• Publications
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SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
TLDR
Modeling the data generator as a stochastic policy in reinforcement learning (RL), SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update.
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
TLDR
A unified framework takes advantage of both schools of thinking in information retrieval modelling and shows that the generative model learns to fit the relevance distribution over documents via the signals from the discriminative model to achieve a better estimation for document ranking.
Product-Based Neural Networks for User Response Prediction
  • Yanru Qu, Han Cai, Jun Wang
  • Computer Science
    IEEE 16th International Conference on Data Mining…
  • 1 November 2016
TLDR
A Product-based Neural Networks (PNN) with an embedding layer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between interfield categories, and further fully connected layers to explore high-order feature interactions.
Self-taught hashing for fast similarity search
TLDR
This paper proposes a novel Self-Taught Hashing (STH) approach to semantic hashing: it first finds the optimal l-bit binary codes for all documents in the given corpus via unsupervised learning, and then train l classifiers via supervised learning to predict the l- bit code for any query document unseen before.
Texygen: A Benchmarking Platform for Text Generation Models
TLDR
The Texygen platform could help standardize the research on text generation and improve the reproductivity and reliability of future research work in text generation.
Long Text Generation via Adversarial Training with Leaked Information
TLDR
The discriminative net is allowed to leak its own high-level extracted features to the generative net to further help the guidance, and without any supervision, LeakGAN would be able to implicitly learn sentence structures only through the interaction between Manager and Worker.
Efficient Architecture Search by Network Transformation
TLDR
This paper proposes a new framework toward efficient architecture search by exploring the architecture space based on the current network and reusing its weights, and employs a reinforcement learning agent as the meta-controller, whose action is to grow the network depth or layer width with function-preserving transformations.
Deep Learning over Multi-field Categorical Data - - A Case Study on User Response Prediction
TLDR
This paper proposes two novel models using deep neural networks (DNNs) to automatically learn effective patterns from categorical feature interactions and make predictions of users' ad clicks and demonstrates that their methods work better than major state-of-the-art models.
Portfolio theory of information retrieval
TLDR
An efficient document ranking algorithm is derived that generalizes the well-known probability ranking principle by considering both the uncertainty of relevance predictions and correlations between retrieved documents and the benefit of diversification is mathematically quantified.
Optimal real-time bidding for display advertising
TLDR
The mathematical derivation suggests that optimal bidding strategies should try to bid more impressions rather than focus on a small set of high valued impressions because according to the current RTB market data, compared to the higher evaluated impressions, the lower evaluated ones are more cost effective and the chances of winning them are relatively higher.
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