Statistical Learning
Policy Learning for Fairness in Ranking
Ashudeep Singh, Thorsten Joachims
Neural Information Processing Systems http://nips.cc/
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification
Ronghui You, Zihan Zhang, Ziye Wang, Suyang Dai, Hiroshi Mamitsuka, Shanfeng Zhu
Neural Information Processing Systems http://nips.cc/
Overlapping Clustering Models, and One (class) SVM to Bind Them All
Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti
Neural Information Processing Systems http://nips.cc/
Computing Full Conformal Prediction Set with Approximate Homotopy
Eugene Ndiaye, Ichiro Takeuchi
If you are predicting the labely of a new object withˆy, how confident are you that y = ˆy?
LOVM: Language-Only Vision Model Selection
Pre-trained multi-modal vision-language models (VLMs) are becoming increasingly popular due to their exceptional performance on downstream vision applications, particularly in the few-and zero-shot settings.
Graph Transformer Networks
Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim
Thelimitations especially become problematic when learning representations on a misspecified graph or a heterogeneous graph that consists of various types of nodes and edges.
Appendix of Foundation Model is Efficient Multimodal Multitask Model Selector Anonymous Author(s) Affiliation Address email A Related Work
Model selection is an important task in transfer learning. In this sense, a multi-task model selector is complementary to foundation models. We will prove the convergence property of the function value. Then this lemma is proved. Lemma 2. Let f be the β -smooth function.