COLT 2023

169 papers

$\ell_p$-Regression in the Arbitrary Partition Model of Communication Yi Li, Honghao Lin, David Woodruff

A Blackbox Approach to Best of Both Worlds in Bandits and Beyond Chris Dann, Chen-Yu Wei, Julian Zimmert

A High-Dimensional Convergence Theorem for U-Statistics with Applications to Kernel-Based Testing Kevin H. Huang, Xing Liu, Andrew Duncan, Axel Gandy

A Lower Bound for Linear and Kernel Regression with Adaptive Covariates Tor Lattimore

A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points Daniel Kane, Ilias Diakonikolas

A New Ranking Scheme for Modern Data and Its Application to Two-Sample Hypothesis Testing Doudou Zhou, Hao Chen

A Pretty Fast Algorithm for Adaptive Private Mean Estimation Rohith Kuditipudi, John Duchi, Saminul Haque

A Second-Order Method for Stochastic Bandit Convex Optimisation Tor Lattimore, András György

A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs Dirk Hoeven, Lukas Zierahn, Tal Lancewicki, Aviv Rosenberg, Nicolò Cesa-Bianchi

Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond David Martínez-Rubio, Elias Wirth, Sebastian Pokutta

Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties David Martínez-Rubio, Sebastian Pokutta

Active Coverage for PAC Reinforcement Learning Aymen Al-Marjani, Andrea Tirinzoni, Emilie Kaufmann

Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian

Algorithmic Gaussianization Through Sketching: Converting Data into Sub-Gaussian Random Designs Michał Dereziński

Algorithmically Effective Differentially Private Synthetic Data Yiyun He, Roman Vershynin, Yizhe Zhu

Allocating Divisible Resources on Arms with Unknown and Random Rewards Wenhao Li, Ningyuan Chen

Approximately Stationary Bandits with Knapsacks Giannis Fikioris, Éva Tardos

Asymptotic Confidence Sets for Random Linear Programs Shuyu Liu, Florentina Bunea, Jonathan Niles-Weed

Backward Feature Correction: How Deep Learning Performs Deep (Hierarchical) Learning Zeyuan Allen-Zhu, Yuanzhi Li

Bagging Is an Optimal PAC Learner Kasper Green Larsen

Bandit Learnability Can Be Undecidable Steve Hanneke, Liu Yang

Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization Spencer Frei, Gal Vardi, Peter Bartlett, Nathan Srebro

Best-of-Three-Worlds Analysis for Linear Bandits with Follow-the-Regularized-Leader Algorithm Fang Kong, Canzhe Zhao, Shuai Li

Best-of-Three-Worlds Linear Bandit Algorithm with Variance-Adaptive Regret Bounds Shinji Ito, Kei Takemura

Beyond Parallel Pancakes: Quasi-Polynomial Time Guarantees for Non-Spherical Gaussian Mixtures Rares-Darius Buhai, David Steurer

Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD Matthew Faw, Litu Rout, Constantine Caramanis, Sanjay Shakkottai

Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation Yuanhao Wang, Qinghua Liu, Yu Bai, Chi Jin

Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation Qiwen Cui, Kaiqing Zhang, Simon Du

Breaking the Lower Bound with (Little) Structure: Acceleration in Non-Convex Stochastic Optimization with Heavy-Tailed Noise Zijian Liu, Jiawei Zhang, Zhengyuan Zhou

Bregman Deviations of Generic Exponential Families Sayak Ray Chowdhury, Patrick Saux, Odalric Maillard, Aditya Gopalan

Causal Matrix Completion Anish Agarwal, Munther Dahleh, Devavrat Shah, Dennis Shen

Community Detection in the Hypergraph SBM: Exact Recovery Given the Similarity Matrix Julia Gaudio, Nirmit Joshi

Complexity of High-Dimensional Identity Testing with Coordinate Conditional Sampling Antonio Blanca, Zongchen Chen, Daniel Štefankovič, Eric Vigoda

Condition-Number-Independent Convergence Rate of Riemannian Hamiltonian Monte Carlo with Numerical Integrators Yunbum Kook, Yin Tat Lee, Ruoqi Shen, Santosh Vempala

Contexts Can Be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms Osama A Hanna, Lin Yang, Christina Fragouli

Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression Aleksandrs Slivkins, Karthik Abinav Sankararaman, Dylan J Foster

Convergence of AdaGrad for Non-Convex Objectives: Simple Proofs and Relaxed Assumptions Bohan Wang, Huishuai Zhang, Zhiming Ma, Wei Chen

Curvature and Complexity: Better Lower Bounds for Geodesically Convex Optimization Christopher Criscitiello, Nicolas Boumal

Detection-Recovery and Detection-Refutation Gaps via Reductions from Planted Clique Guy Bresler, Tianze Jiang

Detection-Recovery Gap for Planted Dense Cycles Cheng Mao, Alexander S. Wein, Shenduo Zhang

Deterministic Nonsmooth Nonconvex Optimization Michael Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis

Differentially Private Algorithms for the Stochastic Saddle Point Problem with Optimal Rates for the Strong Gap Raef Bassily, Cristóbal Guzmán, Michael Menart

Differentially Private and Lazy Online Convex Optimization Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Thakurta

Distribution-Independent Regression for Generalized Linear Models with Oblivious Corruptions Ilias Diakonikolas, Sushrut Karmalkar, Jong Ho Park, Christos Tzamos

Efficient Algorithms for Sparse Moment Problems Without Separation Zhiyuan Fan, Jian Li

Efficient Median of Means Estimator Stanislav Minsker

Empirical Bayes via ERM and Rademacher Complexities: The Poisson Model Soham Jana, Yury Polyanskiy, Anzo Z. Teh, Yihong Wu

Entropic Characterization of Optimal Rates for Learning Gaussian Mixtures Zeyu Jia, Yury Polyanskiy, Yihong Wu

Exploring Local Norms in Exp-Concave Statistical Learning Nikita Puchkin, Nikita Zhivotovskiy

Exponential Hardness of Reinforcement Learning with Linear Function Approximation Sihan Liu, Gaurav Mahajan, Daniel Kane, Shachar Lovett, Gellért Weisz, Csaba Szepesvári

Fast Algorithms for a New Relaxation of Optimal Transport Moses Charikar, Beidi Chen, Christopher Ré, Erik Waingarten

Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions Gavin Brown, Samuel Hopkins, Adam Smith

Find a Witness or Shatter: The Landscape of Computable PAC Learning. Valentino Delle Rose, Alexander Kozachinskiy, Cristóbal Rojas, Tomasz Steifer

Fine-Grained Distribution-Dependent Learning Curves Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya Tolstikhin

Finite-Sample Symmetric Mean Estimation with Fisher Information Rate Shivam Gupta, Jasper C. H. Lee, Eric Price

From High-Dimensional & Mean-Field Dynamics to Dimensionless ODEs: A Unifying Approach to SGD in Two-Layers Networks Luca Arnaboldi, Ludovic Stephan, Florent Krzakala, Bruno Loureiro

From Pseudorandomness to Multi-Group Fairness and Back Cynthia Dwork, Daniel Lee, Huijia Lin, Pranay Tankala

Generalization Error Bounds for Noisy, Iterative Algorithms via Maximal Leakage Ibrahim Issa, Amedeo Roberto Esposito, Michael Gastpar

Generalization Guarantees via Algorithm-Dependent Rademacher Complexity Sarah Sachs, Tim Erven, Liam Hodgkinson, Rajiv Khanna, Umut Şimşekli

Geodesically Convex $m$-Estimation in Metric Spaces Victor-Emmanuel Brunel

Geometric Barriers for Stable and Online Algorithms for Discrepancy Minimization David Gamarnik, Eren C. Kizildağ, Will Perkins, Changji Xu

Hardness of Agnostically Learning Halfspaces from Worst-Case Lattice Problems Stefan Tiegel

Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing Mahdi Soltanolkotabi, Dominik Stöger, Changzhi Xie

Improper Multiclass Boosting Nataly Brukhim, Steve Hanneke, Shay Moran

Improved Bounds for Multi-Task Learning with Trace Norm Regularization Weiwei Liu

Improved Dimension Dependence of a Proximal Algorithm for Sampling Jiaojiao Fan, Bo Yuan, Yongxin Chen

Improved Discretization Analysis for Underdamped Langevin Monte Carlo Shunshi Zhang, Sinho Chewi, Mufan Li, Krishna Balasubramanian, Murat A. Erdogdu

Improved Dynamic Regret for Online Frank-Wolfe Yuanyu Wan, Lijun Zhang, Mingli Song

Inference on Strongly Identified Functionals of Weakly Identified Functions Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara

InfoNCE Loss Provably Learns Cluster-Preserving Representations Advait Parulekar, Liam Collins, Karthikeyan Shanmugam, Aryan Mokhtari, Sanjay Shakkottai

Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane, Puqian Wang, Nikos Zarifis

Information-Directed Selection for Top-Two Algorithms Wei You, Chao Qin, Zihao Wang, Shuoguang Yang

Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory Andrew J. Wagenmaker, Dylan J. Foster

Intrinsic Dimensionality and Generalization Properties of the R-Norm Inductive Bias Navid Ardeshir, Daniel J. Hsu, Clayton H. Sanford

Is Planted Coloring Easier than Planted Clique? Pravesh Kothari, Santosh S Vempala, Alexander S Wein, Jeff Xu

Kernelized Diffusion Maps Loucas Pillaud-Vivien, Francis Bach

Law of Large Numbers for Bayesian Two-Layer Neural Network Trained with Variational Inference Arnaud Descours, Tom Huix, Arnaud Guillin, Manon Michel, Éric Moulines, Boris Nectoux

Learning and Testing Latent-Tree Ising Models Efficiently Vardis Kandiros, Constantinos Daskalakis, Yuval Dagan, Davin Choo

Learning Hidden Markov Models Using Conditional Samples Gaurav Mahajan, Sham Kakade, Akshay Krishnamurthy, Cyril Zhang

Learning Narrow One-Hidden-Layer ReLU Networks Sitan Chen, Zehao Dou, Surbhi Goel, Adam Klivans, Raghu Meka

Limits of Model Selection Under Transfer Learning Steve Hanneke, Samory Kpotufe, Yasaman Mahdaviyeh

Linearization Algorithms for Fully Composite Optimization Maria-Luiza Vladarean, Nikita Doikov, Martin Jaggi, Nicolas Flammarion

List Online Classification Shay Moran, Ohad Sharon, Iska Tsubari, Sivan Yosebashvili

Local Glivenko-Cantelli Doron Cohen, Aryeh Kontorovich

Local Risk Bounds for Statistical Aggregation Jaouad Mourtada, Tomas Vaškevičius, Nikita Zhivotovskiy

Lower Bounds for the Convergence of Tensor Power Iteration on Random Overcomplete Models Yuchen Wu, Kangjie Zhou

Minimax Instrumental Variable Regression and $l_2$ Convergence Guarantees Without Identification or Closedness Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara

Minimax Optimal Testing by Classification Patrik R. Gerber, Yanjun Han, Yury Polyanskiy

Minimizing Dynamic Regret on Geodesic Metric Spaces Zihao Hu, Guanghui Wang, Jacob D. Abernethy

Moments, Random Walks, and Limits for Spectrum Approximation Yujia Jin, Christopher Musco, Aaron Sidford, Apoorv Vikram Singh

Multiclass Online Learning and Uniform Convergence Steve Hanneke, Shay Moran, Vinod Raman, Unique Subedi, Ambuj Tewari

Multitask Learning via Shared Features: Algorithms and Hardness Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan Ullman, Lydia Zakynthinou

Near Optimal Heteroscedastic Regression with Symbiotic Learning Aniket Das, Dheeraj M. Nagaraj, Praneeth Netrapalli, Dheeraj Baby

Near-Optimal Fitting of Ellipsoids to Random Points Aaron Potechin, Paxton M. Turner, Prayaag Venkat, Alexander S. Wein

Non-Asymptotic Convergence Bounds for Sinkhorn Iterates and Their Gradients: A Coupling Approach. Giacomo Greco, Maxence Noble, Giovanni Conforti, Alain Durmus

On a Class of Gibbs Sampling over Networks Bo Yuan, Jiaojiao Fan, Jiaming Liang, Andre Wibisono, Yongxin Chen

On Classification-Calibration of Gamma-Phi Losses Yutong Wang, Clayton Scott

On Testing and Learning Quantum Junta Channels Zongbo Bao, Penghui Yao

On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring Dean Foster, Dylan J. Foster, Noah Golowich, Alexander Rakhlin

On the Existence of a Complexity in Fixed Budget Bandit Identification Rémy Degenne

On the Lower Bound of Minimizing Polyak-Łojasiewicz Functions Pengyun Yue, Cong Fang, Zhouchen Lin

Online Learning and Solving Infinite Games with an ERM Oracle Angelos Assos, Idan Attias, Yuval Dagan, Constantinos Daskalakis, Maxwell K. Fishelson

Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence Ruichen Jiang, Qiujiang Jin, Aryan Mokhtari

Online Learning in Dynamically Changing Environments Changlong Wu, Ananth Grama, Wojciech Szpankowski

Online Nonconvex Optimization with Limited Instantaneous Oracle Feedback Ziwei Guan, Yi Zhou, Yingbin Liang

Online Reinforcement Learning in Stochastic Continuous-Time Systems Mohamad Kazem Shirani Faradonbeh, Mohamad Sadegh Shirani Faradonbeh

Open Problem: Is There a First-Order Method That Only Converges to Local Minimax Optima? Jiseok Chae, Kyuwon Kim, Donghwan Kim

Open Problem: Learning Sparse Linear Concepts by Priming the Features Manfred K. Warmuth, Ehsan Amid

Open Problem: Log(n) Factor in "Local Glivenko-Cantelli" Doron Cohen, Aryeh Kontorovich

Open Problem: Polynomial Linearly-Convergent Method for G-Convex Optimization? Christopher Criscitiello, David Martínez-Rubio, Nicolas Boumal

Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes Pranjal Awasthi, Nika Haghtalab, Eric Zhao

Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran

Optimal Scoring Rules for Multi-Dimensional Effort Jason D. Hartline, Liren Shan, Yingkai Li, Yifan Wu

Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making Adam Block, Max Simchowitz, Alexander Rakhlin

Orthogonal Directions Constrained Gradient Method: From Non-Linear Equality Constraints to Stiefel Manifold Sholom Schechtman, Daniil Tiapkin, Michael Muehlebach, Éric Moulines

Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron Weihang Xu, Simon Du

PAC Verification of Statistical Algorithms Saachi Mutreja, Jonathan Shafer

Precise Asymptotic Analysis of Deep Random Feature Models David Bosch, Ashkan Panahi, Babak Hassibi

Private Covariance Approximation and Eigenvalue-Gap Bounds for Complex Gaussian Perturbations Oren Mangoubi, Nisheeth K. Vishnoi

Private Online Prediction from Experts: Separations and Faster Rates Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar

Projection-Free Online Exp-Concave Optimization Dan Garber, Ben Kretzu

Proper Losses, Moduli of Convexity, and Surrogate Regret Bounds Han Bao

Provable Benefits of Representational Transfer in Reinforcement Learning Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang

Quadratic Memory Is Necessary for Optimal Query Complexity in Convex Optimization: Center-of-Mass Is Pareto-Optimal Moïse Blanchard, Junhui Zhang, Patrick Jaillet

Quantum Channel Certification with Incoherent Measurements Omar Fawzi, Nicolas Flammarion, Aurélien Garivier, Aadil Oufkir

Quasi-Newton Steps for Efficient Online Exp-Concave Optimization Zakaria Mhammedi, Khashayar Gatmiry

Reaching Kesten-Stigum Threshold in the Stochastic Block Model Under Node Corruptions Jingqiu Ding, Tommaso d’Orsi, Yiding Hua, David Steurer

Repeated Bilateral Trade Against a Smoothed Adversary Nicolò Cesa-Bianchi, Tommaso R. Cesari, Roberto Colomboni, Federico Fusco, Stefano Leonardi

Resolving the Mixing Time of the Langevin Algorithm to Its Stationary Distribution for Log-Concave Sampling Jason Altschuler, Kunal Talwar

Self-Directed Linear Classification Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis

Semi-Random Sparse Recovery in Nearly-Linear Time Jonathan Kelner, Jerry Li, Allen X. Liu, Aaron Sidford, Kevin Tian

SGD Learning on Neural Networks: Leap Complexity and Saddle-to-Saddle Dynamics Emmanuel Abbe, Enric Boix Adserà, Theodor Misiakiewicz

Sharp Analysis of EM for Learning Mixtures of Pairwise Differences Abhishek Dhawan, Cheng Mao, Ashwin Pananjady

Sharp Thresholds in Inference of Planted Subgraphs Elchanan Mossel, Jonathan Niles-Weed, Youngtak Sohn, Nike Sun, Ilias Zadik

Sharper Model-Free Reinforcement Learning for Average-Reward Markov Decision Processes Zihan Zhang, Qiaomin Xie

Shortest Program Interpolation Learning Naren Sarayu Manoj, Nathan Srebro

Simple Binary Hypothesis Testing Under Local Differential Privacy and Communication Constraints Ankit Pensia, Amir Reza Asadi, Varun Jog, Po-Ling Loh

Sparse PCA Beyond Covariance Thresholding Gleb Novikov

Sparsity-Aware Generalization Theory for Deep Neural Networks Ramchandran Muthukumar, Jeremias Sulam

SQ Lower Bounds for Learning Mixtures of Separated and Bounded Covariance Gaussians Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis

Stability and Generalization of Stochastic Optimization with Nonconvex and Nonsmooth Problems Yunwen Lei

Statistical and Computational Limits for Tensor-on-Tensor Association Detection Ilias Diakonikolas, Daniel M. Kane, Yuetian Luo, Anru Zhang

Statistical-Computational Tradeoffs in Mixed Sparse Linear Regression Gabriel Arpino, Ramji Venkataramanan

STay-on-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games Constantinos Daskalakis, Noah Golowich, Stratis Skoulakis, Emmanouil Zampetakis

Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective Max Simchowitz, Abhishek Gupta, Kaiqing Zhang

Testing of Index-Invariant Properties in the Huge Object Model Sourav Chakraborty, Eldar Fischer, Arijit Ghosh, Gopinath Mishra, Sayantan Sen

The $k$-Cap Process on Geometric Random Graphs Mirabel E. Reid, Santosh S. Vempala

The Aggregation–Heterogeneity Trade-Off in Federated Learning Xuyang Zhao, Huiyuan Wang, Wei Lin

The Complexity of Markov Equilibrium in Stochastic Games Constantinos Daskalakis, Noah Golowich, Kaiqing Zhang

The Computational Complexity of Finding Stationary Points in Non-Convex Optimization Alexandros Hollender, Emmanouil Zampetakis

The Expressive Power of Tuning Only the Normalization Layers Angeliki Giannou, Shashank Rajput, Dimitris Papailiopoulos

The Implicit Bias of Batch Normalization in Linear Models and Two-Layer Linear Convolutional Neural Networks Yuan Cao, Difan Zou, Yuanzhi Li, Quanquan Gu

The One-Inclusion Graph Algorithm Is Not Always Optimal Ishaq Aden-Ali, Yeshwanth Cherapanamjeri, Abhishek Shetty, Nikita Zhivotovskiy

The Sample Complexity of Approximate Rejection Sampling with Applications to Smoothed Online Learning Adam Block, Yury Polyanskiy

Ticketed Learning–Unlearning Schemes Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang

Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures Wai Ming Tai, Bryon Aragam

Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient Dylan J. Foster, Noah Golowich, Yanjun Han

Tighter PAC-Bayes Bounds Through Coin-Betting Kyoungseok Jang, Kwang-Sung Jun, Ilja Kuzborskij, Francesco Orabona

Toward L_∞Recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields Kefan Dong, Tengyu Ma

Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality Alireza Mousavi-Hosseini, Tyler K. Farghly, Ye He, Krishna Balasubramanian, Murat A. Erdogdu

U-Calibration: Forecasting for an Unknown Agent Bobby Kleinberg, Renato Paes Leme, Jon Schneider, Yifeng Teng

Uniqueness of BP Fixed Point for the Potts Model and Applications to Community Detection Yuzhou Gu, Yury Polyanskiy

Universal Rates for Multiclass Learning Steve Hanneke, Shay Moran, Qian Zhang

Universality of Langevin Diffusion for Private Optimization, with Applications to Sampling from Rashomon Sets Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay

Utilising the CLT Structure in Stochastic Gradient Based Sampling : Improved Analysis and Faster Algorithms Aniket Das, Dheeraj M. Nagaraj, Anant Raj

Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu

VO$Q$L: Towards Optimal Regret in Model-Free RL with Nonlinear Function Approximation Alekh Agarwal, Yujia Jin, Tong Zhang

Weak Recovery Threshold for the Hypergraph Stochastic Block Model Yuzhou Gu, Yury Polyanskiy

Zeroth-Order Optimization with Weak Dimension Dependency Pengyun Yue, Long Yang, Cong Fang, Zhouchen Lin