Dennis Wei

AI Steerability 360: A Toolkit for Steering Large Language Models ACL 2026 Multi-component Causal Tracing in Large Language Models ACL 2026 Parameterized Abstract Interpretation for Transformer Verification AAAI 2026 Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning ICML 2025 Multi-Level Explanations for Generative Language Models ACL 2025 Reasoning Model Unlearning: Forgetting Traces, Not Just Answers, While Preserving Reasoning Skills EMNLP 2025 Causal Bandits with General Causal Models and Interventions AISTATS 2024 Interventional Causal Discovery in a Mixture of DAGs NeurIPS 2024 SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation ICLR 2024 Selective Explanations NeurIPS 2024 Trust Regions for Explanations via Black-Box Probabilistic Certification ICML 2024 Using Causal Inference to Investigate Contraceptive Discontinuation in Sub-Saharan Africa IJCAI 2024 Convex Bounds on the Softmax Function with Applications to Robustness Verification AISTATS 2023 Effective Human-AI Teams via Learned Natural Language Rules and Onboarding NeurIPS 2023 Heavy Sets with Applications to Interpretable Machine Learning Diagnostics AISTATS 2023 Interpretable and Fair Boolean Rule Sets via Column Generation JMLR 2023 Interpretable differencing of machine learning models UAI 2023 Who Should Predict? Exact Algorithms For Learning to Defer to Humans AISTATS 2023 AI Explainability 360: Impact and Design AAAI 2022 On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach NeurIPS 2022 Your fairness may vary: Pretrained language model fairness in toxic text classification ACL 2022 CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions NeurIPS 2021 Conditionally independent data generation UAI 2021 Decision-Making Under Selective Labels: Optimal Finite-Domain Policies and Beyond ICML 2021 Optimized Score Transformation for Consistent Fair Classification JMLR 2021 What Changed? Interpretable Model Comparison IJCAI 2021 AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models JMLR 2020 Characterization of Overlap in Observational Studies AISTATS 2020 DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks NeurIPS 2020 Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing ICML 2020 Optimized Score Transformation for Fair Classification AISTATS 2020 Generalized Linear Rule Models ICML 2019 Boolean Decision Rules via Column Generation NeurIPS 2018 Parallel Bayesian Network Structure Learning ICML 2018 Optimized Pre-Processing for Discrimination Prevention NeurIPS 2017 A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++ NeurIPS 2016 Distributed Learning of Gaussian Graphical Models via Marginal Likelihoods AISTATS 2013