Liu Yang - RankMe

53 papers A* 11A 7B 4Journal 26Unranked 5

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition. ICML Zheyang Xiong, Ziyang Cai, John Cooper, Albert Ge, Vasilis Papageorgiou, Zack Sifakis, Angeliki Giannou, Ziqian Lin, Liu Yang, Saurabh Agarwal, Grigorios Chrysos, Samet Oymak, Kangwook Lee, Dimitris Papailiopoulos
2025	A	`conf`	How Well Can Transformers Emulate In-Context Newton's Method? AISTATS Angeliki Giannou, Liu Yang, Tianhao Wang, Dimitris Papailiopoulos, Jason D. Lee
2025	J	`jnl`	Preference Discerning with LLM-Enhanced Generative Retrieval. Trans. Mach. Learn. Res. Fabian Paischer, Liu Yang, Linfeng Liu, Shuai Shao, Kaveh Hassani, Jiacheng Li, Ricky T. Q. Chen, Zhang Gabriel Li, Xiaoli Gao, Wei Shao, Xue Feng, Nima Noorshams, Sem Park, Bo Long, Hamid Eghbalzadeh
2025	B	`conf`	Reliable Active Apprenticeship Learning. ALT Steve Hanneke, Liu Yang, Gongju Wang, Yulun Song
2025	J	`jnl`	Task Vectors in In-Context Learning: Emergence, Formation, and Benefit. CoRR Liu Yang, Ziqian Lin, Kangwook Lee, Dimitris Papailiopoulos, Robert D. Nowak
2025	J	`jnl`	Unifying Generative and Dense Retrieval for Sequential Recommendation. Trans. Mach. Learn. Res. 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
2024	J	`jnl`	Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition. CoRR 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
2024	J	`jnl`	Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments. CoRR Jeongyeol Kwon, Liu Yang, Robert D. Nowak, Josiah Hanna
2024	J	`jnl`	How Well Can Transformers Emulate In-context Newton's Method? CoRR Angeliki Giannou, Liu Yang, Tianhao Wang, Dimitris Papailiopoulos, Jason D. Lee
2024	A*	`conf`	Looped Transformers are Better at Learning Learning Algorithms. ICLR Liu Yang, Kangwook Lee, Robert D. Nowak, Dimitris Papailiopoulos
2024	J	`jnl`	MultiBalance: Multi-Objective Gradient Balancing in Industrial-Scale Multi-Task Recommendation System. CoRR 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
2024	J	`jnl`	Preference Discerning with LLM-Enhanced Generative Retrieval. CoRR 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
2024	J	`jnl`	Unifying Generative and Dense Retrieval for Sequential Recommendation. CoRR 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
2023	A*	`conf`	Bandit Learnability can be Undecidable. COLT Steve Hanneke, Liu Yang
2023	J	`jnl`	Looped Transformers are Better at Learning Learning Algorithms. CoRR Liu Yang, Kangwook Lee, Robert D. Nowak, Dimitris Papailiopoulos
2022	J	`jnl`	A Better Way to Decay: Proximal Gradient Training Algorithms for Neural Nets. CoRR Liu Yang, Jifan Zhang, Joseph Shenouda, Dimitris S. Papailiopoulos, Kangwook Lee, Robert D. Nowak
2022	A*	`conf`	Rare Gems: Finding Lottery Tickets at Initialization. NeurIPS Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric P. Xing, Kangwook Lee, Dimitris S. Papailiopoulos
2022	J	`jnl`	Rare Gems: Finding Lottery Tickets at Initialization. CoRR Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Kangwook Lee, Dimitris S. Papailiopoulos
2021	A	`conf`	Toward a General Theory of Online Selective Sampling: Trading Off Mistakes and Queries. AISTATS Steve Hanneke, Liu Yang
2019	A	`conf`	Statistical Learning under Nonstationary Mixing Processes. AISTATS Steve Hanneke, Liu Yang
2018	J	`jnl`	Bounds on the minimax rate for estimating a prior over a VC class from independent learning tasks. Theor. Comput. Sci. Liu Yang, Steve Hanneke, Jaime G. Carbonell
2018	J	`jnl`	Testing piecewise functions. Theor. Comput. Sci. Steve Hanneke, Liu Yang
2017	J	`jnl`	Learning with Changing Features. CoRR Amit Dhurandhar, Steve Hanneke, Liu Yang
2017	J	`jnl`	Testing Piecewise Functions. CoRR Steve Hanneke, Liu Yang
2015	B	`conf`	Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks. ALT Liu Yang, Steve Hanneke, Jaime G. Carbonell
2015	J	`jnl`	Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks. CoRR Liu Yang, Steve Hanneke, Jaime G. Carbonell
2015	B	`conf`	Learning with a Drifting Target Concept. ALT Steve Hanneke, Varun Kanade, Liu Yang
2015	J	`jnl`	Learning with a Drifting Target Concept. CoRR Steve Hanneke, Varun Kanade, Liu Yang
2015	J	`jnl`	Minimax analysis of active learning. J. Mach. Learn. Res. Steve Hanneke, Liu Yang
2015	—	`conf`	Online Allocation and Pricing with Economies of Scale. WINE Avrim Blum, Yishay Mansour, Liu Yang
2015	J	`jnl`	Statistical Learning under Nonstationary Mixing Processes. CoRR Steve Hanneke, Tommi S. Jaakkola, Liu Yang
2014	J	`jnl`	Minimax Analysis of Active Learning. CoRR Steve Hanneke, Liu Yang
2013	J	`jnl`	A theory of transfer learning with applications to active learning. Mach. Learn. Liu Yang, Steve Hanneke, Jaime G. Carbonell
2013	—	`conf`	Activized Learning with Uniform Classification Noise. ICML (2) Liu Yang, Steve Hanneke
2013	—	`conf`	Buy-in-Bulk Active Learning. NIPS Liu Yang, Jaime G. Carbonell
2013	A	`conf`	Learnability of DNF with representation-specific queries. ITCS Liu Yang, Avrim Blum, Jaime G. Carbonell
2012	A*	`conf`	Active Property Testing. FOCS Maria-Florina Balcan, Eric Blais, Avrim Blum, Liu Yang
2012	J	`jnl`	Bayesian Active Distance Metric Learning CoRR Liu Yang, Rong Jin, Rahul Sukthankar
2012	J	`jnl`	Surrogate Losses in Passive and Active Learning CoRR Steve Hanneke, Liu Yang
2011	J	`jnl`	Active Testing CoRR Maria-Florina Balcan, Eric Blais, Avrim Blum, Liu Yang
2011	A*	`conf`	Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning. COLT Liu Yang, Steve Hanneke, Jaime G. Carbonell
2011	A	`conf`	The Sample Complexity of Self-Verifying Bayesian Active Learning. AISTATS Liu Yang, Steve Hanneke, Jaime G. Carbonell
2010	J	`jnl`	A Boosting Framework for Visuality-Preserving Distance Metric Learning and Its Application to Medical Image Retrieval. IEEE Trans. Pattern Anal. Mach. Intell. Liu Yang, Rong Jin, Lily B. Mummert, Rahul Sukthankar, Adam Goode, Bin Zheng, Steven C. H. Hoi, Mahadev Satyanarayanan
2010	B	`conf`	Bayesian Active Learning Using Arbitrary Binary Valued Queries. ALT Liu Yang, Steve Hanneke, Jaime G. Carbonell
2010	A	`conf`	Negative Results for Active Learning with Convex Losses. AISTATS Steve Hanneke, Liu Yang
2009	A*	`conf`	Online learning by ellipsoid method. ICML Liu Yang, Rong Jin, Jieping Ye
2008	—	`conf`	Semi-supervised Learning with Weakly-Related Unlabeled Data: Towards Better Text Categorization. NIPS Liu Yang, Rong Jin, Rahul Sukthankar
2008	A*	`conf`	Unifying discriminative visual codebook generation with classifier training for object category recognition. CVPR Liu Yang, Rong Jin, Rahul Sukthankar, Frédéric Jurie
2007	A	`conf`	Bayesian Active Distance Metric Learning. UAI Liu Yang, Rong Jin, Rahul Sukthankar
2007	A*	`conf`	Discriminative Cluster Refinement: Improving Object Category Recognition Given Limited Training Data. CVPR Liu Yang, Rong Jin, Caroline Pantofaru, Rahul Sukthankar
2007	—	`conf`	Learning distance metrics for interactive search-assisted diagnosis of mammograms. Computer-Aided Diagnosis Liu Yang, Rong Jin, Rahul Sukthankar, Bin Zheng, Lily B. Mummert, Mahadev Satyanarayanan, Mei Chen, Drazen Jukic
2006	A*	`conf`	An Efficient Algorithm for Local Distance Metric Learning. AAAI Liu Yang, Rong Jin, Rahul Sukthankar, Yi Liu
2006	A*	`conf`	Semi-supervised Multi-label Learning by Constrained Non-negative Matrix Factorization. AAAI Yi Liu, Rong Jin, Liu Yang