Notes for academic papers
- Causality Lecture Notes
- The Book of Why
- Quantifying Causal Contribution via Structure Preserving Interventions
- Generalization and Invariances in the Presence of Unobserved Confounding
- Learning Causal Effects via Weighted Empirical Risk Minimization
- The Do-Calculus Revisited
- Causal Inference by Using Invariant Prediction: Identification and Confidence Intervals
- Does obesity shorten life? The importance of well-defined interventions to answer causal questions
- Feature Selection as Causal Inference: Experiments with Text Classification
- Causal Regularization
- A Unified View of Causal and Non-causal Feature Selection
- Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters
- Exchanging Lessons Between Algorithmic Fairness and Domain Generalization
- Compositional Explanations for Image Classifiers
- Deep Stable Learning for Out-Of-Distribution Generalization
- Double/Debiased Machine Learning for Treatment and Structural Parameters
- The relationship between trust in AI and trustworthy machine learning technologies
- Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference
- An Investigation of Why Overparameterization Exacerbates Spurious Correlations
- Individual Calibration with Randomized Forecasting
- Interpretable Goal-based Prediction and Planning for Autonomous Driving
- Adversarial Filters of Dataset Biases
- SELECTIVE CLASSIFICATION CAN MAGNIFY DISPARITIES ACROSS GROUPS
- Right Decisions from Wrong Predictions:A Mechanism Design Alternative to Individual Calibration
- DISTRIBUTIONALLY ROBUST NEURAL NETWORKS FOR GROUP SHIFTS: ON THE IMPORTANCE OF REGULARIZATION FOR WORST-CASE GENERALIZATION
- Publicly Available Clinical BERT Embeddings
- Performative Prediction
- Strategic Classification is Causal Modeling in Disguise
- The Implicit Fairness Criterion of Unconstrained Learning
- UNDERSTANDING DEEP LEARNING REQUIRES RE-THINKING GENERALIZATION
- FEATURE-WISE BIAS AMPLIFICATION
- Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning
- Directional Bias Amplification
- Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
- Interpretability Beyond Feature Attribution:Quantitative Testing with Concept Activation Vectors (TCAV)
- Fairness in Machine Learning with Tractable Models
- Heterogeneous Risk Minimization
- Toward an Understanding of Adversarial Examples in Clinical Trials
- Understanding Failures of Deep Networks via Robust Feature Extraction
- Invariant Risk Minimization
- Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning
- Sum-Product Networks: A New Deep Architecture
- Second opinion needed: communicating uncertainty in medical machine learning
- Occam`s Razor
- Bayesian model averaging is not model combination
- Knowledge-Gradient Methods for Statistical Learning
- Improving the quality of machine learning in health applications and clinical research
- Distinguishing prognostic and predictive biomarkers: an information theoretic approach
- Ethical Machine Learning in Health Care
- Sum-Product Networks for Early Outbreak Detection of Emerging Diseases
- AI for radiographic COVID-19 detection selects shortcuts over signal
- Explainable Machine Learning in Deployment
- Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
- Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups
- Discovering Reliable Causal Rules
- Learning Certifiably Optimal Rule Lists for Categorical Data
- Learning Fair Rule Lists
- Anytime Subgroup Discovery in Numerical Domains with Guarantees
- ROC ’n’ Rule Learning—Towards a Better Understanding of Covering Algorithms