Country
Minimax Regret for Cascading Bandits
Cascading bandits is a natural and popular model that frames the task of learning to rank from Bernoulli click feedback in a bandit setting.
Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences
In our work, we propose the first distributed method with client sampling and provable tolerance to Byzantine workers.
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Jeremiah Liu, John Paisley, Marianthi-Anna Kioumourtzoglou, Brent Coull
Neural Information Processing Systems http://nips.cc/
Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
Han Zhu, Daqing Chang, Ziru Xu, Pengye Zhang, Xiang Li, Jie He, Han Li, Jian Xu, Kun Gai
To retrieve and recommend the most relevant items to users under response time limits, resorting to an efficient index structure is an effective and practical solution.
Deep
Asaresult, sinceeach 6 Table 1: results (", )-DPwith = 10 5. The? indicates representations.
ACharacter-LevelLength-ControlAlgorithmfor Non-AutoregressiveSentenceSummarization
Sentence summarization aims at compressing a long sentence into a short one that conveys the main idea of the input.
Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts Anna Mészáros
LLMs show remarkable emergent abilities, such as inferring concepts from presumably out-of-distribution prompts, known as in-context learning.