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ORCIDZeyuan Allen Zhu
Zeyuan Allen-Zhu
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- affiliation: MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, USA
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2020 – today
- 2025
[c63]Tian Ye, Zicheng Xu, Yuanzhi Li, Zeyuan Allen-Zhu:
Physics of Language Models: Part 2.2, How to Learn From Mistakes on Grade-School Math Problems. ICLR 2025- [c62]Tian Ye, Zicheng Xu, Yuanzhi Li, Zeyuan Allen-Zhu:
Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process. ICLR 2025 - [c61]Zeyuan Allen-Zhu, Yuanzhi Li:
Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws. ICLR 2025 - [c60]Zeyuan Allen-Zhu, Yuanzhi Li:
Physics of Language Models: Part 3.2, Knowledge Manipulation. ICLR 2025 - 2024
- [c59]Zeyuan Allen-Zhu, Yuanzhi Li:
Physics of Language Models: Part 3.1, Knowledge Storage and Extraction. ICML 2024 - [i65]Olga Golovneva, Zeyuan Allen-Zhu, Jason Weston, Sainbayar Sukhbaatar:
Reverse Training to Nurse the Reversal Curse. CoRR abs/2403.13799 (2024) - [i64]Zeyuan Allen-Zhu, Yuanzhi Li:
Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws. CoRR abs/2404.05405 (2024) - [i63]Tian Ye, Zicheng Xu, Yuanzhi Li, Zeyuan Allen-Zhu:
Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process. CoRR abs/2407.20311 (2024) - [i62]Tian Ye, Zicheng Xu, Yuanzhi Li, Zeyuan Allen-Zhu:
Physics of Language Models: Part 2.2, How to Learn From Mistakes on Grade-School Math Problems. CoRR abs/2408.16293 (2024) - 2023
- [c58]Zeyuan Allen-Zhu, Yuanzhi Li:
Backward Feature Correction: How Deep Learning Performs Deep (Hierarchical) Learning. COLT 2023: 4598 - [c57]Zeyuan Allen-Zhu, Yuanzhi Li:
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning. ICLR 2023 - [c56]Zeyuan Allen-Zhu, Yuanzhi Li:
Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions. ICLR 2023 - [c55]Cathy Yuanchen Li, Emily Wenger, Zeyuan Allen-Zhu, François Charton, Kristin E. Lauter:
SALSA VERDE: a machine learning attack on LWE with sparse small secrets. NeurIPS 2023 - [i61]Zeyuan Allen-Zhu, Yuanzhi Li:
Physics of Language Models: Part 1, Context-Free Grammar. CoRR abs/2305.13673 (2023) - [i60]Cathy Yuanchen Li, Jana Sotáková, Emily Wenger, Zeyuan Allen-Zhu, François Charton, Kristin E. Lauter:
SALSA VERDE: a machine learning attack on Learning With Errors with sparse small secrets. CoRR abs/2306.11641 (2023) - [i59]Zeyuan Allen Zhu, Yuanzhi Li:
Physics of Language Models: Part 3.1, Knowledge Storage and Extraction. CoRR abs/2309.14316 (2023) - [i58]Zeyuan Allen-Zhu, Yuanzhi Li:
Physics of Language Models: Part 3.2, Knowledge Manipulation. CoRR abs/2309.14402 (2023) - [i57]Cathy Yuanchen Li, Jana Sotáková, Emily Wenger, Zeyuan Allen-Zhu, François Charton, Kristin E. Lauter:
SALSA VERDE: a machine learning attack on Learning with Errors with sparse small secrets. IACR Cryptol. ePrint Arch. 2023: 968 (2023) - 2022
- [c54]Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen:
LoRA: Low-Rank Adaptation of Large Language Models. ICLR 2022 - 2021
- [j6]Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang
:
Near-optimal discrete optimization for experimental design: a regret minimization approach. Math. Program. 186(1): 439-478 (2021) - [c53]Zeyuan Allen-Zhu, Yuanzhi Li:
Feature Purification: How Adversarial Training Performs Robust Deep Learning. FOCS 2021: 977-988 - [c52]Zeyuan Allen-Zhu, Faeze Ebrahimianghazani, Jerry Li, Dan Alistarh:
Byzantine-Resilient Non-Convex Stochastic Gradient Descent. ICLR 2021 - [i56]Zeyuan Allen-Zhu, Yuanzhi Li:
Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions. CoRR abs/2106.02619 (2021) - [i55]Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Weizhu Chen:
LoRA: Low-Rank Adaptation of Large Language Models. CoRR abs/2106.09685 (2021) - 2020
- [i54]Zeyuan Allen-Zhu, Yuanzhi Li:
Backward Feature Correction: How Deep Learning Performs Deep Learning. CoRR abs/2001.04413 (2020) - [i53]Zeyuan Allen-Zhu, Yuanzhi Li:
Feature Purification: How Adversarial Training Performs Robust Deep Learning. CoRR abs/2005.10190 (2020) - [i52]Zeyuan Allen-Zhu, Yuanzhi Li:
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning. CoRR abs/2012.09816 (2020) - [i51]Zeyuan Allen-Zhu, Faeze Ebrahimian, Jerry Li, Dan Alistarh:
Byzantine-Resilient Non-Convex Stochastic Gradient Descent. CoRR abs/2012.14368 (2020)
2010 – 2019
- 2019
- [j5]Zeyuan Allen-Zhu, Lorenzo Orecchia:
Nearly linear-time packing and covering LP solvers - Achieving width-independence and -convergence. Math. Program. 175(1-2): 307-353 (2019) - [c51]Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song:
A Convergence Theory for Deep Learning via Over-Parameterization. ICML 2019: 242-252 - [c50]Zeyuan Allen-Zhu, Yuanzhi Li, Yingyu Liang:
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers. NeurIPS 2019: 6155-6166 - [c49]Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song:
On the Convergence Rate of Training Recurrent Neural Networks. NeurIPS 2019: 6673-6685 - [c48]Zeyuan Allen-Zhu, Yuanzhi Li:
What Can ResNet Learn Efficiently, Going Beyond Kernels? NeurIPS 2019: 9015-9025 - [c47]Zeyuan Allen-Zhu, Yuanzhi Li:
Can SGD Learn Recurrent Neural Networks with Provable Generalization? NeurIPS 2019: 10331-10341 - [i50]Zeyuan Allen-Zhu, David Simchi-Levi, Xinshang Wang:
The Lingering of Gradients: How to Reuse Gradients over Time. CoRR abs/1901.02871 (2019) - [i49]Zeyuan Allen-Zhu, Yuanzhi Li:
Can SGD Learn Recurrent Neural Networks with Provable Generalization? CoRR abs/1902.01028 (2019) - [i48]Zeyuan Allen-Zhu, Yuanzhi Li:
What Can ResNet Learn Efficiently, Going Beyond Kernels? CoRR abs/1905.10337 (2019) - 2018
- [c46]Zeyuan Allen-Zhu:
Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex Optimization. ICML 2018: 179-185 - [c45]Zeyuan Allen-Zhu, Sébastien Bubeck, Yuanzhi Li:
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits. ICML 2018: 186-194 - [c44]Zeyuan Allen-Zhu:
How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD. NeurIPS 2018: 1165-1175 - [c43]Zeyuan Allen-Zhu, David Simchi-Levi, Xinshang Wang:
The Lingering of Gradients: How to Reuse Gradients Over Time. NeurIPS 2018: 1252-1261 - [c42]Zeyuan Allen-Zhu:
Natasha 2: Faster Non-Convex Optimization Than SGD. NeurIPS 2018: 2680-2691 - [c41]Zeyuan Allen-Zhu, Yuanzhi Li:
NEON2: Finding Local Minima via First-Order Oracles. NeurIPS 2018: 3720-3730 - [c40]Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li:
Byzantine Stochastic Gradient Descent. NeurIPS 2018: 4618-4628 - [c39]Chi Jin, Zeyuan Allen-Zhu, Sébastien Bubeck, Michael I. Jordan:
Is Q-Learning Provably Efficient? NeurIPS 2018: 4868-4878 - [c38]Zeyuan Allen-Zhu, Ankit Garg, Yuanzhi Li, Rafael Mendes de Oliveira, Avi Wigderson:
Operator scaling via geodesically convex optimization, invariant theory and polynomial identity testing. STOC 2018: 172-181 - [i47]Zeyuan Allen-Zhu:
How To Make the Gradients Small Stochastically. CoRR abs/1801.02982 (2018) - [i46]Zeyuan Allen-Zhu, Sébastien Bubeck, Yuanzhi Li:
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits. CoRR abs/1802.03386 (2018) - [i45]Zeyuan Allen-Zhu:
Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex Optimization. CoRR abs/1802.03866 (2018) - [i44]Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li:
Byzantine Stochastic Gradient Descent. CoRR abs/1803.08917 (2018) - [i43]Zeyuan Allen-Zhu, Ankit Garg, Yuanzhi Li, Rafael Mendes de Oliveira, Avi Wigderson:
Operator Scaling via Geodesically Convex Optimization, Invariant Theory and Polynomial Identity Testing. CoRR abs/1804.01076 (2018) - [i42]Chi Jin, Zeyuan Allen-Zhu, Sébastien Bubeck, Michael I. Jordan:
Is Q-learning Provably Efficient? CoRR abs/1807.03765 (2018) - [i41]Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song:
On the Convergence Rate of Training Recurrent Neural Networks. CoRR abs/1810.12065 (2018) - [i40]Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song:
A Convergence Theory for Deep Learning via Over-Parameterization. CoRR abs/1811.03962 (2018) - [i39]Zeyuan Allen-Zhu, Yuanzhi Li, Yingyu Liang:
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers. CoRR abs/1811.04918 (2018) - 2017
- [j4]Zeyuan Allen-Zhu:
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods. J. Mach. Learn. Res. 18: 221:1-221:51 (2017) - [c37]Zeyuan Allen-Zhu, Yuanzhi Li:
First Efficient Convergence for Streaming k-PCA: A Global, Gap-Free, and Near-Optimal Rate. FOCS 2017: 487-492 - [c36]Zeyuan Allen-Zhu, Yuanzhi Li, Rafael Mendes de Oliveira, Avi Wigderson:
Much Faster Algorithms for Matrix Scaling. FOCS 2017: 890-901 - [c35]Zeyuan Allen-Zhu:
Natasha: Faster Non-Convex Stochastic Optimization via Strongly Non-Convex Parameter. ICML 2017: 89-97 - [c34]Zeyuan Allen-Zhu, Yuanzhi Li:
Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition. ICML 2017: 98-106 - [c33]Zeyuan Allen-Zhu, Yuanzhi Li:
Faster Principal Component Regression and Stable Matrix Chebyshev Approximation. ICML 2017: 107-115 - [c32]Zeyuan Allen-Zhu, Yuanzhi Li:
Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU. ICML 2017: 116-125 - [c31]Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang:
Near-Optimal Design of Experiments via Regret Minimization. ICML 2017: 126-135 - [c30]Zeyuan Allen Zhu, Lorenzo Orecchia:
Linear Coupling: An Ultimate Unification of Gradient and Mirror Descent. ITCS 2017: 3:1-3:22 - [c29]Zeyuan Allen-Zhu, Elad Hazan, Wei Hu, Yuanzhi Li:
Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls. NIPS 2017: 6191-6200 - [c28]Naman Agarwal, Zeyuan Allen Zhu, Brian Bullins, Elad Hazan, Tengyu Ma:
Finding approximate local minima faster than gradient descent. STOC 2017: 1195-1199 - [c27]Zeyuan Allen Zhu:
Katyusha: the first direct acceleration of stochastic gradient methods. STOC 2017: 1200-1205 - [i38]Zeyuan Allen Zhu, Yuanzhi Li:
Follow the Compressed Leader: Faster Algorithms for Matrix Multiplicative Weight Updates. CoRR abs/1701.01722 (2017) - [i37]Zeyuan Allen Zhu:
Natasha: Faster Non-Convex Stochastic Optimization Via Strongly Non-Convex Parameter. CoRR abs/1702.00763 (2017) - [i36]Zeyuan Allen Zhu, Yuanzhi Li, Rafael Mendes de Oliveira, Avi Wigderson:
Much Faster Algorithms for Matrix Scaling. CoRR abs/1704.02315 (2017) - [i35]Zeyuan Allen-Zhu, Elad Hazan, Wei Hu, Yuanzhi Li:
Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls. CoRR abs/1708.02105 (2017) - [i34]Zeyuan Allen-Zhu:
Natasha 2: Faster Non-Convex Optimization Than SGD. CoRR abs/1708.08694 (2017) - [i33]Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang:
Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach. CoRR abs/1711.05174 (2017) - [i32]Zeyuan Allen-Zhu, Yuanzhi Li:
Neon2: Finding Local Minima via First-Order Oracles. CoRR abs/1711.06673 (2017) - 2016
- [j3]Silvio Micali, Zeyuan Allen Zhu:
Reconstructing Markov processes from independent and anonymous experiments. Discret. Appl. Math. 200: 108-122 (2016) - [j2]Zeyuan Allen Zhu, Rati Gelashvili, Ilya P. Razenshteyn:
Restricted Isometry Property for General p-Norms. IEEE Trans. Inf. Theory 62(10): 5839-5854 (2016) - [c26]Zeyuan Allen Zhu, Zhenyu Liao, Yang Yuan:
Optimization Algorithms for Faster Computational Geometry. ICALP 2016: 53:1-53:6 - [c25]Zeyuan Allen Zhu, Elad Hazan:
Variance Reduction for Faster Non-Convex Optimization. ICML 2016: 699-707 - [c24]Zeyuan Allen Zhu, Yang Yuan:
Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives. ICML 2016: 1080-1089 - [c23]Zeyuan Allen Zhu, Zheng Qu, Peter Richtárik, Yang Yuan:
Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling. ICML 2016: 1110-1119 - [c22]Zeyuan Allen Zhu, Yuanzhi Li:
Even Faster SVD Decomposition Yet Without Agonizing Pain. NIPS 2016: 974-982 - [c21]Zeyuan Allen Zhu, Elad Hazan:
Optimal Black-Box Reductions Between Optimization Objectives. NIPS 2016: 1606-1614 - [c20]Zeyuan Allen Zhu, Yang Yuan, Karthik Sridharan:
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters. NIPS 2016: 1642-1650 - [c19]Zeyuan Allen Zhu, Aditya Bhaskara, Silvio Lattanzi, Vahab S. Mirrokni, Lorenzo Orecchia:
Expanders via Local Edge Flips. SODA 2016: 259-269 - [c18]Zeyuan Allen Zhu, Yin Tat Lee, Lorenzo Orecchia:
Using Optimization to Obtain a Width-Independent, Parallel, Simpler, and Faster Positive SDP Solver. SODA 2016: 1824-1831 - [i31]Zeyuan Allen Zhu, Yang Yuan, Karthik Sridharan:
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters. CoRR abs/1602.02151 (2016) - [i30]Zeyuan Allen Zhu, Elad Hazan:
Optimal Black-Box Reductions Between Optimization Objectives. CoRR abs/1603.05642 (2016) - [i29]Zeyuan Allen Zhu, Elad Hazan:
Variance Reduction for Faster Non-Convex Optimization. CoRR abs/1603.05643 (2016) - [i28]Zeyuan Allen Zhu:
Katyusha: Accelerated Variance Reduction for Faster SGD. CoRR abs/1603.05953 (2016) - [i27]Zeyuan Allen Zhu, Yuanzhi Li:
Even Faster SVD Decomposition Yet Without Agonizing Pain. CoRR abs/1607.03463 (2016) - [i26]Zeyuan Allen Zhu, Yuanzhi Li:
Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition. CoRR abs/1607.06017 (2016) - [i25]Zeyuan Allen Zhu, Yuanzhi Li:
Fast Global Convergence of Online PCA. CoRR abs/1607.07837 (2016) - [i24]Zeyuan Allen Zhu, Yuanzhi Li:
Faster Principal Component Regression via Optimal Polynomial Approximation to sgn(x). CoRR abs/1608.04773 (2016) - [i23]Naman Agarwal, Zeyuan Allen Zhu, Brian Bullins, Elad Hazan, Tengyu Ma:
Finding Approximate Local Minima for Nonconvex Optimization in Linear Time. CoRR abs/1611.01146 (2016) - 2015
- [j1]Alessandro Chiesa, Zeyuan Allen Zhu:
Shorter arithmetization of nondeterministic computations. Theor. Comput. Sci. 600: 107-131 (2015) - [c17]Zeyuan Allen Zhu, Rati Gelashvili, Ilya P. Razenshteyn:
Restricted Isometry Property for General p-Norms. SoCG 2015: 451-460 - [c16]Zeyuan Allen Zhu, Lorenzo Orecchia:
Using Optimization to Break the Epsilon Barrier: A Faster and Simpler Width-Independent Algorithm for Solving Positive Linear Programs in Parallel. SODA 2015: 1439-1456 - [c15]Zeyuan Allen Zhu, Lorenzo Orecchia:
Nearly-Linear Time Positive LP Solver with Faster Convergence Rate. STOC 2015: 229-236 - [c14]Zeyuan Allen Zhu, Zhenyu Liao, Lorenzo Orecchia:
Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates. STOC 2015: 237-245 - [i22]Zeyuan Allen Zhu, Yang Yuan:
UniVR: A Universal Variance Reduction Framework for Proximal Stochastic Gradient Method. CoRR abs/1506.01972 (2015) - [i21]Zeyuan Allen Zhu, Zhenyu Liao, Lorenzo Orecchia:
Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates. CoRR abs/1506.04838 (2015) - [i20]Zeyuan Allen Zhu, Yin Tat Lee, Lorenzo Orecchia:
Using Optimization to Obtain a Width-Independent, Parallel, Simpler, and Faster Positive SDP Solver. CoRR abs/1507.02259 (2015) - [i19]Zeyuan Allen Zhu, Aditya Bhaskara, Silvio Lattanzi, Vahab S. Mirrokni, Lorenzo Orecchia:
Expanders via Local Edge Flips. CoRR abs/1510.07768 (2015) - [i18]Zeyuan Allen Zhu, Yang Yuan:
Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling. CoRR abs/1512.09103 (2015) - 2014
- [c13]Alessandro Chiesa, Silvio Micali, Zeyuan Allen Zhu:
Knightian self uncertainty in the vcg mechanism for unrestricted combinatorial auctions. EC 2014: 619-620 - [c12]Lorenzo Orecchia, Zeyuan Allen Zhu:
Flow-Based Algorithms for Local Graph Clustering. SODA 2014: 1267-1286 - [i17]Alessandro Chiesa, Silvio Micali, Zeyuan Allen Zhu:
Bridging Utility Maximization and Regret Minimization. CoRR abs/1403.6394 (2014) - [i16]Alessandro Chiesa, Silvio Micali, Zeyuan Allen Zhu:
Knightian Robustness from Regret Minimization. CoRR abs/1403.6409 (2014) - [i15]Alessandro Chiesa, Silvio Micali, Zeyuan Allen Zhu:
Knightian Analysis of the VCG Mechanism in Unrestricted Combinatorial Auctions. CoRR abs/1403.6410 (2014) - [i14]Alessandro Chiesa, Silvio Micali, Zeyuan Allen Zhu:
Knightian Robustness of Single-Parameter Domains. CoRR abs/1403.6411 (2014) - [i13]Alessandro Chiesa, Silvio Micali, Zeyuan Allen Zhu:
Knightian Robustness of the Vickrey Mechanism. CoRR abs/1403.6413 (2014) - [i12]Zeyuan Allen Zhu, Lorenzo Orecchia:
A Novel, Simple Interpretation of Nesterov's Accelerated Method as a Combination of Gradient and Mirror Descent. CoRR abs/1407.1537 (2014) - [i11]Zeyuan Allen Zhu, Lorenzo Orecchia:
Using Optimization to Break the Epsilon Barrier: A Faster and Simpler Width-Independent Algorithm for Solving Positive Linear Programs in Parallel. CoRR abs/1407.1925 (2014) - [i10]Zeyuan Allen Zhu, Rati Gelashvili, Ilya P. Razenshteyn:
The Restricted Isometry Property for the General p-Norms. CoRR abs/1407.2178 (2014) - [i9]Zeyuan Allen Zhu, Lorenzo Orecchia:
Nearly-Linear Time Packing and Covering LP Solver with Faster Convergence Rate Than $O(1/\varepsilon^2)$. CoRR abs/1411.1124 (2014) - [i8]Zeyuan Allen Zhu, Rati Gelashvili, Silvio Micali, Nir Shavit:
Johnson-Lindenstrauss Compression with Neuroscience-Based Constraints. CoRR abs/1411.5383 (2014) - [i7]Zeyuan Allen Zhu, Zhenyu Liao, Lorenzo Orecchia:
Using Optimization to Find Maximum Inscribed Balls and Minimum Enclosing Balls. CoRR abs/1412.1001 (2014) - 2013
- [c11]Zeyuan Allen Zhu, Silvio Lattanzi, Vahab S. Mirrokni:
A Local Algorithm for Finding Well-Connected Clusters. ICML (3) 2013: 396-404 - [c10]Jonathan A. Kelner, Lorenzo Orecchia, Aaron Sidford, Zeyuan Allen Zhu:
A simple, combinatorial algorithm for solving SDD systems in nearly-linear time. STOC 2013: 911-920 - [i6]Jonathan A. Kelner, Lorenzo Orecchia, Aaron Sidford, Zeyuan Allen Zhu:
A Simple, Combinatorial Algorithm for Solving SDD Systems in Nearly-Linear Time. CoRR abs/1301.6628 (2013) - [i5]Zeyuan Allen Zhu, Silvio Lattanzi, Vahab S. Mirrokni:
A Local Algorithm for Finding Well-Connected Clusters. CoRR abs/1304.8132 (2013) - [i4]Lorenzo Orecchia, Zeyuan Allen Zhu:
Flow-Based Algorithms for Local Graph Clustering. CoRR abs/1307.2855 (2013) - 2012
- [c9]Alessandro Chiesa, Silvio Micali, Zeyuan Allen Zhu:
Mechanism design with approximate valuations. ITCS 2012: 34-38 - [c8]Zeyuan Allen Zhu, Sasa Misailovic, Jonathan A. Kelner, Martin C. Rinard:
Randomized accuracy-aware program transformations for efficient approximate computations. POPL 2012: 441-454 - 2011
- [c7]Wei Chen, Pinyan Lu, Xiaorui Sun, Bo Tang, Yajun Wang, Zeyuan Allen Zhu:
Optimal Pricing in Social Networks with Incomplete Information. WINE 2011: 49-60 - [i3]Alessandro Chiesa, Silvio Micali, Zeyuan Allen Zhu:
Knightian Auctions. CoRR abs/1112.1147 (2011) - 2010
- [c6]Pinyan Lu, Xiaorui Sun, Yajun Wang, Zeyuan Allen Zhu:
Asymptotically optimal strategy-proof mechanisms for two-facility games. EC 2010: 315-324 - [c5]Zeyuan Allen Zhu, Weizhu Chen, Tom Minka, Chenguang Zhu, Zheng Chen:
A novel click model and its applications to online advertising. WSDM 2010: 321-330 - [i2]Wei Chen, Pinyan Lu, Xiaorui Sun, Yajun Wang, Zeyuan Allen Zhu:
Pricing in Social Networks: Equilibrium and Revenue Maximization. CoRR abs/1007.1501 (2010) - [i1]Zeyuan Allen Zhu:
Survey & Experiment: Towards the Learning Accuracy. CoRR abs/1012.4051 (2010)
2000 – 2009
- 2009
- [c4]Chenguang Zhu, Weizhu Chen, Zeyuan Allen Zhu, Gang Wang, Dong Wang, Zheng Chen:
A general magnitude-preserving boosting algorithm for search ranking. CIKM 2009: 817-826 - [c3]Zeyuan Allen Zhu, Weizhu Chen, Tao Wan, Chenguang Zhu, Gang Wang, Zheng Chen:
To divide and conquer search ranking by learning query difficulty. CIKM 2009: 1883-1886 - [c2]Zeyuan Allen Zhu, Weizhu Chen, Chenguang Zhu, Gang Wang, Haixun Wang, Zheng Chen:
Inverse Time Dependency in Convex Regularized Learning. ICDM 2009: 667-676 - [c1]Zeyuan Allen Zhu, Weizhu Chen, Gang Wang, Chenguang Zhu, Zheng Chen:
P-packSVM: Parallel Primal grAdient desCent Kernel SVM. ICDM 2009: 677-686
Coauthor Index
[c63] [c62] [c61] [c60] [c59] [i64] [i63] [i62] [c58] [c57] [c56] [i61] [i59] [i58] [c54] [j6] [c53] [i56] [i55] [i54] [i53] [i52] [c51] [c50] [c49] [c48] [c47] [i49] [i48] [c45] [c41] [c38] [i46] [i43] [i41] [i40] [i39] [c37] [c36] [c34] [c33] [c32] [c31] [c29] [i38] [i36] [i35] [i33] [i32] [c22] [i27] [i26] [i25] [i24]
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