Search Results for author: Miao Liu

Found 39 papers, 11 papers with code

Listen to Look into the Future: Audio-Visual Egocentric Gaze Anticipation

no code implementations6 May 2023 Bolin Lai, Fiona Ryan, Wenqi Jia, Miao Liu, James M. Rehg

Egocentric gaze anticipation serves as a key building block for the emerging capability of Augmented Reality.

Representation Learning

Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks

no code implementations6 Feb 2023 Shuai Zhang, Meng Wang, Pin-Yu Chen, Sijia Liu, Songtao Lu, Miao Liu

Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and storage costs.

Sparse Learning

Werewolf Among Us: A Multimodal Dataset for Modeling Persuasion Behaviors in Social Deduction Games

no code implementations16 Dec 2022 Bolin Lai, Hongxin Zhang, Miao Liu, Aryan Pariani, Fiona Ryan, Wenqi Jia, Shirley Anugrah Hayati, James M. Rehg, Diyi Yang

We also explore the generalization ability of language models for persuasion modeling and the role of persuasion strategies in predicting social deduction game outcomes.

Persuasion Strategies

Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria

no code implementations28 Oct 2022 Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Gerald Tesauro, Jonathan P. How

By directly comparing active equilibria to Nash equilibria in these examples, we find that active equilibria find more effective solutions than Nash equilibria, concluding that an active equilibrium is the desired solution for multiagent learning settings.

Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach

no code implementations23 Oct 2022 Heshan Fernando, Han Shen, Miao Liu, Subhajit Chaudhury, Keerthiram Murugesan, Tianyi Chen

Machine learning problems with multiple objective functions appear either in learning with multiple criteria where learning has to make a trade-off between multiple performance metrics such as fairness, safety and accuracy; or, in multi-task learning where multiple tasks are optimized jointly, sharing inductive bias between them.

Fairness Inductive Bias +1

In the Eye of Transformer: Global-Local Correlation for Egocentric Gaze Estimation

no code implementations8 Aug 2022 Bolin Lai, Miao Liu, Fiona Ryan, James M. Rehg

To this end, we design the transformer encoder to embed the global context as one additional visual token and further propose a novel Global-Local Correlation (GLC) module to explicitly model the correlation of the global token and each local token.

Gaze Estimation

Generative Adversarial Network for Future Hand Segmentation from Egocentric Video

1 code implementation21 Mar 2022 Wenqi Jia, Miao Liu, James M. Rehg

We introduce the novel problem of anticipating a time series of future hand masks from egocentric video.

Hand Segmentation Image Segmentation +2

Influencing Long-Term Behavior in Multiagent Reinforcement Learning

1 code implementation7 Mar 2022 Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P. How

An effective approach that has recently emerged for addressing this non-stationarity is for each agent to anticipate the learning of other agents and influence the evolution of future policies towards desirable behavior for its own benefit.

reinforcement-learning Reinforcement Learning (RL)

Hierarchical Reinforcement Learning with AI Planning Models

no code implementations1 Mar 2022 JunKyu Lee, Michael Katz, Don Joven Agravante, Miao Liu, Geraud Nangue Tasse, Tim Klinger, Shirin Sohrabi

Our approach defines options in hierarchical reinforcement learning (HRL) from AIP operators by establishing a correspondence between the state transition model of AI planning problem and the abstract state transition system of a Markov Decision Process (MDP).

Decision Making Hierarchical Reinforcement Learning +2

Ego4D: Around the World in 3,000 Hours of Egocentric Video

3 code implementations CVPR 2022 Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei HUANG, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, Jitendra Malik

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.

De-identification Ethics

Learning Multi-Objective Curricula for Robotic Policy Learning

1 code implementation6 Oct 2021 Jikun Kang, Miao Liu, Abhinav Gupta, Chris Pal, Xue Liu, Jie Fu

Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL).

Reinforcement Learning (RL)

Interpreting Reinforcement Policies through Local Behaviors

no code implementations29 Sep 2021 Ronny Luss, Amit Dhurandhar, Miao Liu

Many works in explainable AI have focused on explaining black-box classification models.

Reinforcement Learning (RL)

Context-Specific Representation Abstraction for Deep Option Learning

1 code implementation20 Sep 2021 Marwa Abdulhai, Dong-Ki Kim, Matthew Riemer, Miao Liu, Gerald Tesauro, Jonathan P. How

Hierarchical reinforcement learning has focused on discovering temporally extended actions, such as options, that can provide benefits in problems requiring extensive exploration.

Hierarchical Reinforcement Learning

Egocentric Activity Recognition and Localization on a 3D Map

no code implementations20 May 2021 Miao Liu, Lingni Ma, Kiran Somasundaram, Yin Li, Kristen Grauman, James M. Rehg, Chao Li

Given a video captured from a first person perspective and the environment context of where the video is recorded, can we recognize what the person is doing and identify where the action occurs in the 3D space?

Action Localization Action Recognition +2

4D Human Body Capture from Egocentric Video via 3D Scene Grounding

no code implementations26 Nov 2020 Miao Liu, Dexin Yang, Yan Zhang, Zhaopeng Cui, James M. Rehg, Siyu Tang

We introduce a novel task of reconstructing a time series of second-person 3D human body meshes from monocular egocentric videos.

Time Series Analysis

Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games

no code implementations23 Nov 2020 Tyler Malloy, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro, Chris R. Sims

This paper introduces an information-theoretic constraint on learned policy complexity in the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithm.

Continual Learning Continuous Control +2

A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning

1 code implementation31 Oct 2020 Dong-Ki Kim, Miao Liu, Matthew Riemer, Chuangchuang Sun, Marwa Abdulhai, Golnaz Habibi, Sebastian Lopez-Cot, Gerald Tesauro, Jonathan P. How

A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other simultaneously learning agents.

reinforcement-learning Reinforcement Learning (RL)

Deep RL With Information Constrained Policies: Generalization in Continuous Control

no code implementations9 Oct 2020 Tyler Malloy, Chris R. Sims, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro

We focus on the model-free reinforcement learning (RL) setting and formalize our approach in terms of an information-theoretic constraint on the complexity of learned policies.

Continuous Control reinforcement-learning +1

In the Eye of the Beholder: Gaze and Actions in First Person Video

no code implementations31 May 2020 Yin Li, Miao Liu, James M. Rehg

Moving beyond the dataset, we propose a novel deep model for joint gaze estimation and action recognition in FPV.

Action Recognition Gaze Estimation

Coagent Networks Revisited

1 code implementation28 Jan 2020 Modjtaba Shokrian Zini, Mohammad Pedramfar, Matthew Riemer, Miao Liu

This work is aiming to discuss and close some of the gaps in the literature on models using options (and more generally coagents).

On the Role of Weight Sharing During Deep Option Learning

no code implementations31 Dec 2019 Matthew Riemer, Ignacio Cases, Clemens Rosenbaum, Miao Liu, Gerald Tesauro

In this work we note that while this key assumption of the policy gradient theorems of option-critic holds in the tabular case, it is always violated in practice for the deep function approximation setting.

Atari Games

Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video

1 code implementation ECCV 2020 Miao Liu, Siyu Tang, Yin Li, James Rehg

Motivated by this, we adopt intentional hand movement as a future representation and propose a novel deep network that jointly models and predicts the egocentric hand motion, interaction hotspots and future action.

Action Anticipation Human-Object Interaction Detection

CAPACITY-LIMITED REINFORCEMENT LEARNING: APPLICATIONS IN DEEP ACTOR-CRITIC METHODS FOR CONTINUOUS CONTROL

no code implementations25 Sep 2019 Tyler James Malloy, Matthew Riemer, Miao Liu, Tim Klinger, Gerald Tesauro, Chris R. Sims

We formalize this type of bounded rationality in terms of an information-theoretic constraint on the complexity of policies that agents seek to learn.

Continuous Control reinforcement-learning +1

Attention Distillation for Learning Video Representations

no code implementations5 Apr 2019 Miao Liu, Xin Chen, Yun Zhang, Yin Li, James M. Rehg

To this end, we make use of attention modules that learn to highlight regions in the video and aggregate features for recognition.

Action Recognition Video Recognition

Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

1 code implementation ICLR 2019 Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro

In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples.

Continual Learning Meta-Learning

Learning Abstract Options

no code implementations NeurIPS 2018 Matthew Riemer, Miao Liu, Gerald Tesauro

Building systems that autonomously create temporal abstractions from data is a key challenge in scaling learning and planning in reinforcement learning.

In the Eye of Beholder: Joint Learning of Gaze and Actions in First Person Video

no code implementations ECCV 2018 Yin Li, Miao Liu, James M. Rehg

We address the task of jointly determining what a person is doing and where they are looking based on the analysis of video captured by a headworn camera.

Action Recognition Gaze Estimation +1

Learning to Teach in Cooperative Multiagent Reinforcement Learning

no code implementations20 May 2018 Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How

The problem of teaching to improve agent learning has been investigated by prior works, but these approaches make assumptions that prevent application of teaching to general multiagent problems, or require domain expertise for problems they can apply to.

reinforcement-learning Reinforcement Learning (RL)

Faster Reinforcement Learning with Expert State Sequences

no code implementations ICLR 2018 Xiaoxiao Guo, Shiyu Chang, Mo Yu, Miao Liu, Gerald Tesauro

In this paper, we consider a realistic and more difficult sce- nario where a reinforcement learning agent only has access to the state sequences of an expert, while the expert actions are not available.

Imitation Learning reinforcement-learning +1

The Eigenoption-Critic Framework

no code implementations11 Dec 2017 Miao Liu, Marlos C. Machado, Gerald Tesauro, Murray Campbell

Eigenoptions (EOs) have been recently introduced as a promising idea for generating a diverse set of options through the graph Laplacian, having been shown to allow efficient exploration.

Efficient Exploration Hierarchical Reinforcement Learning +1

Eigenoption Discovery through the Deep Successor Representation

no code implementations ICLR 2018 Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, Murray Campbell

Options in reinforcement learning allow agents to hierarchically decompose a task into subtasks, having the potential to speed up learning and planning.

Atari Games reinforcement-learning +2

Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions

no code implementations24 Jul 2017 Miao Liu, Kavinayan Sivakumar, Shayegan Omidshafiei, Christopher Amato, Jonathan P. How

We implement two variants of multi-robot Search and Rescue (SAR) domains (with and without obstacles) on hardware to demonstrate the learned policies can effectively control a team of distributed robots to cooperate in a partially observable stochastic environment.

Decision Making Decision Making Under Uncertainty

Socially Aware Motion Planning with Deep Reinforcement Learning

2 code implementations26 Mar 2017 Yu Fan Chen, Michael Everett, Miao Liu, Jonathan P. How

For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e. g., passing on the right).

Autonomous Navigation Motion Planning +3

Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning

no code implementations26 Sep 2016 Yu Fan Chen, Miao Liu, Michael Everett, Jonathan P. How

Finding feasible, collision-free paths for multiagent systems can be challenging, particularly in non-communicating scenarios where each agent's intent (e. g. goal) is unobservable to the others.

Multiagent Systems

Stick-Breaking Policy Learning in Dec-POMDPs

no code implementations1 May 2015 Miao Liu, Christopher Amato, Xuejun Liao, Lawrence Carin, Jonathan P. How

Expectation maximization (EM) has recently been shown to be an efficient algorithm for learning finite-state controllers (FSCs) in large decentralized POMDPs (Dec-POMDPs).

Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture

1 code implementation NeurIPS 2013 Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin

This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters.

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