Victor Veitch

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 49

Top Topics

Causal Inference

Invariant Representation

Top Venues

Publications

Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam, Victor Veitch
On the Origins of Linear Representations in Large Language Models. CoRR (2024)
Zihao Wang, Chirag Nagpal, Jonathan Berant, Jacob Eisenstein, Alex D'Amour, Sanmi Koyejo, Victor Veitch
Transforming and Combining Rewards for Aligning Large Language Models. CoRR (2024)
Kiho Park, Yo Joong Choe, Yibo Jiang, Victor Veitch
The Geometry of Categorical and Hierarchical Concepts in Large Language Models. CoRR (2024)
Lin Gui, Cristina Gârbacea, Victor Veitch
BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling. CoRR (2024)
Yibo Jiang, Bryon Aragam, Victor Veitch
Uncovering Meanings of Embeddings via Partial Orthogonality. NeurIPS (2023)
Jacy Reese Anthis, Victor Veitch
Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness. NeurIPS (2023)
Lin Gui, Victor Veitch
Causal Estimation for Text Data with (Apparent) Overlap Violations. ICLR (2023)
Yibo Jiang, Bryon Aragam, Victor Veitch
Uncovering Meanings of Embeddings via Partial Orthogonality. CoRR (2023)
Zihao Wang, Lin Gui, Jeffrey Negrea, Victor Veitch
Concept Algebra for (Score-Based) Text-Controlled Generative Models. NeurIPS (2023)
Kiho Park, Yo Joong Choe, Victor Veitch
The Linear Representation Hypothesis and the Geometry of Large Language Models. CoRR (2023)
Jacy Reese Anthis, Victor Veitch
Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness. CoRR (2023)
Zihao Wang, Lin Gui, Jeffrey Negrea, Victor Veitch
Concept Algebra for Text-Controlled Vision Models. CoRR (2023)
Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton
Efficient Conditionally Invariant Representation Learning. ICLR (2023)
Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton
Efficient Conditionally Invariant Representation Learning. CoRR (2022)
Zihao Wang, Victor Veitch
A Unified Causal View of Domain Invariant Representation Learning. CoRR (2022)
Lin Gui, Victor Veitch
Causal Estimation for Text Data with (Apparent) Overlap Violations. CoRR (2022)
Wesley Tansey, Victor Veitch, Haoran Zhang, Raul Rabadan, David M. Blei
The Holdout Randomization Test for Feature Selection in Black Box Models. J. Comput. Graph. Stat. 31 (1) (2022)
Yibo Jiang, Victor Veitch
Invariant and Transportable Representations for Anti-Causal Domain Shifts. NeurIPS (2022)
Alexander D'Amour, Katherine A. 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, Yi-An Ma, Cory Y. 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
Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 23 (2022)
Yibo Jiang, Victor Veitch
Invariant and Transportable Representations for Anti-Causal Domain Shifts. CoRR (2022)
Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M. Stewart, Victor Veitch, Diyi Yang
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond. Trans. Assoc. Comput. Linguistics 10 (2022)
Irina Cristali, Victor Veitch
Using Embeddings for Causal Estimation of Peer Influence in Social Networks. CoRR (2022)
Irina Cristali, Victor Veitch
Using Embeddings for Causal Estimation of Peer Influence in Social Networks. NeurIPS (2022)
Jason S. Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown
Valid Causal Inference with (Some) Invalid Instruments. ICML (2021)
Aaron Schein, Keyon Vafa, Dhanya Sridhar, Victor Veitch, Jeffrey Quinn, James Moffet, David M. Blei, Donald P. Green
Assessing the Effects of Friend-to-Friend Texting onTurnout in the 2018 US Midterm Elections. WWW (2021)
Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M. Stewart, Victor Veitch, Diyi Yang
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond. CoRR (2021)
Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein
Counterfactual Invariance to Spurious Correlations in Text Classification. NeurIPS (2021)
Claudia Shi, Victor Veitch, David M. Blei
Invariant representation learning for treatment effect estimation. UAI (2021)
Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests. CoRR (2021)
Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar
Causal Effects of Linguistic Properties. NAACL-HLT (2021)
Alexander D'Amour, Katherine A. 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, Yi-An Ma, Cory Y. 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
Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR (2020)
Victor Veitch, Anisha Zaveri
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding. NeurIPS (2020)
Victor Veitch, Dhanya Sridhar, David M. Blei
Adapting Text Embeddings for Causal Inference. UAI (2020)
Victor Veitch, Anisha Zaveri
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding. CoRR (2020)
Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar
Causal Effects of Linguistic Properties. CoRR (2020)
Jason S. Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown
Valid Causal Inference with (Some) Invalid Instruments. CoRR (2020)
Claudia Shi, Victor Veitch, David M. Blei
Invariant Representation Learning for Treatment Effect Estimation. CoRR (2020)
Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. AISTATS (2019)
Claudia Shi, David M. Blei, Victor Veitch
Adapting Neural Networks for the Estimation of Treatment Effects. NeurIPS (2019)
Claudia Shi, David M. Blei, Victor Veitch
Adapting Neural Networks for the Estimation of Treatment Effects. CoRR (2019)
Victor Veitch, Yixin Wang, David M. Blei
Using Embeddings to Correct for Unobserved Confounding. CoRR (2019)
Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz
Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach. ICLR (Poster) (2019)
Victor Veitch, Dhanya Sridhar, David M. Blei
Using Text Embeddings for Causal Inference. CoRR (2019)
Victor Veitch, Yixin Wang, David M. Blei
Using Embeddings to Correct for Unobserved Confounding in Networks. NeurIPS (2019)
Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. CoRR (2018)
Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz
Compressibility and Generalization in Large-Scale Deep Learning. CoRR (2018)
Victor Veitch, Daniel M. Roy
Sampling and Estimation for (Sparse) Exchangeable Graphs. CoRR (2016)
Mark Howard, Joel Wallman, Victor Veitch, Joseph Emerson
Contextuality Supplies the Magic for Quantum Computation. ISMVL (2015)
Victor Veitch, Daniel M. Roy
The Class of Random Graphs Arising from Exchangeable Random Measures. CoRR (2015)
Mark Howard, Joel Wallman, Victor Veitch, Joseph Emerson
Contextuality supplies the 'magic' for quantum computation. Nat. 510 (7505) (2014)