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ORCIDLiu Yang 0001
Person information
- affiliation: Yale University, New Haven, CT, USA
- affiliation (former): IBM T.J. Watson Research Center, Yorktown Heights, USA
- affiliation (former): Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA
- affiliation (former): Michigan State University, East Lansing, MI, USA
Other persons with the same name
- Liu Yang — disambiguation page
- Liu Yang 0002
![[0000-0001-9915-6432]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0001-9915-6432")
— Guangzhou University, School of Computer Science and Education Software, China (and 1 more) - Liu Yang 0003 — Chongqing University of Posts and Telecommunications, School of Communication and Information Engineering, China
- Liu Yang 0004 — Army Engineering University of PLA, College of Communication Engineering, Nanjing, China
- Liu Yang 0005 — University of Massachusetts Amherst, College of Information and Computer Sciences, MA, USA
- Liu Yang 0006 — Beijing Jiaotong University, State Key Laboratory of Rail Traffic Control and Safety, China
- Liu Yang 0007 — Beijing Institute of Technology, School of Automation, China
- Liu Yang 0008 — Hong Kong University of Science and Technology, Department of Computer Science and Engineering, Hong Kong
2020 – today
- 2025
[c25]Steve Hanneke, Liu Yang, Gongju Wang, Yulun Song:
Reliable Active Apprenticeship Learning. ALT 2025: 512-538- [i19]Liu Yang, Ziqian Lin, Kangwook Lee, Dimitris Papailiopoulos, Robert D. Nowak:
Task Vectors in In-Context Learning: Emergence, Formation, and Benefit. CoRR abs/2501.09240 (2025) - 2024
- [c24]Liu Yang, Kangwook Lee, Robert D. Nowak, Dimitris Papailiopoulos:
Looped Transformers are Better at Learning Learning Algorithms. ICLR 2024 - [i18]Jeongyeol Kwon, Liu Yang, Robert D. Nowak, Josiah Hanna:
Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments. CoRR abs/2402.07102 (2024) - [i17]Angeliki Giannou, Liu Yang, Tianhao Wang, Dimitris Papailiopoulos, Jason D. Lee:
How Well Can Transformers Emulate In-context Newton's Method? CoRR abs/2403.03183 (2024) - [i16]Zheyang Xiong, Ziyang Cai, John Cooper, Albert Ge, Vasilis Papageorgiou, Zack Sifakis, Angeliki Giannou, Ziqian Lin, Liu Yang, Saurabh Agarwal, Grigorios G. Chrysos, Samet Oymak, Kangwook Lee, Dimitris Papailiopoulos:
Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition. CoRR abs/2410.05603 (2024) - [i15]Yun He, Xuxing Chen, Jiayi Xu, Renqin Cai, Yiling You, Jennifer Cao, Minhui Huang, Liu Yang, Yiqun Liu, Xiaoyi Liu, Rong Jin, Sem Park, Bo Long, Xue Feng:
MultiBalance: Multi-Objective Gradient Balancing in Industrial-Scale Multi-Task Recommendation System. CoRR abs/2411.11871 (2024) - [i14]Liu Yang, Fabian Paischer, Kaveh Hassani, Jiacheng Li, Shuai Shao, Zhang Gabriel Li, Yun He, Xue Feng, Nima Noorshams, Sem Park, Bo Long, Robert D. Nowak, Xiaoli Gao, Hamid Eghbalzadeh:
Unifying Generative and Dense Retrieval for Sequential Recommendation. CoRR abs/2411.18814 (2024) - [i13]Fabian Paischer, Liu Yang, Linfeng Liu, Shuai Shao, Kaveh Hassani, Jiacheng Li, Ricky Chen, Zhang Gabriel Li, Xialo Gao, Wei Shao, Xue Feng, Nima Noorshams, Sem Park, Bo Long, Hamid Eghbalzadeh:
Preference Discerning with LLM-Enhanced Generative Retrieval. CoRR abs/2412.08604 (2024) - 2023
- [c23]Steve Hanneke, Liu Yang:
Bandit Learnability can be Undecidable. COLT 2023: 5813-5849 - [i12]Liu Yang, Kangwook Lee, Robert D. Nowak, Dimitris Papailiopoulos:
Looped Transformers are Better at Learning Learning Algorithms. CoRR abs/2311.12424 (2023) - 2022
- [c22]Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric P. Xing, Kangwook Lee, Dimitris S. Papailiopoulos:
Rare Gems: Finding Lottery Tickets at Initialization. NeurIPS 2022 - [i11]Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Kangwook Lee, Dimitris S. Papailiopoulos:
Rare Gems: Finding Lottery Tickets at Initialization. CoRR abs/2202.12002 (2022) - [i10]Liu Yang, Jifan Zhang, Joseph Shenouda, Dimitris S. Papailiopoulos, Kangwook Lee, Robert D. Nowak:
A Better Way to Decay: Proximal Gradient Training Algorithms for Neural Nets. CoRR abs/2210.03069 (2022) - 2021
- [c21]Steve Hanneke, Liu Yang:
Toward a General Theory of Online Selective Sampling: Trading Off Mistakes and Queries. AISTATS 2021: 3997-4005
2010 – 2019
- 2019
- [c20]Steve Hanneke, Liu Yang:
Statistical Learning under Nonstationary Mixing Processes. AISTATS 2019: 1678-1686 - 2018
- [j5]Liu Yang, Steve Hanneke, Jaime G. Carbonell:
Bounds on the minimax rate for estimating a prior over a VC class from independent learning tasks. Theor. Comput. Sci. 716: 124-140 (2018) - [j4]Steve Hanneke, Liu Yang:
Testing piecewise functions. Theor. Comput. Sci. 745: 23-35 (2018) - 2017
- [i9]Amit Dhurandhar, Steve Hanneke, Liu Yang:
Learning with Changing Features. CoRR abs/1705.00219 (2017) - [i8]Steve Hanneke, Liu Yang:
Testing Piecewise Functions. CoRR abs/1706.07669 (2017) - 2015
- [j3]Steve Hanneke, Liu Yang:
Minimax analysis of active learning. J. Mach. Learn. Res. 16: 3487-3602 (2015) - [c19]Steve Hanneke, Varun Kanade, Liu Yang:
Learning with a Drifting Target Concept. ALT 2015: 149-164 - [c18]Liu Yang, Steve Hanneke, Jaime G. Carbonell:
Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks. ALT 2015: 270-284 - [c17]Avrim Blum, Yishay Mansour, Liu Yang:
Online Allocation and Pricing with Economies of Scale. WINE 2015: 159-172 - [i7]Steve Hanneke, Varun Kanade, Liu Yang:
Learning with a Drifting Target Concept. CoRR abs/1505.05215 (2015) - [i6]Liu Yang, Steve Hanneke, Jaime G. Carbonell:
Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks. CoRR abs/1505.05231 (2015) - [i5]Steve Hanneke, Tommi S. Jaakkola, Liu Yang:
Statistical Learning under Nonstationary Mixing Processes. CoRR abs/1512.08064 (2015) - 2014
- [i4]Steve Hanneke, Liu Yang:
Minimax Analysis of Active Learning. CoRR abs/1410.0996 (2014) - 2013
- [j2]Liu Yang, Steve Hanneke, Jaime G. Carbonell:
A theory of transfer learning with applications to active learning. Mach. Learn. 90(2): 161-189 (2013) - [c16]Liu Yang, Steve Hanneke:
Activized Learning with Uniform Classification Noise. ICML (2) 2013: 370-378 - [c15]Liu Yang, Avrim Blum, Jaime G. Carbonell:
Learnability of DNF with representation-specific queries. ITCS 2013: 37-46 - [c14]Liu Yang, Jaime G. Carbonell:
Buy-in-Bulk Active Learning. NIPS 2013: 2229-2237 - 2012
- [c13]Maria-Florina Balcan, Eric Blais, Avrim Blum, Liu Yang:
Active Property Testing. FOCS 2012: 21-30 - [i3]Liu Yang, Rong Jin, Rahul Sukthankar:
Bayesian Active Distance Metric Learning. CoRR abs/1206.5283 (2012) - [i2]Steve Hanneke, Liu Yang:
Surrogate Losses in Passive and Active Learning. CoRR abs/1207.3772 (2012) - 2011
- [c12]Liu Yang, Steve Hanneke, Jaime G. Carbonell:
Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning. COLT 2011: 789-806 - [c11]Liu Yang, Steve Hanneke, Jaime G. Carbonell:
The Sample Complexity of Self-Verifying Bayesian Active Learning. AISTATS 2011: 816-822 - [i1]Maria-Florina Balcan, Eric Blais, Avrim Blum, Liu Yang:
Active Testing. CoRR abs/1111.0897 (2011) - 2010
- [j1]Liu Yang, Rong Jin, Lily B. Mummert, Rahul Sukthankar, Adam Goode, Bin Zheng, Steven C. H. Hoi, Mahadev Satyanarayanan:
A Boosting Framework for Visuality-Preserving Distance Metric Learning and Its Application to Medical Image Retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 32(1): 30-44 (2010) - [c10]Liu Yang, Steve Hanneke, Jaime G. Carbonell:
Bayesian Active Learning Using Arbitrary Binary Valued Queries. ALT 2010: 50-58 - [c9]Steve Hanneke, Liu Yang:
Negative Results for Active Learning with Convex Losses. AISTATS 2010: 321-325
2000 – 2009
- 2009
- [c8]Liu Yang, Rong Jin, Jieping Ye:
Online learning by ellipsoid method. ICML 2009: 1153-1160 - 2008
- [c7]Liu Yang, Rong Jin, Rahul Sukthankar, Frédéric Jurie:
Unifying discriminative visual codebook generation with classifier training for object category recognition. CVPR 2008 - [c6]Liu Yang, Rong Jin, Rahul Sukthankar:
Semi-supervised Learning with Weakly-Related Unlabeled Data: Towards Better Text Categorization. NIPS 2008: 1857-1864 - 2007
- [c5]Liu Yang, Rong Jin, Caroline Pantofaru, Rahul Sukthankar:
Discriminative Cluster Refinement: Improving Object Category Recognition Given Limited Training Data. CVPR 2007 - [c4]Liu Yang, Rong Jin, Rahul Sukthankar, Bin Zheng, Lily B. Mummert, Mahadev Satyanarayanan, Mei Chen, Drazen Jukic:
Learning distance metrics for interactive search-assisted diagnosis of mammograms. Computer-Aided Diagnosis 2007: 65141H - [c3]Liu Yang, Rong Jin, Rahul Sukthankar:
Bayesian Active Distance Metric Learning. UAI 2007: 442-449 - 2006
- [c2]Yi Liu, Rong Jin, Liu Yang:
Semi-supervised Multi-label Learning by Constrained Non-negative Matrix Factorization. AAAI 2006: 421-426 - [c1]Liu Yang, Rong Jin, Rahul Sukthankar, Yi Liu:
An Efficient Algorithm for Local Distance Metric Learning. AAAI 2006: 543-548
Coauthor Index
aka: Dimitris Papailiopoulos
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