Summary of Fair-tat: Improving Model Fairness Using Targeted Adversarial Training, by Tejaswini Medi et al.
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Trainingby Tejaswini Medi, Steffen Jung, Margret KeuperFirst submitted…
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Trainingby Tejaswini Medi, Steffen Jung, Margret KeuperFirst submitted…
The Good, the Bad, and the Ugly: The Role of AI Quality Disclosure in Lie…
FoLDTree: A ULDA-Based Decision Tree Framework for Efficient Oblique Splits and Feature Selectionby Siyu WangFirst…
HiBO: Hierarchical Bayesian Optimization via Adaptive Search Space Partitioningby Wenxuan Li, Taiyi Wang, Eiko YonekiFirst…
QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMsby Mohammad Shahverdikondori, Ehsan Mokhtarian, Negar KiyavashFirst submitted…
VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planningby Yichao Liang, Nishanth Kumar,…
Directional anomaly detectionby Oliver Urs Lenz, Matthijs van LeeuwenFirst submitted to arxiv on: 30 Oct…
Fourier Amplitude and Correlation Loss: Beyond Using L2 Loss for Skillful Precipitation Nowcastingby Chiu-Wai Yan,…
FlexTSF: A Universal Forecasting Model for Time Series with Variable Regularitiesby Jingge Xiao, Yile Chen,…
SciPIP: An LLM-based Scientific Paper Idea Proposerby Wenxiao Wang, Lihui Gu, Liye Zhang, Yunxiang Luo,…