ML Anthology

Veitch, Victor

33 publications

ICML 2025 RATE: Causal Explainability of Reward Models with Imperfect Counterfactuals David Reber, Sean M Richardson, Todd Nief, Cristina Garbacea, Victor Veitch
ICLR 2025 The Geometry of Categorical and Hierarchical Concepts in Large Language Models Kiho Park, Yo Joong Choe, Yibo Jiang, Victor Veitch
NeurIPS 2024 BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-N Sampling Lin Gui, Cristina Gârbacea, Victor Veitch
ICMLW 2024 Does Editing Provide Evidence for Localization? Zihao Wang, Victor Veitch
ICML 2024 On the Origins of Linear Representations in Large Language Models Yibo Jiang, Goutham Rajendran, Pradeep Kumar Ravikumar, Bryon Aragam, Victor Veitch
ICMLW 2024 The Geometry of Categorical and Hierarchical Concepts in Large Language Models Kiho Park, Yo Joong Choe, Yibo Jiang, Victor Veitch
ICMLW 2024 The Geometry of Categorical and Hierarchical Concepts in Large Language Models Kiho Park, Yo Joong Choe, Yibo Jiang, Victor Veitch
ICML 2024 The Linear Representation Hypothesis and the Geometry of Large Language Models Kiho Park, Yo Joong Choe, Victor Veitch
ICML 2024 Transforming and Combining Rewards for Aligning Large Language Models Zihao Wang, Chirag Nagpal, Jonathan Berant, Jacob Eisenstein, Alexander Nicholas D’Amour, Sanmi Koyejo, Victor Veitch
NeurIPS 2023 Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness Jacy Anthis, Victor Veitch
ICLR 2023 Causal Estimation for Text Data with (Apparent) Overlap Violations Lin Gui, Victor Veitch
NeurIPS 2023 Concept Algebra for (Score-Based) Text-Controlled Generative Models Zihao Wang, Lin Gui, Jeffrey Negrea, Victor Veitch
ICMLW 2023 Concept Algebra for Score-Based Conditional Model Zihao Wang, Lin Gui, Jeffrey Negrea, Victor Veitch
ICLR 2023 Efficient Conditionally Invariant Representation Learning Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton
NeurIPSW 2023 Reward Model Aggregation Zihao Wang, Chirag Nagpal, Alexander D'Amour, Victor Veitch, Sanmi Koyejo
NeurIPSW 2023 The Linear Representation Hypothesis and the Geometry of Large Language Models Kiho Park, Yo Joong Choe, Victor Veitch
NeurIPS 2023 Uncovering Meanings of Embeddings via Partial Orthogonality Yibo Jiang, Bryon Aragam, Victor Veitch
NeurIPSW 2023 What's Your Use Case? a Taxonomy of Causal Evaluations of Post-Hoc Interpretability David Reber, Cristina Garbacea, Victor Veitch
ICMLW 2022 A Unified Causal View of Domain Invariant Representation Learning Zihao Wang, Victor Veitch
NeurIPSW 2022 Causal Estimation for Text Data with (Apparent) Overlap Violations Lin Gui, Victor Veitch
NeurIPS 2022 Invariant and Transportable Representations for Anti-Causal Domain Shifts Yibo Jiang, Victor Veitch
ICMLW 2022 Invariant and Transportable Representations for Anti-Causal Domain Shifts Yibo Jiang, Victor Veitch
JMLR 2022 Underspecification Presents Challenges for Credibility in Modern Machine Learning Alexander D'Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley
NeurIPS 2022 Using Embeddings for Causal Estimation of Peer Influence in Social Networks Irina Cristali, Victor Veitch
NeurIPS 2021 Counterfactual Invariance to Spurious Correlations in Text Classification Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein
UAI 2021 Invariant Representation Learning for Treatment Effect Estimation Claudia Shi, Victor Veitch, David M. Blei
ICML 2021 Valid Causal Inference with (Some) Invalid Instruments Jason S Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown
UAI 2020 Adapting Text Embeddings for Causal Inference Victor Veitch, Dhanya Sridhar, David Blei
NeurIPS 2020 Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding Victor Veitch, Anisha Zaveri
NeurIPS 2019 Adapting Neural Networks for the Estimation of Treatment Effects Claudia Shi, David Blei, Victor Veitch
AISTATS 2019 Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz
ICLR 2019 Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz
NeurIPS 2019 Using Embeddings to Correct for Unobserved Confounding in Networks Victor Veitch, Yixin Wang, David Blei