Contextuality supplies the 'magic' for quantum computation. - Mark Howard; Joel Wallman 002; Victor Veitch; Joseph Emerson (Nat., 2014)

The Class of Random Graphs Arising from Exchangeable Random Measures. - Victor Veitch; Daniel M. Roy 001 (CoRR, 2015)

Sampling and Estimation for (Sparse) Exchangeable Graphs. - Victor Veitch; Daniel M. Roy 001 (CoRR, 2016)

Compressibility and Generalization in Large-Scale Deep Learning. - Wenda Zhou; Victor Veitch; Morgane Austern; Ryan P. Adams; Peter Orbanz (CoRR, 2018)

Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. - Victor Veitch; Morgane Austern; Wenda Zhou; David M. Blei; Peter Orbanz (AISTATS, 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 (ICLR (Poster), 2019)

Adapting Neural Networks for the Estimation of Treatment Effects. - Claudia Shi; David M. Blei; Victor Veitch (NeurIPS, 2019)

Using Embeddings to Correct for Unobserved Confounding in Networks. - Victor Veitch; Yixin Wang 002; David M. Blei (NeurIPS, 2019)

Using Embeddings to Correct for Unobserved Confounding. - Victor Veitch; Yixin Wang 002; David M. Blei (CoRR, 2019)

Using Text Embeddings for Causal Inference. - Victor Veitch; Dhanya Sridhar; David M. Blei (CoRR, 2019)

Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding. - Victor Veitch; Anisha Zaveri (NeurIPS, 2020)

Adapting Text Embeddings for Causal Inference. - Victor Veitch; Dhanya Sridhar; David M. Blei (UAI, 2020)

Valid Causal Inference with (Some) Invalid Instruments. - Jason S. Hartford; Victor Veitch; Dhanya Sridhar; Kevin Leyton-Brown (ICML, 2021)

Causal Effects of Linguistic Properties. - Reid Pryzant; Dallas Card; Dan Jurafsky; Victor Veitch; Dhanya Sridhar (NAACL-HLT, 2021)

Counterfactual Invariance to Spurious Correlations in Text Classification. - Victor Veitch; Alexander D'Amour; Steve Yadlowsky; Jacob Eisenstein (NeurIPS, 2021)

Invariant representation learning for treatment effect estimation. - Claudia Shi; Victor Veitch; David M. Blei (UAI, 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 (WWW, 2021)

Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests. - Victor Veitch; Alexander D'Amour; Steve Yadlowsky; Jacob Eisenstein (CoRR, 2021)

Using Embeddings for Causal Estimation of Peer Influence in Social Networks. - Irina Cristali; Victor Veitch (NeurIPS, 2022)

Invariant and Transportable Representations for Anti-Causal Domain Shifts. - Yibo Jiang; Victor Veitch (NeurIPS, 2022)

A Unified Causal View of Domain Invariant Representation Learning. - Zihao Wang; Victor Veitch (CoRR, 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)

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; et al. (J. Mach. Learn. Res., 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; et al. (Trans. Assoc. Comput. Linguistics, 2022)

Causal Estimation for Text Data with (Apparent) Overlap Violations. - Lin Gui 007; Victor Veitch (ICLR, 2023)

Efficient Conditionally Invariant Representation Learning. - Roman Pogodin; Namrata Deka; Yazhe Li; Danica J. Sutherland; Victor Veitch; Arthur Gretton (ICLR, 2023)

Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness. - Jacy Reese Anthis; Victor Veitch (NeurIPS, 2023)

Uncovering Meanings of Embeddings via Partial Orthogonality. - Yibo Jiang; Bryon Aragam; Victor Veitch (NeurIPS, 2023)

Concept Algebra for (Score-Based) Text-Controlled Generative Models. - Zihao Wang; Lin Gui 007; Jeffrey Negrea; Victor Veitch (NeurIPS, 2023)

Concept Algebra for Text-Controlled Vision Models. - Zihao Wang; Lin Gui 007; Jeffrey Negrea; Victor Veitch (CoRR, 2023)

On the Origins of Linear Representations in Large Language Models. - Yibo Jiang; Goutham Rajendran; Pradeep Kumar Ravikumar; Bryon Aragam; Victor Veitch (ICML, 2024)

The Linear Representation Hypothesis and the Geometry of Large Language Models. - Kiho Park 001; 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 (ICML, 2024)

BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling. - Lin Gui 007; Cristina Garbacea; Victor Veitch (NeurIPS, 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 001; Yo Joong Choe; Yibo Jiang; Victor Veitch (ICLR, 2025)

Does Editing Provide Evidence for Localization? - Zihao Wang; Victor Veitch (CoRR, 2025)

Incorporating Hierarchical Semantics in Sparse Autoencoder Architectures. - Mark Muchane; Sean Richardson; Kiho Park 001; Victor Veitch (CoRR, 2025)