117 citations · 286 across the 21 of their papers we have counts for
21 papers
Harnessing Distribution Ratio Estimators for Learning Agents with Quality and Diversity
Tanmay Gangwani, Jian Peng, Yuan Zhou
Quality-Diversity (QD) is a concept from Neuroevolution with some intriguing applications to Reinforcement Learning. It facilitates learning a population of agents where each membe…
Off-Policy Interval Estimation with Lipschitz Value Iteration
Ziyang Tang, Yihao Feng, Na Zhang +2
Off-policy evaluation provides an essential tool for evaluating the effects of different policies or treatments using only observed data. When applied to high-stakes scenarios such…
Hunting for Dark Matter Subhalos in Strong Gravitational Lensing with Neural Networks
Joshua Yao-Yu Lin, Hang Yu, Warren Morningstar +2
Dark matter substructures are interesting since they can reveal the properties of dark matter. Collisionless N-body simulations of cold dark matter show more substructures compared…
Learning Guidance Rewards with Trajectory-space Smoothing
Tanmay Gangwani, Yuan Zhou, Jian Peng
Long-term temporal credit assignment is an important challenge in deep reinforcement learning (RL). It refers to the ability of the agent to attribute actions to consequences that…
Efficient Competitive Self-Play Policy Optimization
Yuanyi Zhong, Yuan Zhou, Jian Peng
Reinforcement learning from self-play has recently reported many successes. Self-play, where the agents compete with themselves, is often used to generate training data for iterati…
State-only Imitation with Transition Dynamics Mismatch
Tanmay Gangwani, Jian Peng
Imitation Learning (IL) is a popular paradigm for training agents to achieve complicated goals by leveraging expert behavior, rather than dealing with the hardships of designing a…
Disentangling Controllable Object through Video Prediction Improves Visual Reinforcement Learning
Yuanyi Zhong, Alexander Schwing, Jian Peng
In many vision-based reinforcement learning (RL) problems, the agent controls a movable object in its visual field, e.g., the player's avatar in video games and the robotic arm in…
Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery
Carl Yang, Mengxiong Liu, Frank He +3
Heterogeneous networks are widely used to model real-world semi-structured data. The key challenge of learning over such networks is the modeling of node similarity under both netw…
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding
Yu He, Yangqiu Song, Jianxin Li +3
Heterogeneous information network (HIN) embedding has gained increasing interests recently. However, the current way of random-walk based HIN embedding methods have paid few attent…
Characterizing Attacks on Deep Reinforcement Learning
Xinlei Pan, Chaowei Xiao, Warren He +8
Recent studies show that Deep Reinforcement Learning (DRL) models are vulnerable to adversarial attacks, which attack DRL models by adding small perturbations to the observations.…
Learning Belief Representations for Imitation Learning in POMDPs
Tanmay Gangwani, Joel Lehman, Qiang Liu +1
We consider the problem of imitation learning from expert demonstrations in partially observable Markov decision processes (POMDPs). Belief representations, which characterize the…
Thresholding Bandit with Optimal Aggregate Regret
Chao Tao, Saùl Blanco, Jian Peng +1
We consider the thresholding bandit problem, whose goal is to find arms of mean rewards above a given threshold , with a fixed budget of trials. We introduce LSA, a new, sim…
Anchor Box Optimization for Object Detection
Yuanyi Zhong, Jianfeng Wang, Jian Peng +1
In this paper, we propose a general approach to optimize anchor boxes for object detection. Nowadays, anchor boxes are widely adopted in state-of-the-art detection frameworks. Howe…
Learning Self-Imitating Diverse Policies
Tanmay Gangwani, Qiang Liu, Jian Peng
The success of popular algorithms for deep reinforcement learning, such as policy-gradients and Q-learning, relies heavily on the availability of an informative reward signal at ea…
Learning to Explore with Meta-Policy Gradient
Tianbing Xu, Qiang Liu, Liang Zhao +1
The performance of off-policy learning, including deep Q-learning and deep deterministic policy gradient (DDPG), critically depends on the choice of the exploration policy. Existin…
DPPred: An Effective Prediction Framework with Concise Discriminative Patterns
Jingbo Shang, Meng Jiang, Wenzhu Tong +3
In the literature, two series of models have been proposed to address prediction problems including classification and regression. Simple models, such as generalized linear models,…
Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks
Jingbo Shang, Meng Qu, Jialu Liu +3
Most real-world data can be modeled as heterogeneous information networks (HINs) consisting of vertices of multiple types and their relationships. Search for similar vertices of th…
Complexes Detection in Biological Networks via Diversified Dense Subgraphs Mining
Xiuli Ma, Guangyu Zhou, Jingjing Wang +2
Protein-protein interaction (PPI) networks, providing a comprehensive landscape of protein interacting patterns, enable us to explore biological processes and cellular components a…
Low-density locality-sensitive hashing boosts metagenomic binning
Yunan Luo, Jianyang Zeng, Bonnie Berger +1
Metagenomic binning is an essential task in analyzing metagenomic sequence datasets. To analyze structure or function of microbial communities from environmental samples, metagenom…
Bermuda: Bidirectional de novo assembly of transcripts with new insights for handling uneven coverage
Qingming Tang, Sheng Wang, Jian Peng +2
Motivation: RNA-seq has made feasible the analysis of a whole set of expressed mRNAs. Mapping-based assembly of RNA-seq reads sometimes is infeasible due to lack of high-quality re…
Exact Hybrid Covariance Thresholding for Joint Graphical Lasso
Qingming Tang, Chao Yang, Jian Peng +1
This paper considers the problem of estimating multiple related Gaussian graphical models from a -dimensional dataset consisting of different classes. Our work is based upon the…