Statistical Learning
Proximal Causal Inference with Text Data
Data-driven decision making relies on estimating the effect of interventions, i.e. causal effect estimation . For example, a doctor must decide which medicine she will give her patient, ideally the one with the greatest effect on positive outcomes.
Towards Reliable Model Selection for Unsupervised Domain Adaptation: An Empirical Study and A Certified Baseline
Code is available at https://github.com/LHXXHB/EnsV.
Avoiding Undesired Future with Minimal Cost in Non-Stationary Environments
Machine learning (ML) has achieved remarkable success in prediction tasks.
Weak Supervision Performance Evaluation via Partial Identification Felipe Maia Polo Department of Statistics University of Michigan Subha Maity
In previous work, for example, Fréchet bounds were studied in the financial context ( e.g., see
Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization
Transformers have demonstrated great power in the recent development of large foundational models.
Evaluate then Cooperate: Shapley-based View Cooperation Enhancement for Multi-view Clustering Fangdi Wang
Moreover, how to further enhance view cooperation for better fusion still needs to be explored.
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators Yiyan Huang
Second, they lack a specific focus on selecting a robust CA TE estimator.
Language Model as Visual Explainer
While efforts have been made to explain computer vision models, these approaches often fall short of providing direct and human-understandable explanations.