Search Results for author: Yi Wu

Found 101 papers, 38 papers with code

Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study

no code implementations16 Apr 2024 Shusheng Xu, Wei Fu, Jiaxuan Gao, Wenjie Ye, Weilin Liu, Zhiyu Mei, Guangju Wang, Chao Yu, Yi Wu

However, in academic benchmarks, state-of-the-art results are often achieved via reward-free methods, such as Direct Preference Optimization (DPO).

Code Generation

Infinite-ID: Identity-preserved Personalization via ID-semantics Decoupling Paradigm

no code implementations18 Mar 2024 Yi Wu, Ziqiang Li, Heliang Zheng, Chaoyue Wang, Bin Li

Drawing on recent advancements in diffusion models for text-to-image generation, identity-preserved personalization has made significant progress in accurately capturing specific identities with just a single reference image.

Text-to-Image Generation

LLM-Powered Hierarchical Language Agent for Real-time Human-AI Coordination

1 code implementation23 Dec 2023 Jijia Liu, Chao Yu, Jiaxuan Gao, Yuqing Xie, Qingmin Liao, Yi Wu, Yu Wang

AI agents powered by Large Language Models (LLMs) have made significant advances, enabling them to assist humans in diverse complex tasks and leading to a revolution in human-AI coordination.

Code Generation

Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing

no code implementations4 Dec 2023 Ying Yuan, Haichuan Che, Yuzhe Qin, Binghao Huang, Zhao-Heng Yin, Kang-Won Lee, Yi Wu, Soo-Chul Lim, Xiaolong Wang

In this paper, we introduce a system that leverages visual and tactile sensory inputs to enable dexterous in-hand manipulation.

Evolving Domain Adaptation of Pretrained Language Models for Text Classification

no code implementations16 Nov 2023 Yun-Shiuan Chuang, Yi Wu, Dhruv Gupta, Rheeya Uppaal, Ananya Kumar, Luhang Sun, Makesh Narsimhan Sreedhar, Sijia Yang, Timothy T. Rogers, Junjie Hu

Adapting pre-trained language models (PLMs) for time-series text classification amidst evolving domain shifts (EDS) is critical for maintaining accuracy in applications like stance detection.

Domain Adaptation Stance Detection +3

Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game

no code implementations29 Oct 2023 Zelai Xu, Chao Yu, Fei Fang, Yu Wang, Yi Wu

To mitigate the intrinsic bias in language actions, our agents use an LLM to perform deductive reasoning and generate a diverse set of action candidates.

Decision Making Reinforcement Learning (RL)

BitNet: Scaling 1-bit Transformers for Large Language Models

2 code implementations17 Oct 2023 Hongyu Wang, Shuming Ma, Li Dong, Shaohan Huang, Huaijie Wang, Lingxiao Ma, Fan Yang, Ruiping Wang, Yi Wu, Furu Wei

The increasing size of large language models has posed challenges for deployment and raised concerns about environmental impact due to high energy consumption.

Language Modelling Quantization

Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games

no code implementations5 Oct 2023 Zelai Xu, Yancheng Liang, Chao Yu, Yu Wang, Yi Wu

Alternatively, Policy-Space Response Oracles (PSRO) is an iterative framework for learning NE, where the best responses w. r. t.

Multi-agent Reinforcement Learning

OmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Control

1 code implementation22 Sep 2023 Botian Xu, Feng Gao, Chao Yu, Ruize Zhang, Yi Wu, Yu Wang

In this work, we introduce OmniDrones, an efficient and flexible platform tailored for reinforcement learning in drone control, built on Nvidia's Omniverse Isaac Sim.

reinforcement-learning

DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data

no code implementations7 Aug 2023 Yancheng Liang, Jiajie Zhang, Hui Li, Xiaochen Liu, Yi Hu, Yong Wu, Jinyao Zhang, Yongyan Liu, Yi Wu

Despite the tremendous advances achieved over the past years by deep learning techniques, the latest risk prediction models for industrial applications still rely on highly handtuned stage-wised statistical learning tools, such as gradient boosting and random forest methods.

Quarl: A Learning-Based Quantum Circuit Optimizer

no code implementations17 Jul 2023 Zikun Li, Jinjun Peng, Yixuan Mei, Sina Lin, Yi Wu, Oded Padon, Zhihao Jia

Applying reinforcement learning (RL) to quantum circuit optimization raises two main challenges: the large and varying action space and the non-uniform state representation.

Reinforcement Learning (RL)

BrickPal: Augmented Reality-based Assembly Instructions for Brick Models

no code implementations6 Jul 2023 Yao Shi, Xiaofeng Zhang, Ran Zhang, Zhou Yang, Xiao Tang, Hongni Ye, Yi Wu

The assembly instruction is a mandatory component of Lego-like brick sets. The conventional production of assembly instructions requires a considerable amount of manual fine-tuning, which is intractable for casual users and customized brick sets. Moreover, the traditional paper-based instructions lack expressiveness and interactivity. To tackle the two problems above, we present BrickPal, an augmented reality-based system, which visualizes assembly instructions in an augmented reality head-mounted display.

SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores

1 code implementation29 Jun 2023 Zhiyu Mei, Wei Fu, Guangju Wang, Huanchen Zhang, Yi Wu

In a large-scale cluster, the novel architecture of SRL leads to up to 3. 7x speedup compared to the design choices adopted by the existing libraries.

reinforcement-learning Reinforcement Learning (RL)

Automatic Truss Design with Reinforcement Learning

1 code implementation27 Jun 2023 Weihua Du, Jinglun Zhao, Chao Yu, Xingcheng Yao, Zimeng Song, Siyang Wu, Ruifeng Luo, Zhiyuan Liu, Xianzhong Zhao, Yi Wu

Directly applying end-to-end reinforcement learning (RL) methods to truss layout design is infeasible either, since only a tiny portion of the entire layout space is valid under the physical constraints, leading to particularly sparse rewards for RL training.

Combinatorial Optimization Layout Design +3

Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios

no code implementations14 Jun 2023 Hong Sun, Ziqiang Li, Pengfei Xia, Heng Li, Beihao Xia, Yi Wu, Bin Li

However, existing backdoor attack methods make unrealistic assumptions, assuming that all training data comes from a single source and that attackers have full access to the training data.

Backdoor Attack

How Effective Are Neural Networks for Fixing Security Vulnerabilities

1 code implementation29 May 2023 Yi Wu, Nan Jiang, Hung Viet Pham, Thibaud Lutellier, Jordan Davis, Lin Tan, Petr Babkin, Sameena Shah

The results call for innovations to enhance automated Java vulnerability repair such as creating larger vulnerability repair training data, tuning LLMs with such data, and applying code simplification transformation to facilitate vulnerability repair.

Code Completion Program Repair

Efficient bimanual handover and rearrangement via symmetry-aware actor-critic learning

1 code implementation IEEE International Conference on Robotics and Automation (ICRA) 2023 Yunfei Li;, Chaoyi Pan, Huazhe Xu, Xiaolong Wang, Yi Wu

We develop a symmetry-aware actor-critic framework that leverages the interchangeable roles of the two manipulators in the bimanual control setting to reduce the policy search space.

Reinforcement Learning (RL)

Differentiable Arbitrating in Zero-sum Markov Games

no code implementations20 Feb 2023 Jing Wang, Meichen Song, Feng Gao, Boyi Liu, Zhaoran Wang, Yi Wu

We initiate the study of how to perturb the reward in a zero-sum Markov game with two players to induce a desirable Nash equilibrium, namely arbitrating.

Multi-agent Reinforcement Learning reinforcement-learning +1

SOAR: Scene-debiasing Open-set Action Recognition

no code implementations ICCV 2023 Yuanhao Zhai, Ziyi Liu, Zhenyu Wu, Yi Wu, Chunluan Zhou, David Doermann, Junsong Yuan, Gang Hua

Deep models have the risk of utilizing spurious clues to make predictions, e. g., recognizing actions via classifying the background scene.

Open Set Action Recognition Scene Classification

Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning

no code implementations17 Dec 2022 Zhecheng Yuan, Zhengrong Xue, Bo Yuan, Xueqian Wang, Yi Wu, Yang Gao, Huazhe Xu

Hence, we propose Pre-trained Image Encoder for Generalizable visual reinforcement learning (PIE-G), a simple yet effective framework that can generalize to the unseen visual scenarios in a zero-shot manner.

reinforcement-learning Reinforcement Learning (RL)

AlphaSnake: Policy Iteration on a Nondeterministic NP-hard Markov Decision Process

no code implementations17 Nov 2022 Kevin Du, Ian Gemp, Yi Wu, Yingying Wu

Reinforcement learning has recently been used to approach well-known NP-hard combinatorial problems in graph theory.

reinforcement-learning Reinforcement Learning (RL)

PILE: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation

no code implementations11 Nov 2022 Lianshang Cai, Linhao Zhang, Dehong Ma, Jun Fan, Daiting Shi, Yi Wu, Zhicong Cheng, Simiu Gu, Dawei Yin

In this paper, we focus on two key questions in knowledge distillation for ranking models: 1) how to ensemble knowledge from multi-teacher; 2) how to utilize the label information of data in the distillation process.

Knowledge Distillation

FedBA: Non-IID Federated Learning Framework in UAV Networks

no code implementations10 Oct 2022 Pei Li, Zhijun Liu, Luyi Chang, Jialiang Peng, Yi Wu

This is because most drones still use centralized cloud-based data processing, which may lead to leakage of data collected by drones.

Federated Learning

Multi-Task Learning for Emotion Descriptors Estimation at the fourth ABAW Challenge

no code implementations20 Jul 2022 Yanan Chang, Yi Wu, Xiangyu Miao, Jiahe Wang, Shangfei Wang

The 4th competition on affective behavior analysis in the wild (ABAW) provided images with valence/arousal, expression and action unit labels.

Multi-Task Learning

Hand-Assisted Expression Recognition Method from Synthetic Images at the Fourth ABAW Challenge

no code implementations20 Jul 2022 Xiangyu Miao, Jiahe Wang, Yanan Chang, Yi Wu, Shangfei Wang

Learning from synthetic images plays an important role in facial expression recognition task due to the difficulties of labeling the real images, and it is challenging because of the gap between the synthetic images and real images.

Facial Expression Recognition Facial Expression Recognition (FER)

Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning

no code implementations24 Jun 2022 Yunfei Li, Tian Gao, Jiaqi Yang, Huazhe Xu, Yi Wu

It has been a recent trend to leverage the power of supervised learning (SL) towards more effective reinforcement learning (RL) methods.

reinforcement-learning Reinforcement Learning (RL)

Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning

no code implementations15 Jun 2022 Wei Fu, Chao Yu, Zelai Xu, Jiaqi Yang, Yi Wu

Despite all the advantages, we revisit these two principles and show that in certain scenarios, e. g., environments with a highly multi-modal reward landscape, value decomposition, and parameter sharing can be problematic and lead to undesired outcomes.

Multi-agent Reinforcement Learning reinforcement-learning +2

Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition

no code implementations28 May 2022 Kan Xie, Zhe Zhang, Bo Li, Jiawen Kang, Dusit Niyato, Shengli Xie, Yi Wu

However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information.

Federated Learning Privacy Preserving +1

Self-Calibrated Efficient Transformer for Lightweight Super-Resolution

1 code implementation19 Apr 2022 Wenbin Zou, Tian Ye, Weixin Zheng, Yunchen Zhang, Liang Chen, Yi Wu

Recently, deep learning has been successfully applied to the single-image super-resolution (SISR) with remarkable performance.

Image Super-Resolution

Learning Design and Construction with Varying-Sized Materials via Prioritized Memory Resets

1 code implementation12 Apr 2022 Yunfei Li, Tao Kong, Lei LI, Yi Wu

Can a robot autonomously learn to design and construct a bridge from varying-sized blocks without a blueprint?

Motion Planning

E^2TAD: An Energy-Efficient Tracking-based Action Detector

1 code implementation9 Apr 2022 Xin Hu, Zhenyu Wu, Hao-Yu Miao, Siqi Fan, Taiyu Long, Zhenyu Hu, Pengcheng Pi, Yi Wu, Zhou Ren, Zhangyang Wang, Gang Hua

Video action detection (spatio-temporal action localization) is usually the starting point for human-centric intelligent analysis of videos nowadays.

Fine-Grained Action Detection object-detection +3

Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization

no code implementations ICLR 2022 Zihan Zhou, Wei Fu, Bingliang Zhang, Yi Wu

We present Reward-Switching Policy Optimization (RSPO), a paradigm to discover diverse strategies in complex RL environments by iteratively finding novel policies that are both locally optimal and sufficiently different from existing ones.

Continuous Control

Understanding Curriculum Learning in Policy Optimization for Online Combinatorial Optimization

1 code implementation11 Feb 2022 Runlong Zhou, Zelin He, Yuandong Tian, Yi Wu, Simon S. Du

Furthermore, our theory explains the benefit of curriculum learning: it can find a strong sampling policy and reduce the distribution shift, a critical quantity that governs the convergence rate in our theorem.

Combinatorial Optimization Reinforcement Learning (RL)

Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones

no code implementations3 Jan 2022 Zhe Zhang, Shiyao Ma, Zhaohui Yang, Zehui Xiong, Jiawen Kang, Yi Wu, Kejia Zhang, Dusit Niyato

This emerging technology relies on sharing ground truth labeled data between Unmanned Aerial Vehicle (UAV) swarms to train a high-quality automatic image recognition model.

Federated Learning Privacy Preserving

Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination

2 code implementations22 Dec 2021 Rui Zhao, Jinming Song, Yufeng Yuan, Hu Haifeng, Yang Gao, Yi Wu, Zhongqian Sun, Yang Wei

We study the problem of training a Reinforcement Learning (RL) agent that is collaborative with humans without using any human data.

Reinforcement Learning (RL)

Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension

no code implementations13 Dec 2021 Shusheng Xu, Yichen Liu, Xiaoyu Yi, Siyuan Zhou, Huizi Li, Yi Wu

We present Native Chinese Reader (NCR), a new machine reading comprehension (MRC) dataset with particularly long articles in both modern and classical Chinese.

Common Sense Reasoning Machine Reading Comprehension

A Benchmark for Low-Switching-Cost Reinforcement Learning

no code implementations13 Dec 2021 Shusheng Xu, Yancheng Liang, Yunfei Li, Simon Shaolei Du, Yi Wu

A ubiquitous requirement in many practical reinforcement learning (RL) applications, including medical treatment, recommendation system, education and robotics, is that the deployed policy that actually interacts with the environment cannot change frequently.

Atari Games reinforcement-learning +1

Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward

no code implementations12 Dec 2021 Weilin Liu, Ye Mu, Chao Yu, Xuefei Ning, Zhong Cao, Yi Wu, Shuang Liang, Huazhong Yang, Yu Wang

These scenarios indeed correspond to the vulnerabilities of the under-test driving policies, thus are meaningful for their further improvements.

Autonomous Driving Multi-agent Reinforcement Learning

NovelD: A Simple yet Effective Exploration Criterion

1 code implementation NeurIPS 2021 Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian

We analyze NovelD thoroughly in MiniGrid and found that empirically it helps the agent explore the environment more uniformly with a focus on exploring beyond the boundary.

Efficient Exploration Montezuma's Revenge +1

Nonlinear Intensity Sonar Image Matching based on Deep Convolution Features

no code implementations17 Nov 2021 Xiaoteng Zhou, Changli Yu, Xin Yuan, Yi Wu, Haijun Feng, Citong Luo

In the field of deep-sea exploration, sonar is presently the only efficient long-distance sensing device.

Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems

1 code implementation NeurIPS 2021 Jiayu Chen, Yuanxin Zhang, Yuanfan Xu, Huimin Ma, Huazhong Yang, Jiaming Song, Yu Wang, Yi Wu

We motivate our paradigm through a variational perspective, where the learning objective can be decomposed into two terms: task learning on the current task distribution, and curriculum update to a new task distribution.

Multi-agent Reinforcement Learning

PhyloTransformer: A Discriminative Model for Mutation Prediction Based on a Multi-head Self-attention Mechanism

no code implementations3 Nov 2021 Yingying Wu, Shusheng Xu, Shing-Tung Yau, Yi Wu

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused an ongoing pandemic infecting 219 million people as of 10/19/21, with a 3. 6% mortality rate.

Language Modelling

Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design

no code implementations26 Oct 2021 Zhe Zhang, Shiyao Ma, Jiangtian Nie, Yi Wu, Qiang Yan, Xiaoke Xu, Dusit Niyato

In this paper, we present a robust semi-supervised FL system design, where the system aims to solve the problem of data availability and non-IID in FL.

Federated Learning

Learning Efficient Multi-Agent Cooperative Visual Exploration

no code implementations12 Oct 2021 Chao Yu, Xinyi Yang, Jiaxuan Gao, Huazhong Yang, Yu Wang, Yi Wu

In this paper, we extend the state-of-the-art single-agent visual navigation method, Active Neural SLAM (ANS), to the multi-agent setting by introducing a novel RL-based planning module, Multi-agent Spatial Planner (MSP). MSP leverages a transformer-based architecture, Spatial-TeamFormer, which effectively captures spatial relations and intra-agent interactions via hierarchical spatial self-attentions.

Reinforcement Learning (RL) Visual Navigation

Sequence Level Contrastive Learning for Text Summarization

no code implementations8 Sep 2021 Shusheng Xu, Xingxing Zhang, Yi Wu, Furu Wei

In this paper, we propose a contrastive learning model for supervised abstractive text summarization, where we view a document, its gold summary and its model generated summaries as different views of the same mean representation and maximize the similarities between them during training.

Abstractive Text Summarization Contrastive Learning +2

Temporal Induced Self-Play for Stochastic Bayesian Games

1 code implementation21 Aug 2021 Weizhe Chen, Zihan Zhou, Yi Wu, Fei Fang

One practical requirement in solving dynamic games is to ensure that the players play well from any decision point onward.

Learning to Design and Construct Bridge without Blueprint

no code implementations5 Aug 2021 Yunfei Li, Tao Kong, Lei LI, Yifeng Li, Yi Wu

In this task, the robot needs to first design a feasible bridge architecture for arbitrarily wide cliffs and then manipulate the blocks reliably to construct a stable bridge according to the proposed design.

Motion Planning

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

no code implementations26 Apr 2021 Longling Zhang, Bochen Shen, Ahmed Barnawi, Shan Xi, Neeraj Kumar, Yi Wu

Under the FL framework and Differentially Private thinking, we propose a FederatedDifferentially Private Generative Adversarial Network (FedDPGAN) to detectCOVID-19 pneumonia for sustainable smart cities.

Federated Learning Generative Adversarial Network

Classifying herbal medicine origins by temporal and spectral data mining of electronic nose

1 code implementation14 Apr 2021 Li Liu, Xianghao Zhan, Ziheng Duan, Yi Wu, Rumeng Wu, Xiaoqing Guan, Zhan Wang, You Wang, Guang Li

In this study, we classified different origins of three categories of herbal medicines with different feature extraction methods: manual feature extraction, mathematical transformation, deep learning algorithms.

Dimensionality Reduction

Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels

1 code implementation7 Apr 2021 Qingqing Long, Yilun Jin, Yi Wu, Guojie Song

However, the inability of GNNs to model substructures in graphs remains a significant drawback.

Graph Mining

Reference-Aided Part-Aligned Feature Disentangling for Video Person Re-Identification

no code implementations21 Mar 2021 Guoqing Zhang, Yuhao Chen, Yang Dai, yuhui Zheng, Yi Wu

Due to the inaccurate person detections and pose changes, pedestrian misalignment significantly increases the difficulty of feature extraction and matching.

Video-Based Person Re-Identification

Solving Compositional Reinforcement Learning Problems via Task Reduction

1 code implementation ICLR 2021 Yunfei Li, Yilin Wu, Huazhe Xu, Xiaolong Wang, Yi Wu

We propose a novel learning paradigm, Self-Imitation via Reduction (SIR), for solving compositional reinforcement learning problems.

Continuous Control reinforcement-learning +1

Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization

2 code implementations ICLR 2021 Zhenggang Tang, Chao Yu, Boyuan Chen, Huazhe Xu, Xiaolong Wang, Fei Fang, Simon Du, Yu Wang, Yi Wu

We propose a simple, general and effective technique, Reward Randomization for discovering diverse strategic policies in complex multi-agent games.

The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

15 code implementations2 Mar 2021 Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, Yi Wu

This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems.

Multi-agent Reinforcement Learning reinforcement-learning +3

Growth, Electronic Structure and Superconductivity of Ultrathin Epitaxial CoSi2 Films

no code implementations21 Jan 2021 Yuan Fang, Ding Wang, Peng Li, Hang Su, Tian Le, Yi Wu, Guo-Wei Yang, Hua-Li Zhang, Zhi-Guang Xiao, Yan-Qiu Sun, Si-Yuan Hong, Yan-Wu Xie, Huan-Hua Wang, Chao Cao, Xin Lu, Hui-Qiu Yuan, Yang Liu

We report growth, electronic structure and superconductivity of ultrathin epitaxial CoSi2 films on Si(111).

Mesoscale and Nanoscale Physics

Deep Q-Learning with Low Switching Cost

no code implementations1 Jan 2021 Shusheng Xu, Simon Shaolei Du, Yi Wu

We initiate the study on deep reinforcement learning problems that require low switching cost, i. e., small number of policy switches during training.

Atari Games Q-Learning +2

Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms

no code implementations1 Jan 2021 Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, Yi Wu

We benchmark commonly used multi-agent deep reinforcement learning (MARL) algorithms on a variety of cooperative multi-agent games.

Benchmarking reinforcement-learning +2

BeBold: Exploration Beyond the Boundary of Explored Regions

2 code implementations15 Dec 2020 Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian

In this paper, we analyze the pros and cons of each method and propose the regulated difference of inverse visitation counts as a simple but effective criterion for IR.

Efficient Exploration NetHack

Charge density wave and weak Kondo effect in a Dirac semimetal CeSbTe

no code implementations23 Nov 2020 Peng Li, Baijiang Lv, Yuan Fang, Wei Guo, Zhongzheng Wu, Yi Wu, Cheng-Maw Cheng, Dawei Shen, Yuefeng Nie, Luca Petaccia, Chao Cao, Zhu-An Xu, Yang Liu

Using angle-resolved photoemission spectroscopy (ARPES) and low-energy electron diffraction (LEED), together with density-functional theory (DFT) calculation, we report the formation of charge density wave (CDW) and its interplay with the Kondo effect and topological states in CeSbTe.

Strongly Correlated Electrons Materials Science

Multi-Agent Collaboration via Reward Attribution Decomposition

2 code implementations16 Oct 2020 Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian

In this work, we propose Collaborative Q-learning (CollaQ) that achieves state-of-the-art performance in the StarCraft multi-agent challenge and supports ad hoc team play.

Dota 2 Multi-agent Reinforcement Learning +2

Streaming Graph Neural Networks via Continual Learning

no code implementations23 Sep 2020 Junshan Wang, Guojie Song, Yi Wu, Liang Wang

In this paper, we propose a streaming GNN model based on continual learning so that the model is trained incrementally and up-to-date node representations can be obtained at each time step.

Continual Learning Node Classification

Multi-Task Reinforcement Learning with Soft Modularization

1 code implementation NeurIPS 2020 Ruihan Yang, Huazhe Xu, Yi Wu, Xiaolong Wang

While training multiple tasks jointly allow the policies to share parameters across different tasks, the optimization problem becomes non-trivial: It remains unclear what parameters in the network should be reused across tasks, and how the gradients from different tasks may interfere with each other.

Meta-Learning Multi-Task Learning +2

Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning

1 code implementation ICLR 2020 Qian Long, Zihan Zhou, Abhibav Gupta, Fei Fang, Yi Wu, Xiaolong Wang

In multi-agent games, the complexity of the environment can grow exponentially as the number of agents increases, so it is particularly challenging to learn good policies when the agent population is large.

Multi-agent Reinforcement Learning reinforcement-learning +1

Influence-Based Multi-Agent Exploration

1 code implementation ICLR 2020 Tonghan Wang, Jianhao Wang, Yi Wu, Chongjie Zhang

We present two exploration methods: exploration via information-theoretic influence (EITI) and exploration via decision-theoretic influence (EDTI), by exploiting the role of interaction in coordinated behaviors of agents.

reinforcement-learning Reinforcement Learning (RL)

PPGAN: Privacy-preserving Generative Adversarial Network

no code implementations4 Oct 2019 Yi Liu, Jialiang Peng, James J. Q. Yu, Yi Wu

To address this issue, we propose a Privacy-preserving Generative Adversarial Network (PPGAN) model, in which we achieve differential privacy in GANs by adding well-designed noise to the gradient during the model learning procedure.

Generative Adversarial Network Privacy Preserving

Emergent Tool Use From Multi-Agent Autocurricula

3 code implementations ICLR 2020 Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, Igor Mordatch

Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocurriculum inducing multiple distinct rounds of emergent strategy, many of which require sophisticated tool use and coordination.

reinforcement-learning Reinforcement Learning (RL)

Bayesian Relational Memory for Semantic Visual Navigation

1 code implementation ICCV 2019 Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian

We introduce a new memory architecture, Bayesian Relational Memory (BRM), to improve the generalization ability for semantic visual navigation agents in unseen environments, where an agent is given a semantic target to navigate towards.

Navigate Visual Navigation

Fairness in Recommendation Ranking through Pairwise Comparisons

no code implementations2 Mar 2019 Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow

Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information.

Fairness Recommendation Systems

Deep Reinforcement Learning for Green Security Games with Real-Time Information

no code implementations6 Nov 2018 Yufei Wang, Zheyuan Ryan Shi, Lantao Yu, Yi Wu, Rohit Singh, Lucas Joppa, Fei Fang

Green Security Games (GSGs) have been proposed and applied to optimize patrols conducted by law enforcement agencies in green security domains such as combating poaching, illegal logging and overfishing.

Q-Learning reinforcement-learning +1

Learning and Planning with a Semantic Model

no code implementations ICLR 2019 Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian

Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI.

Visual Navigation

Robust Fuzzy-Learning For Partially Overlapping Channels Allocation In UAV Communication Networks

no code implementations28 Jun 2018 Chaoqiong Fan, Bin Li, Jia Hou, Yi Wu, Weisi Guo, Chenglin Zhao

This allows the system to achieve a smoother and more robust performance by optimizing in an alternate space.

Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms

no code implementations ICML 2018 Yi Wu, Siddharth Srivastava, Nicholas Hay, Simon Du, Stuart Russell

Despite the recent successes of probabilistic programming languages (PPLs) in AI applications, PPLs offer only limited support for random variables whose distributions combine discrete and continuous elements.

Probabilistic Programming

Near-Linear Time Local Polynomial Nonparametric Estimation with Box Kernels

no code implementations26 Feb 2018 Yining Wang, Yi Wu, Simon S. Du

Local polynomial regression (Fan and Gijbels 1996) is an important class of methods for nonparametric density estimation and regression problems.

Density Estimation regression

Building Generalizable Agents with a Realistic and Rich 3D Environment

5 code implementations ICLR 2018 Yi Wu, Yuxin Wu, Georgia Gkioxari, Yuandong Tian

To generalize to unseen environments, an agent needs to be robust to low-level variations (e. g. color, texture, object changes), and also high-level variations (e. g. layout changes of the environment).

Data Augmentation

Adversarial Training for Relation Extraction

no code implementations EMNLP 2017 Yi Wu, David Bamman, Stuart Russell

Adversarial training is a mean of regularizing classification algorithms by generating adversarial noise to the training data.

General Classification Image Classification +4

Meta-Learning MCMC Proposals

no code implementations NeurIPS 2018 Tongzhou Wang, Yi Wu, David A. Moore, Stuart J. Russell

The learned neural proposals generalize to occurrences of common structural motifs across different models, allowing for the construction of a library of learned inference primitives that can accelerate inference on unseen models with no model-specific training required.

Meta-Learning named-entity-recognition +2

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

3 code implementations ICCV 2017 Yousong Zhu, Chaoyang Zhao, Jinqiao Wang, Xu Zhao, Yi Wu, Hanqing Lu

To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection.

Object object-detection +3

Improving the Annotation of Sentence Specificity

no code implementations LREC 2016 Junyi Jessy Li, Bridget O{'}Daniel, Yi Wu, Wenli Zhao, Ani Nenkova

We found that the lack of specificity distributes evenly among immediate prior context, long distance prior context and no prior context.

Sentence Specificity

Towards Practical Bayesian Parameter and State Estimation

no code implementations29 Mar 2016 Yusuf Bugra Erol, Yi Wu, Lei LI, Stuart Russell

Joint state and parameter estimation is a core problem for dynamic Bayesian networks.

Value Iteration Networks

8 code implementations NeurIPS 2016 Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, Pieter Abbeel

We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within.

reinforcement-learning Reinforcement Learning (RL)

Adaptive Compressive Tracking via Online Vector Boosting Feature Selection

no code implementations21 Apr 2015 Qingshan Liu, Jing Yang, Kaihua Zhang, Yi Wu

Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency, but it cannot well deal with the large scale target appearance variations due to its data-independent random projection matrix that results in less discriminative features.

feature selection

Tractability and Decompositions of Global Cost Functions

no code implementations9 Feb 2015 David Allouche, Christian Bessiere, Patrice Boizumault, Simon de Givry, Patricia Gutierrez, Jimmy H. M. Lee, Kam Lun Leung, Samir Loudni, Jean-Philippe Métivier, Thomas Schiex, Yi Wu

A global cost function is called tractable projection-safe when applying an EPT to it is tractable and does not break the tractability property.

Robust Visual Tracking via Convolutional Networks

no code implementations19 Jan 2015 Kaihua Zhang, Qingshan Liu, Yi Wu, Ming-Hsuan Yang

In this paper we present that, even without offline training with a large amount of auxiliary data, simple two-layer convolutional networks can be powerful enough to develop a robust representation for visual tracking.

Visual Tracking

Online Object Tracking: A Benchmark

no code implementations CVPR 2013 Yi Wu, Jongwoo Lim, Ming-Hsuan Yang

Object tracking is one of the most important components in numerous applications of computer vision.

Object Object Tracking

Dual-Space Analysis of the Sparse Linear Model

no code implementations NeurIPS 2012 Yi Wu, David P. Wipf

In contrast, for analyses of update rules and sparsity properties of local and global solutions, as well as extensions to more general likelihood models, we can leverage coefficient-space techniques developed for Type I and apply them to Type II.

Vocal Bursts Type Prediction

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