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Victor Veitch
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2020 – today
- 2024
- [c22]Yibo Jiang, Goutham Rajendran, Pradeep Kumar Ravikumar, Bryon Aragam, Victor Veitch:
On the Origins of Linear Representations in Large Language Models. ICML 2024 - [c21]Kiho Park, Yo Joong Choe, Victor Veitch:
The Linear Representation Hypothesis and the Geometry of Large Language Models. ICML 2024 - [c20]Zihao Wang, Chirag Nagpal, Jonathan Berant, Jacob Eisenstein, Alexander Nicholas D'Amour, Sanmi Koyejo, Victor Veitch:
Transforming and Combining Rewards for Aligning Large Language Models. ICML 2024 - [i28]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 abs/2402.00742 (2024) - [i27]Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam, Victor Veitch:
On the Origins of Linear Representations in Large Language Models. CoRR abs/2403.03867 (2024) - [i26]Lin Gui, Cristina Gârbacea, Victor Veitch:
BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling. CoRR abs/2406.00832 (2024) - [i25]Kiho Park, Yo Joong Choe, Yibo Jiang, Victor Veitch:
The Geometry of Categorical and Hierarchical Concepts in Large Language Models. CoRR abs/2406.01506 (2024) - [i24]David Reber, Sean Richardson, Todd Nief, Cristina Garbacea, Victor Veitch:
RATE: Score Reward Models with Imperfect Rewrites of Rewrites. CoRR abs/2410.11348 (2024) - 2023
- [c19]Lin Gui, Victor Veitch:
Causal Estimation for Text Data with (Apparent) Overlap Violations. ICLR 2023 - [c18]Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton:
Efficient Conditionally Invariant Representation Learning. ICLR 2023 - [c17]Jacy Reese Anthis, Victor Veitch:
Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness. NeurIPS 2023 - [c16]Yibo Jiang, Bryon Aragam, Victor Veitch:
Uncovering Meanings of Embeddings via Partial Orthogonality. NeurIPS 2023 - [c15]Zihao Wang, Lin Gui, Jeffrey Negrea, Victor Veitch:
Concept Algebra for (Score-Based) Text-Controlled Generative Models. NeurIPS 2023 - [i23]Zihao Wang, Lin Gui, Jeffrey Negrea, Victor Veitch:
Concept Algebra for Text-Controlled Vision Models. CoRR abs/2302.03693 (2023) - [i22]Yibo Jiang, Bryon Aragam, Victor Veitch:
Uncovering Meanings of Embeddings via Partial Orthogonality. CoRR abs/2310.17611 (2023) - [i21]Jacy Reese Anthis, Victor Veitch:
Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness. CoRR abs/2310.19691 (2023) - [i20]Kiho Park, Yo Joong Choe, Victor Veitch:
The Linear Representation Hypothesis and the Geometry of Large Language Models. CoRR abs/2311.03658 (2023) - 2022
- [j4]Wesley Tansey
, Victor Veitch, Haoran Zhang
The Holdout Randomization Test for Feature Selection in Black Box Models. J. Comput. Graph. Stat. 31(1): 151-162 (2022) - [j3]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: 226:1-226:61 (2022) - [j2]Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond. Trans. Assoc. Comput. Linguistics 10: 1138-1158 (2022) - [c14]Irina Cristali, Victor Veitch:
Using Embeddings for Causal Estimation of Peer Influence in Social Networks. NeurIPS 2022 - [c13]Yibo Jiang, Victor Veitch:
Invariant and Transportable Representations for Anti-Causal Domain Shifts. NeurIPS 2022 - [i19]Irina Cristali, Victor Veitch:
Using Embeddings for Causal Estimation of Peer Influence in Social Networks. CoRR abs/2205.08033 (2022) - [i18]Yibo Jiang, Victor Veitch:
Invariant and Transportable Representations for Anti-Causal Domain Shifts. CoRR abs/2207.01603 (2022) - [i17]Zihao Wang, Victor Veitch:
A Unified Causal View of Domain Invariant Representation Learning. CoRR abs/2208.06987 (2022) - [i16]Lin Gui, Victor Veitch:
Causal Estimation for Text Data with (Apparent) Overlap Violations. CoRR abs/2210.00079 (2022) - [i15]Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton:
Efficient Conditionally Invariant Representation Learning. CoRR abs/2212.08645 (2022) - 2021
- [c12]Jason S. Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown:
Valid Causal Inference with (Some) Invalid Instruments. ICML 2021: 4096-4106 - [c11]Reid Pryzant, Dallas Card
Causal Effects of Linguistic Properties. NAACL-HLT 2021: 4095-4109 - [c10]Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein:
Counterfactual Invariance to Spurious Correlations in Text Classification. NeurIPS 2021: 16196-16208 - [c9]Claudia Shi, Victor Veitch, David M. Blei:
Invariant representation learning for treatment effect estimation. UAI 2021: 1546-1555 - [c8]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: 2025-2036 - [i14]Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein:
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests. CoRR abs/2106.00545 (2021) - [i13]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 abs/2109.00725 (2021) - 2020
- [c7]Victor Veitch, Anisha Zaveri:
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding. NeurIPS 2020 - [c6]Victor Veitch, Dhanya Sridhar, David M. Blei:
Adapting Text Embeddings for Causal Inference. UAI 2020: 919-928 - [i12]Victor Veitch, Anisha Zaveri:
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding. CoRR abs/2003.01747 (2020) - [i11]Jason S. Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown:
Valid Causal Inference with (Some) Invalid Instruments. CoRR abs/2006.11386 (2020) - [i10]Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar:
Causal Effects of Linguistic Properties. CoRR abs/2010.12919 (2020) - [i9]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
Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR abs/2011.03395 (2020) - [i8]Claudia Shi, Victor Veitch, David M. Blei:
Invariant Representation Learning for Treatment Effect Estimation. CoRR abs/2011.12379 (2020)
2010 – 2019
- 2019
- [c5]Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz:
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. AISTATS 2019: 1733-1742 - [c4]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 - [c3]Claudia Shi, David M. Blei, Victor Veitch:
Adapting Neural Networks for the Estimation of Treatment Effects. NeurIPS 2019: 2503-2513 - [c2]Victor Veitch, Yixin Wang, David M. Blei:
Using Embeddings to Correct for Unobserved Confounding in Networks. NeurIPS 2019: 13769-13779 - [i7]Victor Veitch, Yixin Wang, David M. Blei:
Using Embeddings to Correct for Unobserved Confounding. CoRR abs/1902.04114 (2019) - [i6]Victor Veitch, Dhanya Sridhar, David M. Blei:
Using Text Embeddings for Causal Inference. CoRR abs/1905.12741 (2019) - [i5]Claudia Shi, David M. Blei, Victor Veitch:
Adapting Neural Networks for the Estimation of Treatment Effects. CoRR abs/1906.02120 (2019) - 2018
- [i4]Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz:
Compressibility and Generalization in Large-Scale Deep Learning. CoRR abs/1804.05862 (2018) - [i3]Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz:
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. CoRR abs/1806.10701 (2018) - 2016
- [i2]Victor Veitch, Daniel M. Roy:
Sampling and Estimation for (Sparse) Exchangeable Graphs. CoRR abs/1611.00843 (2016) - 2015
- [c1]Mark Howard, Joel Wallman, Victor Veitch, Joseph Emerson:
Contextuality Supplies the Magic for Quantum Computation. ISMVL 2015: 96 - [i1]Victor Veitch, Daniel M. Roy:
The Class of Random Graphs Arising from Exchangeable Random Measures. CoRR abs/1512.03099 (2015) - 2014
- [j1]Mark Howard
Contextuality supplies the 'magic' for quantum computation. Nat. 510(7505): 351-355 (2014)
Coauthor Index
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