Daoming Lyu will do a summer research scientist internship in Tencent AI lab, Seattle, WA. Congratulations to Daoming!
Dr. Bo Liu will serve as a senior PC member of IJCAI-2020, and a PC member of other AI/ML conferences (NIPS, ICML, UAI, ICLR, AISTATS, AAAI).
Our paper "A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming" is accepted by ICLP-2019
! This is our 3rd publication on the topic of trustworthy and interpretable decision-making! Thanks to my collaborators!
We are grateful to the NSF Award "Trustworthy Interactive Decision-making Using Symbolic Planning"
(IIS-1910794, $420K) under the IIS core program! Make autonomies smart, interpretable, and risk-aware!
Our SDRL and PEORL work were highlighted in the AAAI'2019 Tutorial "Knowledge-based
Sequential Decision-Making under Uncertainty"
by Dr. Shiqi Zhang from SUNY Binghamton!
Our patent "Systems and methods for neural clinical paraphrase generation" is issued! Thanks to my collaborators and Dr. Sadid Hasan for the lead!
Mar 2019Nirmit Patel
, the first M.S student in our lab, just won the prestigious Outstanding Master Student Award. Way to go, Nirmit!
Dr. Bo Liu will serve as a senior PC member of IJCAI-2019, and a PC member of ICML, UAI, NIPS, ICLR, AAAI, AISTATS.
paper "Optimal Control of Complex Systems through Variational Inference with a Discrete Event Decision Process" is accepted! Thanks to my collaborators!
We are grateful to Amazon for awarding us the prestigious Amazon Research Award
(Class of 2018)! We will conduct research on using sequential decision-making to tackle fraud transaction risk management problems in e-commerce.
paper accepted! "SDRL: Interpretable and Data-efficient
Deep Reinforcement Learning Leveraging Symbolic Planning" is our 2nd publication on the topic of trustworthy and interpretable decision-making! Thanks to my collaborators!
Our paper "A Block Coordinate Ascent Algorithm for Mean-Variance Optimization" is accepted by NIPS-2018
! This offers the first risk-sensitive RL method without the burden of tuning two-time-scale stepsizes, and also with provable sample complexity analysis! Thanks to my collaborators!
Our paper "Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity" is accepted by Journal of Artificial Intelligence Research (JAIR
)! This paper provides a family of stable, off-policy, linear complexity per-step, and provable sample complexity reinforcement learning algorithms based on the Legendre-Fenchel duality. Thanks to my collaborators!
Our paper "Hierarchical Feature Selection for Random Projection" is accepted by IEEE-TNN
! Thanks to my collaborators!
Our paper "Stable and Efficient Policy Evaluation" is accepted by IEEE-TNN
! This paper offers an attempt when you cannot obtain long traces (consecutive trajectories) in some problems. Thanks to my collaborators! Also congratulations to Daoming, who is the first author of the paper!
Our paper "PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making" is accepted by IJCAI-2018
! This paper offers a unified framework between symbolic planning and data-driven decision-making. Thanks to my collaborators!
Daoming Lyu will do a summer research scientist internship in Maana in Bellevue, WA. Congratulations to Daoming!
Our lab was awarded the prestigious Tencent Rhino-Bird Award
(Class of 2017) by Tencent AI LAB among many world-class researchers! Thanks to Tencent!
You are welcome to submit your wonderful ideas on machine learning, data mining with applications to cyber systems! Check the ICDM-2017 workshop 'ML in Cyber!'
See you in New Orleans in the sunny November!
Our paper “Dantzig Selector with an Approximately Optimal Denoising Matrix and its Application to Reinforcement Learning” is accepted by UAI-2016! Thanks to my collaborators!
Our paper “Proximal Gradient Temporal Difference Learning Algorithms” is accepted by IJCAI-2016! Thanks to my collaborators!
Our Proximal Gradient TD work received high praise from Prof. Rich Sutton in NIPS-2015
"Try the new true-gradient RL methods (Gradient-TD and proximal-gradient TD) developed by Maei (2011) and Mahadevan (2015) et al. These seem to me to be the best attempts to make TD methods with the robust convergence properties of stochastic gradient descent."
Our paper “Uncorrelated Group LASSO” is accepted by AAAI-2016! Congratulations to my collaborators!
Our paper “Finite-Sample Analysis of Proximal Gradient TD Algorithms” won the Facebook Best Student Paper Award of UAI 2015! Thanks to my collaborators!