastive Learning
Xiyang Hu, Xinchi Chen, Peng Qi +4
Multilingual information retrieval (IR) is challenging since annotated training data is costly to obtain in many languages. We present an effective method to train multilingual IR…
Science Driven Innovations Powering Mobile Product: Cloud AI vs. Device AI Solutions on Smart Device
Deguang Kong
Recent years have witnessed the increasing popularity of mobile devices (such as iphone) due to the convenience that it brings to human lives. On one hand, rich user profiling and…
DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices
Dawei Li, Xiaolong Wang, Deguang Kong
Deploying deep neural networks on mobile devices is a challenging task. Current model compression methods such as matrix decomposition effectively reduce the deployed model size, b…
An Iterative Locally Linear Embedding Algorithm
Deguang Kong, Chris H. Q. Ding, Heng Huang +1
Local Linear embedding (LLE) is a popular dimension reduction method. In this paper, we first show LLE with nonnegative constraint is equivalent to the widely used Laplacian embedd…