Yuandong Tian web metrics

Research Scientist and Senior Manager
Meta AI (FAIR)
Email: yuandong [at] meta [dot] com

Brief Bio

Yuandong Tian is a Research Scientist and Senior Manager in Meta AI Research (FAIR), working on reinforcement learning, representation learning and optimization. He is the first-author recipient of 2021 ICML Outstanding Paper Honorable Mentions and 2013 ICCV Marr Prize Honorable Mentions , and is the lead scientist and engineer for project. He also receives 2022 CGO Distinguished Paper Award . Prior to that, he worked in Google Self-driving Car team in 2013-2014 and received a Ph.D in Robotics Institute, Carnegie Mellon University in 2013.

Google Scholar and CV.

Research Directions: Reinforcement Learning and Optimization, Representation Learning


[Sep. 21, 2023] 3 papers ( ) are accepted in NeurIPS2023!

[May. 02, 2023] 1 papers () is accepted in ACL2023!

[Apr. 24, 2023] 4 papers ( ) are accepted in ICML2023!

[Mar. 02, 2023] Invited talk in Microsoft on Long-form Story Generation (, ). Slides here.

[Feb. 28, 2023] Invited talk in IPAM about AI-guided optimization (, ). Slides here.

[Feb. 17, 2023] 1 papers () is accepted in MLSys2023!

[Jan. 25, 2023] 1 papers () is accepted in CPAIOR2023!

[Jan. 20, 2023] 3 papers ( ) are accepted in ICLR2023!

[Oct. 17, 2022] 1 papers () is accepted in HPCA2023!

[Oct. 06, 2022] 1 papers () is accepted in EMNLP2022!

[Oct. 03, 2022] Invited talk in MIT Poggio's lab about recent works on contrastive learning (, ). Slides here.

[Sep. 14, 2022] 2 papers ( ) are accepted in NeurIPS2022!

[Sep. 13, 2022] Co-organize AAAI'23 workshop "Reinforcement Learning Ready for Production".

[Aug. 21, 2022] Keynote talk in IEEE Conference on Games.

[Aug. 04, 2022] Invited talk in TTIC workshop on representation learning theory. Link

[Jun. 08, 2022] Invited talk in VALSE Webinar about SSL.

[May. 18, 2022] 1 papers () is accepted in KDD2022!

[May. 15, 2022] 1 papers () is accepted in ICML2022!

[Apr. 28, 2022] Guest lecture in Tianqi Chen's group in CMU.

[Apr. 07, 2022] Invited talk in UIUC about representation learning.

[Mar. 05, 2022] 1 papers () is accepted in CVPR2022!

[Jan. 24, 2022] 3 papers ( ) are accepted in ICLR2022!

[Nov. 29, 2021] 1 papers () is accepted in AAAI2022!

[Nov. 05, 2021] 1 papers () is accepted in CGO2022!

[Sep. 28, 2021] 4 papers ( ) are accepted in NeurIPS2021!

[Jul. 19, 2021] Our paper got ICML Outstanding Paper Award Honorable Mention!

[Jun. 04, 2021] Invited talk in University of Washington NeuralAI Lab about . Slides here. Thanks Eli Shlizerman for inviting!

[May. 08, 2021] 3 papers ( ) are accepted in ICML2021!

[Apr. 29, 2021] 1 papers () is accepted in SIGCOMM2021!

[Apr. 21, 2021] Invited talk in VALSE Webinar about understanding self-supervised learning. Slides here

[Apr. 21, 2021] Invited Guest Lecture in UPenn (Thanks Jing Li) for the invitation. Slides here.

[Apr. 12, 2021] Invited Talk in UCL DARK Lab. Slides here.

[Feb. 28, 2021] 2 papers ( ) are accepted in CVPR2021!

[Jan. 31, 2021] In Black-box optimization challenge of NeurIPS'20, two teams extended our and won 3rd and 8th place! See their reports (JetBrains, KAIST).

[Jan. 22, 2021] 1 papers () is accepted in AIStats2021!

[Dec. 12, 2020] Invited talk in NeurIPS 2020 workshop of Learning meets Combinatorial Algorithms.

[Dec. 12, 2020] Contributed talk in NeurIPS 2020 workshop of Self-supervised Learning, Theory and Practice.

[Nov. 30, 2020] Invited talk (Slides) at Workshop of Reinforcement Learning from Batch Data and Simulation in Simons Institute of UC Berkeley.

[Oct. 20, 2020] Invited Guest lecture in University of Wisconsin Madison (Class syllabus).

[Oct. 14, 2020] Distinguished Guest lecture in IIIS, Tsinghua University.

[Jun. 06, 2020] Invited guest lecture in UCLA.

[Nov. 07, 2019] Invited talk in IAS "Workshop on New Directions in Reinforcement Learning and Control" in Princeton University.

[Nov. 06, 2019] Invited talk in NEC Laboratories Princeton.

[Oct. 27, 2019] Invited talk in AI Sys Workshop in SOSP'19

[Jun. 03, 2019] Long oral talk about in ICML 2019.

[Jan. 03, 2019] Talks in Deep Learning Summit, AAAI 2019 Workshops (Reproducible AI and Game and Environments in Artificial Intelligence).

[Jun. 01, 2018] Multiple talks in Stanford, AI NextCon, etc. link

[Dec. 20, 2017] Keynote at Future Leaders of AI Retreat (FLAIR), Shanghai. Slides here.

[Dec. 06, 2017] Oral talk about platform, NIPS 2017, Long Beach. Slides link.

[Nov. 05, 2017] DRL and Game Tutorial in AI Frontier, Santa Clara. Slides link.

[Oct. 27, 2017] DRL and Game Tutorial in Mountain View, ACMMM 2017. Slides link.

[Aug. 10, 2017] Presentation in Video Games and Machine Learning VGML Workshop, ICML 2017. Slides here. The same talk is also presented in University of Sydney on Aug. 11, hosted by Dong Xu.

[Jul. 03, 2017] On topic "AI In Games: Achievements and Challenges", giving 5 talks in China (CASIA, Tsinghua, Shanghai Tech, Brain-AI workshop and CCF-GAIR 2017) located in Beijing, Shanghai and Shenzhen. Slides here.

JoMA: Demystifying Multilayer Transformers via JOint Dynamics of MLP and Attention [link]
Yuandong Tian, Yiping Wang, Zhenyu Zhang, Beidi Chen, Simon Du

arXiv 2023

H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models [link]
Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Ré, Clark Barrett, Zhangyang Wang, Beidi Chen

NeurIPS 2023

Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformer [link] [talk] [slides]
Yuandong Tian, Yiping Wang, Beidi Chen, Simon Du

NeurIPS 2023

Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information [link] [code]
Arman Zharmagambetov, Brandon Amos, Aaron Ferber, Taoan Huang, Bistra Dilkina, Yuandong Tian

NeurIPS 2023

RLCD: Reinforcement Learning from Contrast Distillation for Language Model Alignment [link] [code]
Kevin Yang, Dan Klein, Asli Celikyilmaz, Nanyun Peng, Yuandong Tian

arXiv 2023

Extending Context Window of Large Language Models via Positional Interpolation [link]
Shouyuan Chen, Sherman Wong, Liangjian Chen, Yuandong Tian

arXiv 2023

DOC: Improving Long Story Coherence With Detailed Outline Control [link] [code]
Kevin Yang, Dan Klein, Nanyun Peng, Yuandong Tian

ACL 2023

Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time [link]
Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Re, Beidi Chen

ICML 2023 (Oral)

Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning [link]
Taoan Huang, Aaron Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner

ICML 2023

SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems [link] [Slides]
Aaron Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner, Bistra Dilkina, Yuandong Tian

ICML 2023

Learning Compiler Pass Orders using Coreset and Normalized Value Prediction [link]
Youwei Liang*, Kevin Stone*, Ali Shameli, Chris Cummins, Mostafa Elhoushi, Jiadong Guo, Benoit Steiner, Xiaomeng Yang, Pengtao Xie, Hugh Leather, Yuandong Tian
(* = Equal 1st authors)

ICML 2023

Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models
Daochen Zha, Louis Feng, Liang Luo, Bhargav Bhushanam, Zirui Liu, Yusuo Hu, Jade Nie, Yuzhen Huang, Yuandong Tian, Arun Kejariwal, Xia Hu

MLSys 2023

Local Branching Relaxation Heuristics for Integer Linear Programs [link]
Taoan Huang, Aaron Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner


Efficient Planning in a Compact Latent Action Space [link] [code] [website]
Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, Yuandong Tian

ICLR 2023

Understanding the Role of Nonlinearity in Training Dynamics of Contrastive Learning [link] [code] [workshop version] [workshop poster] [5min talk]
Yuandong Tian

ICLR 2023

MACTA: A Multi-agent Reinforcement Learning Approach for Cache Timing Attacks and Detection [link]
Jiaxun Cui, Xiaomeng Yang*, Geunbae Lee*, Mulong Luo*, Peter Stone, Hsien-Hsin S. Lee, Benjamin Lee, G. Edward Suh, Wenjie Xiong**, Yuandong Tian**
(* = Equal 2nd authors, ** = Equal advising)

ICLR 2023

Modeling Scattering Coefficients using Self-Attentive Complex Polynomials with Image-based Representation [link]
Andrew Cohen*, Weiping Dou, Jiang Zhu, Slawomir Koziel, Peter Renner, Jan-Ove Mattsson, Xiaomeng Yang, Beidi Chen, Kevin Stone, Yuandong Tian*
(* = Equal 1st authors)

arXiv 2023

AutoCAT: Reinforcement Learning for Automated Exploration of Cache Timing-Channel Attacks [link] [code]
Mulong Luo*, Wenjie Xiong*, Geunbae Lee, Yueying Li, Xiaomeng Yang, Amy Zhang, Yuandong Tian, Hsien Hsin S Lee, G Edward Suh
(* = Equal 1st authors)

HPCA 2023

Re3: Generating Longer Stories With Recursive Reprompting and Revision [link] [code]
Kevin Yang, Yuandong Tian, Nanyun Peng, Dan Klein

EMNLP 2022

Understanding Deep Contrastive Learning via Coordinate-wise Optimization [link] [code] [video] [5min talk slides] [poster]
Yuandong Tian

NeurIPS 2022 (Oral)

DreamShard: Generalizable Embedding Table Placement for Recommender Systems [link] [code]
Daochen Zha, Louis Feng, Qiaoyu Tan, Zirui Liu, Kwei-Herng Lai, Bhargav Bhushanam, Yuandong Tian, Arun Kejariwal, Xia Hu

NeurIPS 2022

AutoShard: Automated Embedding Table Sharding for Recommender Systems [link] [code]
Daochen Zha, Louis Feng, Bhargav Bhushanam, Dhruv Choudhary, Jade Nie, Yuandong Tian, Jay Chae, Yinbin Ma, Arun Kejariwal, Xia Hu

KDD 2022

Denoised MDPs: Learning World Models Better Than the World Itself [link] [code] [website]
Tongzhou Wang, Simon S Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian

ICML 2022

On the Importance of Asymmetry for Siamese Representation Learning [link] [code]
Xiao Wang*, Haoqi Fan*, Yuandong Tian, Daisuke Kihara, Xinlei Chen
(* = Equal 1st authors)

CVPR 2022

Understanding Dimensional Collapse in Contrastive Self-supervised Learning [link] [code]
Li Jing, Pascal Vincent, Yann LeCun, Yuandong Tian

ICLR 2022

NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training [link] [code]
Chengyue Gong, Dilin Wang, Meng Li, Xinlei Chen, Zhicheng Yan, Yuandong Tian, Vikas Chandra

ICLR 2022

Multi-objective Optimization by Learning Space Partitions [link] [code]
Yiyang Zhao, Linnan Wang, Kevin Yang, Tianjun Zhang, Tian Guo, Yuandong Tian

ICLR 2022

Towards demystifying representation learning with non-contrastive self-supervision [link]
Xiang Wang, Xinlei Chen, Simon S Du, Yuandong Tian

arXiv 2021

Sample-Efficient Neural Architecture Search by Learning Action Space [link] [code]
Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, Yuandong Tian

T-PAMI 2021

Learning Bounded Context-Free-Grammar via LSTM and the Transformer: Difference and Explanations [link] [code]
Hui Shi, Sicun Gao, Yuandong Tian, Xinyun Chen, Jishen Zhao

AAAI 2022

CompilerGym: robust, performant compiler optimization environments for AI research [link] [code]
Chris Cummins, Bram Wasti, Jiadong Guo, Brandon Cui, Jason Ansel, Sahir Gomez, Somya Jain, Jia Liu, Olivier Teytaud, Benoit Steiner, Yuandong Tian, Hugh Leather

CGO 2022 (Outstanding Paper)

NovelD: A Simple yet Effective Exploration Criterion [link] [code] [video]
Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian

NeurIPS 2021

MADE: Exploration via Maximizing Deviation from Explored Regions [link] [code]
Tianjun Zhang*, Paria Rashidinejad*, Jiantao Jiao, Yuandong Tian, Joseph Gonzalez, Stuart Russell
(* = Equal 1st authors)

NeurIPS 2021

Learning Space Partitions for Path Planning [link] [code]
Kevin Yang*, Tianjun Zhang*, Chris Cummins, Brandon Cui, Benoit Steiner, Linnan Wang, Joseph E. Gonzalez, Dan Klein, Yuandong Tian
(* = Equal 1st authors)

NeurIPS 2021

Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages [link] [code]
Xinyun Chen, Dawn Song, Yuandong Tian

NeurIPS 2021

Understanding Self-supervised Learning Dynamics without Contrastive Pairs [link] [code] [video] [Slides] [Blogpost]
Yuandong Tian, Xinlei Chen, Surya Ganguli

ICML 2021 (Outstanding Paper Award Honorable Mention)

Few-shot Neural Architecture Search [link] [code] [Blogpost]
Yiyang Zhao*, Linnan Wang*, Yuandong Tian, Rodrigo Fonseca, Tian Guo
(* = Equal 1st authors)

ICML 2021 (Long Oral)

Learn-to-Share: A Hardware-friendly Transfer Learning Framework Exploiting Computation and Parameter Sharing [link]
Cheng Fu, Hanxian Huang, Xinyun Chen, Yuandong Tian, Jishen Zhao (UCSD)

ICML 2021 (Long Oral)

Network Planning with Deep Reinforcement Learning [link] [code]
Hang Zhu (JHU), Varun Gupta, Satyajeet Singh Ahuja, Yuandong Tian, Ying Zhang, Xin Jin


FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining [link]
Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Bichen Wu, Zijian He, Zhen Wei, Kan Chen, Yuandong Tian, Matthew Yu, Peter Vajda, Joseph Gonzalez

CVPR 2021

FPNAS: Fast Probabilistic Neural Architecture Search [link]
Zhicheng Yan, Xiaoliang Dai, Peizhao Zhang, Yuandong Tian, Bichen Wu, Matt Feiszli

CVPR 2021

Understanding Robustness in Teacher-Student Setting: A New Perspective [link] [Slides]
Zhuolin Yang*, Zhaoxi Chen, Tiffany Cai, Xinyun Chen, Bo Li, Yuandong Tian*
(* = Equal 1st authors)

AIStats 2021

Multi-Agent Collaboration via Reward Attribution Decomposition [link] [code] [video] [website]
Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian

arxiv 2020

Joint Policy Search for Multi-agent Collaboration with Imperfect Information [link] [code] [video]
Yuandong Tian, Qucheng Gong, Tina Jiang

NeurIPS 2020

Understanding Self-supervised Learning with Dual Deep Networks [link] [code] [video]
Yuandong Tian, Lantao Yu, Xinlei Chen, Surya Ganguli

arXiv 2020

Student Specialization in Deep ReLU Networks With Finite Width and Input Dimension [link] [code]
Yuandong Tian

ICML 2020

Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP [link]
Haonan Yu, Sergey Edunov, Yuandong Tian, Ari S. Morcos

ICLR 2020

Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search [link] [code]
Linnan Wang, Rodrigo Fonseca, Yuandong Tian

NeurIPS 2020

Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction [link]
Qingquan Song, Dehua Cheng, Hanning Zhou, Jiyan Yang, Yuandong Tian, Xia Hu

KDD 2020

FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions [link] [code]
Alvin Wan, Xiaoliang Dai, Peizhao Zhang, Zijian He, Yuandong Tian, Saining Xie, Bichen Wu, Matthew Yu, Tao Xu, Kan Chen, Peter Vajda, Joseph Gonzalez

CVPR 2020

N-Bref : A High-fidelity Decompiler Exploiting Programming Structures [link] [code] [Blogpost]
Cheng Fu, Kunlin Yang, Xinyun Chen, Yuandong Tian, Jishen Zhao

arxiv 2020

AlphaX: Exploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search [link]
Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca

AAAI 2020

Deep Symbolic Superoptimization Without Human Knowledge [link] [code]
Hui Shi, Yang Zhang, Xinyun Chen, Yuandong Tian, Jishen Zhao

ICLR 2020

Hierarchical Decision Making by Generating and Following Natural Language Instructions [link] [code]
Hengyuan Hu*, Denis Yarats*, Qucheng Gong, Yuandong Tian, Mike Lewis
(* = Equal 1st authors)

NeurIPS 2019

ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero [link] [code] [pretrained model and game records] [Other resources] [Blogpost] [Talk]

An open source reimplementation of DeepMind's zero-knowledge training and its application to the game of Go. Trained on 2000 GPUs for 9 days. With a single GPU and 50 seconds per move, the model won 20-0 versus 4 top 30 professional players, given human unlimited thinking time. It also won 980-18 versus LeelaZero (version Apr. 25).

Yuandong Tian, Jerry Ma*, Qucheng Gong*, Shubho Sengupta*, Zhuoyuan Chen, James Pinkerton, Larry Zitnick
(* = Equal 2nd authors)

ICML 2019 (Long Oral)

Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees [link] [code]
Yuping Luo*, Huazhe Xu*, Yuanzhi Li, Yuandong Tian, Trevor Darrell, Tengyu Ma
(* = Equal 1st authors)

ICLR 2019

M^3RL: Mind-aware Multi-agent Management Reinforcement Learning [link] [code]
Tianmin Shu, Yuandong Tian

ICLR 2019

Luck Matters: Understanding Training Dynamics of Deep ReLU Networks [link] [code] [Poster]
Yuandong Tian, Tina Jiang, Qucheng Gong, Ari Morcos

ICML-workshop 2019

One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers [link]
Ari S. Morcos, Haonan Yu, Michela Paganini, Yuandong Tian

NeurIPS 2019

Real-world video adaptation with reinforcement learning [link]
Hongzi Mao, Shannon Chen, Drew Dimmery, Shaun Singh, Drew Blaisdell, Yuandong Tian, Mohammad Alizadeh, Eytan Bakshy

ICML-Workshop 2019

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search [link] [code]
Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, Kurt Keutzer

CVPR 2019

Learning to Perform Local Rewriting for Combinatorial Optimization [link] [code]
Xinyun Chen, Yuandong Tian

NeurIPS 2019

Coda: An End-to-End Neural Program Decompiler [link]
Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao

NeurIPS 2019

Building Generalizable Agents with a Realistic and Rich 3D Environment [link] [code]
Yi Wu, Yuxin Wu, Georgia Gkioxari, Yuandong Tian

ICLR-Workshop 2018

A Theoretical Framework for Deep Locally Connected ReLU Network [link] [Poster]
Yuandong Tian

arxiv 2018

Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima [link]
Simon S. Du, Jason D. Lee, Yuandong Tian, Barnabas Poczos, Aarti Singh

ICML 2018 (Long Oral)

When is a Convolutional Filter Easy To Learn? [link]
Simon S. Du, Jason D. Lee, Yuandong Tian

ICLR 2018

ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games [link] [code] [video]
Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu, Larry Zitnick

NeurIPS 2017 (Oral)

Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning [link]
Yuxin Wu, Yuandong Tian

ICLR 2017

An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis [link] [code]
Yuandong Tian

ICML 2017

Semantic Amodal Segmentation [link]
Yan Zhu, Yuandong Tian, Dimitris Mexatas, Piotr Dollár

CVPR 2017

Better Computer Go Player with Neural Network and Long-term Prediction [link] [code] [pretrained model] [mit tech review]
Yuandong Tian, Yan Zhu

ICLR 2016

Single Image 3D Interpreter Network [link]
Jiajun Wu*, Tianfan Xue*, Joseph J. Lim, Yuandong Tian, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman
(* = Equal 1st authors)

ECCV 2016 (Oral)

Simple Baseline for Visual Question Answering [link] [code]
Bolei Zhou, Yuandong Tian, Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus

arxiv 2016

Theory and Practice of Hierarchical Data-driven Descent for Optimal Deformation Estimation [link]
Yuandong Tian, Srinivasa G. Narasimhan

IJCV 2015

Theory and Practice of Globally Optimal Deformation Estimation [link]
Yuandong Tian

PhD thesis 2013

Hierarchical Data-Driven Descent for Efficient Optimal Deformation Estimation [link] [Proofs]
Yuandong Tian, Srinivasa G. Narasimhan

ICCV 2013 (Marr Prize Honorable Mentions)

Integrating Perceptual Learning with External World Knowledge in a Simulated Student [link]
Nan Li, Yuandong Tian, William W. Cohen, Ken Koedinger

AIED 2013

Exploring the Spatial Hierarchy of Mixture Models for Human Pose Estimation [link] [code] [website]
Yuandong Tian, Larry Zitnick, Srinivasa G. Narasimhan

ECCV 2012

Learning from Crowds in the Presence of Schools of Thought [link] [code] [Slides] [Dataset]
Yuandong Tian, Jun Zhu

KDD 2012

Depth from Optical Turbulence [link] [website]
Yuandong Tian, Srinivasa G. Narasimhan, Alan J. Vannevel

CVPR 2012

A Combined Theory of Defocused Illumination and Global Light Transport [link]
Mohit Gupta, Yuandong Tian, Srinivasa G. Narasimhan, Li Zhang

IJCV 2011

Globally Optimal Estimation of Nonrigid Image Distortion [link]
Yuandong Tian, Srinivasa G. Narasimhan

IJCV 2011

Rectification and 3D reconstruction of Curved Document Images [link] [website]
Yuandong Tian, Srinivasa G. Narasimhan

CVPR 2011 (Oral)

Local Isomorphism to Solve the Pre-image Problem in Kernel Methods [link]
Dong Huang, Yuandong Tian, Fernando De la Torre

CVPR 2011

A Globally Optimal Data-Driven Approach for Image Distortion Estimation [link] [website]
Yuandong Tian, Srinivasa G. Narasimhan

CVPR 2010 (Oral)

Seeing through water: Image restoration using model-based tracking [link] [website]
Yuandong Tian, Srinivasa G. Narasimhan

ICCV 2009

(De) Focusing on Global Light Transport for Active Scene Recovery [link]
Mohit Gupta, Yuandong Tian, Srinivasa G. Narasimhan, Li Zhang

CVPR 2009 (Oral)