Victor Veitch

According to our database, Victor Veitch authored at least 36 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

RATE: Score Reward Models with Imperfect Rewrites of Rewrites.

David Reber, Sean Richardson, Todd Nief, Cristina Garbacea, Victor Veitch

CoRR, 2024

The Geometry of Categorical and Hierarchical Concepts in Large Language Models.

Kiho Park, Yo Joong Choe, Yibo Jiang, Victor Veitch

CoRR, 2024

BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling.

Lin Gui, Cristina Gârbacea, Victor Veitch

CoRR, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

The Linear Representation Hypothesis and the Geometry of Large Language Models.

Kiho Park, Yo Joong Choe, Victor Veitch

Proceedings of the Forty-first International Conference on Machine Learning, 2024

On the Origins of Linear Representations in Large Language Models.

Yibo Jiang, Goutham Rajendran, Pradeep Kumar Ravikumar, Bryon Aragam, Victor Veitch

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Concept Algebra for Text-Controlled Vision Models.

Zihao Wang, Lin Gui, Jeffrey Negrea, Victor Veitch

CoRR, 2023

Concept Algebra for (Score-Based) Text-Controlled Generative Models.

Zihao Wang, Lin Gui, Jeffrey Negrea, Victor Veitch

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Uncovering Meanings of Embeddings via Partial Orthogonality.

Yibo Jiang, Bryon Aragam, Victor Veitch

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness.

Jacy Reese Anthis, Victor Veitch

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficient Conditionally Invariant Representation Learning.

Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Causal Estimation for Text Data with (Apparent) Overlap Violations.

Lin Gui, Victor Veitch

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond.

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

Trans. Assoc. Comput. Linguistics, 2022

Underspecification Presents Challenges for Credibility in Modern Machine Learning.

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

J. Mach. Learn. Res., 2022

The Holdout Randomization Test for Feature Selection in Black Box Models.

Wesley Tansey, Victor Veitch, Haoran Zhang, Raul Rabadan, David M. Blei

J. Comput. Graph. Stat., 2022

A Unified Causal View of Domain Invariant Representation Learning.

Zihao Wang, Victor Veitch

CoRR, 2022

Invariant and Transportable Representations for Anti-Causal Domain Shifts.

Yibo Jiang, Victor Veitch

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Using Embeddings for Causal Estimation of Peer Influence in Social Networks.

Irina Cristali, Victor Veitch

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests.

Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein

CoRR, 2021

Assessing the Effects of Friend-to-Friend Texting onTurnout in the 2018 US Midterm Elections.

Aaron Schein, Keyon Vafa, Dhanya Sridhar, Victor Veitch, Jeffrey Quinn, James Moffet, David M. Blei, Donald P. Green

Proceedings of the WWW '21: The Web Conference 2021, 2021

Invariant representation learning for treatment effect estimation.

Claudia Shi, Victor Veitch, David M. Blei

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Counterfactual Invariance to Spurious Correlations in Text Classification.

Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Causal Effects of Linguistic Properties.

Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

Valid Causal Inference with (Some) Invalid Instruments.

Jason S. Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Adapting Text Embeddings for Causal Inference.

Victor Veitch, Dhanya Sridhar, David M. Blei

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding.

Victor Veitch, Anisha Zaveri

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Using Text Embeddings for Causal Inference.

Victor Veitch, Dhanya Sridhar, David M. Blei

CoRR, 2019

Using Embeddings to Correct for Unobserved Confounding.

Victor Veitch, Yixin Wang, David M. Blei

CoRR, 2019

Using Embeddings to Correct for Unobserved Confounding in Networks.

Victor Veitch, Yixin Wang, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Adapting Neural Networks for the Estimation of Treatment Effects.

Claudia Shi, David M. Blei, Victor Veitch

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 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

Proceedings of the 7th International Conference on Learning Representations, 2019

Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data.

Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Compressibility and Generalization in Large-Scale Deep Learning.

Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz

CoRR, 2018

2016

Sampling and Estimation for (Sparse) Exchangeable Graphs.

Victor Veitch, Daniel M. Roy

CoRR, 2016

2015

The Class of Random Graphs Arising from Exchangeable Random Measures.

Victor Veitch, Daniel M. Roy

CoRR, 2015

2014

Contextuality supplies the 'magic' for quantum computation.

Mark Howard, Joel Wallman, Victor Veitch, Joseph Emerson

Nat., 2014