211 citations · 303 across the 6 of their papers we have counts for
6 papers
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
Evan Zheran Liu, Kelvin Guu, Panupong Pasupat +2
Reinforcement learning (RL) agents improve through trial-and-error, but when reward is sparse and the agent cannot discover successful action sequences, learning stagnates. This ha…
Adversarial Learning for Neural Dialogue Generation
Jiwei Li, Will Monroe, Tianlin Shi +3
In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that ar…
Max-margin Deep Generative Models
Chongxuan Li, Jun Zhu, Tianlin Shi +1
Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. How…
A Reverse Hierarchy Model for Predicting Eye Fixations
Tianlin Shi, Liang Ming, Xiaolin Hu
A number of psychological and physiological evidences suggest that early visual attention works in a coarse-to-fine way, which lays a basis for the reverse hierarchy theory (RHT).…
Online Bayesian Passive-Aggressive Learning
Tianlin Shi, Jun Zhu
Online Passive-Aggressive (PA) learning is an effective framework for performing max-margin online learning. But the deterministic formulation and estimated single large-margin mod…
A Fully Polynomial-Time Approximation Scheme for Approximating a Sum of Random Variables
Jian Li, Tianlin Shi
Given independent random variables and an integer , we study the fundamental problem of computing the probability that the sum is at…