Search Results for author: Fei Fang

Found 52 papers, 21 papers with code

Multi-defender Security Games with Schedules

no code implementations28 Nov 2023 Zimeng Song, Chun Kai Ling, Fei Fang

We show that unlike prior work on multi-defender security games, the introduction of schedules can cause non-existence of equilibrium even under rather restricted environments.

RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization

no code implementations6 Nov 2023 Mateo Dulce Rubio, Siqi Zeng, Qi Wang, Didier Alvarado, Francisco Moreno, Hoda Heidari, Fei Fang

Landmines remain a threat to war-affected communities for years after conflicts have ended, partly due to the laborious nature of demining tasks.

Feature Engineering Humanitarian +1

DiffDance: Cascaded Human Motion Diffusion Model for Dance Generation

no code implementations5 Aug 2023 Qiaosong Qi, Le Zhuo, Aixi Zhang, Yue Liao, Fei Fang, Si Liu, Shuicheng Yan

To address these limitations, we present a novel cascaded motion diffusion model, DiffDance, designed for high-resolution, long-form dance generation.

Representation Learning Super-Resolution

NewsPanda: Media Monitoring for Timely Conservation Action

1 code implementation30 Apr 2023 Sedrick Scott Keh, Zheyuan Ryan Shi, David J. Patterson, Nirmal Bhagabati, Karun Dewan, Areendran Gopala, Pablo Izquierdo, Debojyoti Mallick, Ambika Sharma, Pooja Shrestha, Fei Fang

We introduce NewsPanda, a toolkit which automatically detects and analyzes online articles related to environmental conservation and infrastructure construction.

Active Learning

Bi-level Latent Variable Model for Sample-Efficient Multi-Agent Reinforcement Learning

no code implementations12 Apr 2023 Aravind Venugopal, Stephanie Milani, Fei Fang, Balaraman Ravindran

At the bottom level, it learns latent representations for each agent, given the global latent representations from the top level.

reinforcement-learning SMAC+

Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games

no code implementations2 Mar 2023 Stephanie Milani, Arthur Juliani, Ida Momennejad, Raluca Georgescu, Jaroslaw Rzpecki, Alison Shaw, Gavin Costello, Fei Fang, Sam Devlin, Katja Hofmann

We aim to understand how people assess human likeness in navigation produced by people and artificially intelligent (AI) agents in a video game.


Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation

no code implementations28 Feb 2023 Guoqiang Sun, Yibin Shen, Sijin Zhou, Xiang Chen, Hongyan Liu, Chunming Wu, Chenyi Lei, Xianhui Wei, Fei Fang

In this paper, we propose a cross-domain recommendation method: Self-supervised Interest Transfer Network (SITN), which can effectively transfer invariant knowledge between domains via prototypical contrastive learning.

Contrastive Learning

Safe Subgame Resolving for Extensive Form Correlated Equilibrium

no code implementations29 Dec 2022 Chun Kai Ling, Fei Fang

Correlated Equilibrium is a solution concept that is more general than Nash Equilibrium (NE) and can lead to outcomes with better social welfare.

Function Approximation for Solving Stackelberg Equilibrium in Large Perfect Information Games

1 code implementation29 Dec 2022 Chun Kai Ling, J. Zico Kolter, Fei Fang

Function approximation (FA) has been a critical component in solving large zero-sum games.

Neighborhood Adaptive Estimators for Causal Inference under Network Interference

no code implementations7 Dec 2022 Alexandre Belloni, Fei Fang, Alexander Volfovsky

In contrast to previous work, the proposed procedure aims to approximate the relevant network interference patterns.

Causal Inference Feature Engineering

Explainable Action Advising for Multi-Agent Reinforcement Learning

1 code implementation15 Nov 2022 Yue Guo, Joseph Campbell, Simon Stepputtis, Ruiyu Li, Dana Hughes, Fei Fang, Katia Sycara

This allows the student to self-reflect on what it has learned, enabling advice generalization and leading to improved sample efficiency and learning performance - even in environments where the teacher is sub-optimal.

Multi-agent Reinforcement Learning reinforcement-learning +2

Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation

1 code implementation18 Oct 2022 Peide Huang, Mengdi Xu, Jiacheng Zhu, Laixi Shi, Fei Fang, Ding Zhao

Curriculum Reinforcement Learning (CRL) aims to create a sequence of tasks, starting from easy ones and gradually learning towards difficult tasks.

Domain Adaptation reinforcement-learning +1

Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation

no code implementations24 Aug 2022 Yuanliang Zhang, XiaoFeng Wang, Jinxin Hu, Ke Gao, Chenyi Lei, Fei Fang

we summarize three practical challenges which are not well solved for multi-scenario modeling: (1) Lacking of fine-grained and decoupled information transfer controls among multiple scenarios.

Contrastive Learning Disentanglement +1

Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao

no code implementations11 Aug 2022 Lixin Liu, Yanling Wang, Tianming Wang, Dong Guan, Jiawei Wu, Jingxu Chen, Rong Xiao, Wenxiang Zhu, Fei Fang

Therefore, it is crucial to perform cross-domain CTR prediction to transfer knowledge from large domains to small domains to alleviate the data sparsity issue.

Click-Through Rate Prediction Recommendation Systems +1

Tradeoffs in Preventing Manipulation in Paper Bidding for Reviewer Assignment

no code implementations22 Jul 2022 Steven Jecmen, Nihar B. Shah, Fei Fang, Vincent Conitzer

Many conferences rely on paper bidding as a key component of their reviewer assignment procedure.

A Dataset on Malicious Paper Bidding in Peer Review

1 code implementation24 Jun 2022 Steven Jecmen, Minji Yoon, Vincent Conitzer, Nihar B. Shah, Fei Fang

The performance of these detection algorithms can be taken as a baseline for future research on detecting malicious bidding.


Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning

1 code implementation18 May 2022 Fei Fang, Kunal Sinha, Noah D. Goodman, Christopher Potts, Elisa Kreiss

It seems likely that these patterns are shaped by the environment a speaker is exposed to in complex ways.

Language Acquisition

Ranked Prioritization of Groups in Combinatorial Bandit Allocation

1 code implementation11 May 2022 Lily Xu, Arpita Biswas, Fei Fang, Milind Tambe

Preventing poaching through ranger patrols protects endangered wildlife, directly contributing to the UN Sustainable Development Goal 15 of life on land.

PerfectDou: Dominating DouDizhu with Perfect Information Distillation

1 code implementation30 Mar 2022 Guan Yang, Minghuan Liu, Weijun Hong, Weinan Zhang, Fei Fang, Guangjun Zeng, Yue Lin

To this end, we characterize card and game features for DouDizhu to represent the perfect and imperfect information.

Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training

no code implementations19 Feb 2022 Peide Huang, Mengdi Xu, Fei Fang, Ding Zhao

In this paper, we introduce a novel hierarchical formulation of robust RL - a general-sum Stackelberg game model called RRL-Stack - to formalize the sequential nature and provide extra flexibility for robust training.

reinforcement-learning Reinforcement Learning (RL)

A Survey of Explainable Reinforcement Learning

no code implementations17 Feb 2022 Stephanie Milani, Nicholay Topin, Manuela Veloso, Fei Fang

In this survey, we propose a novel taxonomy for organizing the XRL literature that prioritizes the RL setting.

Decision Making reinforcement-learning +1

Efficiency, Fairness, and Stability in Non-Commercial Peer-to-Peer Ridesharing

no code implementations4 Oct 2021 Hoon Oh, Yanhan Tang, Zong Zhang, Alexandre Jacquillat, Fei Fang

Unlike commercial ridesharing, non-commercial peer-to-peer (P2P) ridesharing has been subject to limited research -- although it can promote viable solutions in non-urban communities.


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.

Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design

1 code implementation13 Aug 2021 Steven Jecmen, Hanrui Zhang, Ryan Liu, Fei Fang, Vincent Conitzer, Nihar B. Shah

Many scientific conferences employ a two-phase paper review process, where some papers are assigned additional reviewers after the initial reviews are submitted.

Robust Reinforcement Learning Under Minimax Regret for Green Security

1 code implementation15 Jun 2021 Lily Xu, Andrew Perrault, Fei Fang, Haipeng Chen, Milind Tambe

We formulate the problem as a game between the defender and nature who controls the parameter values of the adversarial behavior and design an algorithm MIRROR to find a robust policy.

Decision Making reinforcement-learning +1

Concadia: Towards Image-Based Text Generation with a Purpose

1 code implementation16 Apr 2021 Elisa Kreiss, Fei Fang, Noah D. Goodman, Christopher Potts

Current deep learning models often achieve excellent results on benchmark image-to-text datasets but fail to generate texts that are useful in practice.

Image Captioning Text Generation

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.

Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods

no code implementations25 Feb 2021 Nicholay Topin, Stephanie Milani, Fei Fang, Manuela Veloso

Because of this decision tree equivalence, any function approximator can be used during training, including a neural network, while yielding a decision tree policy for the base MDP.

reinforcement-learning Reinforcement Learning (RL)

Deep Archimedean Copulas

1 code implementation NeurIPS 2020 Chun Kai Ling, Fei Fang, J. Zico Kolter

A central problem in machine learning and statistics is to model joint densities of random variables from data.

Dual-Mandate Patrols: Multi-Armed Bandits for Green Security

2 code implementations14 Sep 2020 Lily Xu, Elizabeth Bondi, Fei Fang, Andrew Perrault, Kai Wang, Milind Tambe

Conservation efforts in green security domains to protect wildlife and forests are constrained by the limited availability of defenders (i. e., patrollers), who must patrol vast areas to protect from attackers (e. g., poachers or illegal loggers).

Multi-Armed Bandits

Bandit Data-Driven Optimization

1 code implementation26 Aug 2020 Zheyuan Ryan Shi, Zhiwei Steven Wu, Rayid Ghani, Fei Fang

In this paper, we introduce bandit data-driven optimization, the first iterative prediction-prescription framework to address these pain points.

BIG-bench Machine Learning

TPG-DNN: A Method for User Intent Prediction Based on Total Probability Formula and GRU Loss with Multi-task Learning

no code implementations5 Aug 2020 Jingxing Jiang, Zhubin Wang, Fei Fang, Binqiang Zhao

Critical as is to improve the online shopping experience for customers and merchants, how to find a proper approach for user intent prediction are paid great attention in both industry and academia.

Multi-Task Learning

Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments

2 code implementations NeurIPS 2020 Steven Jecmen, Hanrui Zhang, Ryan Liu, Nihar B. Shah, Vincent Conitzer, Fei Fang

We further consider the problem of restricting the joint probability that certain suspect pairs of reviewers are assigned to certain papers, and show that this problem is NP-hard for arbitrary constraints on these joint probabilities but efficiently solvable for a practical special case.

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

Artificial Intelligence for Social Good: A Survey

no code implementations7 Jan 2020 Zheyuan Ryan Shi, Claire Wang, Fei Fang

Artificial intelligence for social good (AI4SG) is a research theme that aims to use and advance artificial intelligence to address societal issues and improve the well-being of the world.

AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline

no code implementations16 Dec 2019 Andrew Perrault, Fei Fang, Arunesh Sinha, Milind Tambe

With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems.

Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium

no code implementations NeurIPS 2019 Gabriele Farina, Chun Kai Ling, Fei Fang, Tuomas Sandholm

We show that a regret minimizer can be designed for a scaled extension of any two convex sets, and that from the decomposition we then obtain a global regret minimizer.

Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning

no code implementations10 Sep 2019 Liheng Chen, Hongyi Guo, Yali Du, Fei Fang, Haifeng Zhang, Yaoming Zhu, Ming Zhou, Wei-Nan Zhang, Qing Wang, Yong Yu

Although existing works formulate this problem into a centralized learning with decentralized execution framework, which avoids the non-stationary problem in training, their decentralized execution paradigm limits the agents' capability to coordinate.

Multi-agent Reinforcement Learning reinforcement-learning +1

Dynamic Trip-Vehicle Dispatch with Scheduled and On-Demand Requests

no code implementations20 Jul 2019 Taoan Huang, Bohui Fang, Xiaohui Bei, Fei Fang

Transportation service providers that dispatch drivers and vehicles to riders start to support both on-demand ride requests posted in real time and rides scheduled in advance, leading to new challenges which, to the best of our knowledge, have not been addressed by existing works.

Learning and Planning in the Feature Deception Problem

no code implementations13 May 2019 Zheyuan Ryan Shi, Ariel D. Procaccia, Kevin S. Chan, Sridhar Venkatesan, Noam Ben-Asher, Nandi O. Leslie, Charles Kamhoua, Fei Fang

In order to formally reason about deception, we introduce the feature deception problem (FDP), a domain-independent model and present a learning and planning framework for finding the optimal deception strategy, taking into account the adversary's preferences which are initially unknown to the defender.

Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games

no code implementations11 Mar 2019 Chun Kai Ling, Fei Fang, J. Zico Kolter

With the recent advances in solving large, zero-sum extensive form games, there is a growing interest in the inverse problem of inferring underlying game parameters given only access to agent actions.

Draining the Water Hole: Mitigating Social Engineering Attacks with CyberTWEAK

no code implementations3 Jan 2019 Zheyuan Ryan Shi, Aaron Schlenker, Brian Hay, Daniel Bittleston, Siyu Gao, Emily Peterson, John Trezza, Fei Fang

Cyber adversaries have increasingly leveraged social engineering attacks to breach large organizations and threaten the well-being of today's online users.

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

The Impact of Humanoid Affect Expression on Human Behavior in a Game-Theoretic Setting

1 code implementation10 Jun 2018 Aaron M. Roth, Umang Bhatt, Tamara Amin, Afsaneh Doryab, Fei Fang, Manuela Veloso

In this pilot study, we investigate (1) in what way a robot can express a certain mood to influence a human's decision making behavioral model; (2) how and to what extent the human will be influenced in a game theoretic setting.

Decision Making

What game are we playing? End-to-end learning in normal and extensive form games

1 code implementation7 May 2018 Chun Kai Ling, Fei Fang, J. Zico Kolter

Although recent work in AI has made great progress in solving large, zero-sum, extensive-form games, the underlying assumption in most past work is that the parameters of the game itself are known to the agents.

Designing the Game to Play: Optimizing Payoff Structure in Security Games

no code implementations5 May 2018 Zheyuan Ryan Shi, Ziye Tang, Long Tran-Thanh, Rohit Singh, Fei Fang

We study Stackelberg Security Games where the defender, in addition to allocating defensive resources to protect targets from the attacker, can strategically manipulate the attacker's payoff under budget constraints in weighted L^p-norm form regarding the amount of change.

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