Victor Veitch - RankMe

63 papers A* 19A 3Journal 38Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Expected Reward Prediction, with Applications to Model Routing. CoRR Kenan Hasanaliyev, Silas Alberti, Jenny Hamer, Dheeraj Rajagopal, Kevin Robinson, Jasper Snoek, Victor Veitch, Alexander Nicholas D'Amour
2026	J	`jnl`	The Information Geometry of Softmax: Probing and Steering. CoRR Kiho Park, Todd Nief, Yo Joong Choe, Victor Veitch
2025	J	`jnl`	Chasing the Tail: Effective Rubric-based Reward Modeling for Large Language Model Post-Training. CoRR Junkai Zhang, Zihao Wang, Lin Gui, Swarnashree Mysore Sathyendra, Jaehwan Jeong, Victor Veitch, Wei Wang, Yunzhong He, Bing Liu, Lifeng Jin
2025	J	`jnl`	Does Editing Provide Evidence for Localization? CoRR Zihao Wang, Victor Veitch
2025	J	`jnl`	Incorporating Hierarchical Semantics in Sparse Autoencoder Architectures. CoRR Mark Muchane, Sean M. Richardson, Kiho Park, Victor Veitch
2025	J	`jnl`	Let it Calm: Exploratory Annealed Decoding for Verifiable Reinforcement Learning. CoRR Chenghao Yang, Lin Gui, Chenxiao Yang, Victor Veitch, Lizhu Zhang, Zhuokai Zhao
2025	A*	`conf`	RATE: Causal Explainability of Reward Models with Imperfect Counterfactuals. ICML David Reber, Sean M. Richardson, Todd Nief, Cristina Garbacea, Victor Veitch
2025	A*	`conf`	The Geometry of Categorical and Hierarchical Concepts in Large Language Models. ICLR Kiho Park, Yo Joong Choe, Yibo Jiang, Victor Veitch
2024	A*	`conf`	BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling. NeurIPS Lin Gui, Cristina Garbacea, Victor Veitch
2024	J	`jnl`	BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling. CoRR Lin Gui, Cristina Gârbacea, Victor Veitch
2024	A*	`conf`	On the Origins of Linear Representations in Large Language Models. ICML Yibo Jiang, Goutham Rajendran, Pradeep Kumar Ravikumar, Bryon Aragam, Victor Veitch
2024	J	`jnl`	On the Origins of Linear Representations in Large Language Models. CoRR Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam, Victor Veitch
2024	J	`jnl`	RATE: Score Reward Models with Imperfect Rewrites of Rewrites. CoRR David Reber, Sean M. Richardson, Todd Nief, Cristina Garbacea, Victor Veitch
2024	J	`jnl`	The Geometry of Categorical and Hierarchical Concepts in Large Language Models. CoRR Kiho Park, Yo Joong Choe, Yibo Jiang, Victor Veitch
2024	A*	`conf`	The Linear Representation Hypothesis and the Geometry of Large Language Models. ICML Kiho Park, Yo Joong Choe, Victor Veitch
2024	A*	`conf`	Transforming and Combining Rewards for Aligning Large Language Models. ICML Zihao Wang, Chirag Nagpal, Jonathan Berant, Jacob Eisenstein, Alexander Nicholas D'Amour, Sanmi Koyejo, Victor Veitch
2024	J	`jnl`	Transforming and Combining Rewards for Aligning Large Language Models. CoRR Zihao Wang, Chirag Nagpal, Jonathan Berant, Jacob Eisenstein, Alex D'Amour, Sanmi Koyejo, Victor Veitch
2023	A*	`conf`	Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness. NeurIPS Jacy Reese Anthis, Victor Veitch
2023	J	`jnl`	Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness. CoRR Jacy Reese Anthis, Victor Veitch
2023	A*	`conf`	Causal Estimation for Text Data with (Apparent) Overlap Violations. ICLR Lin Gui, Victor Veitch
2023	A*	`conf`	Concept Algebra for (Score-Based) Text-Controlled Generative Models. NeurIPS Zihao Wang, Lin Gui, Jeffrey Negrea, Victor Veitch
2023	J	`jnl`	Concept Algebra for Text-Controlled Vision Models. CoRR Zihao Wang, Lin Gui, Jeffrey Negrea, Victor Veitch
2023	A*	`conf`	Efficient Conditionally Invariant Representation Learning. ICLR Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton
2023	J	`jnl`	The Linear Representation Hypothesis and the Geometry of Large Language Models. CoRR Kiho Park, Yo Joong Choe, Victor Veitch
2023	A*	`conf`	Uncovering Meanings of Embeddings via Partial Orthogonality. NeurIPS Yibo Jiang, Bryon Aragam, Victor Veitch
2023	J	`jnl`	Uncovering Meanings of Embeddings via Partial Orthogonality. CoRR Yibo Jiang, Bryon Aragam, Victor Veitch
2022	J	`jnl`	A Unified Causal View of Domain Invariant Representation Learning. CoRR Zihao Wang, Victor Veitch
2022	J	`jnl`	Causal Estimation for Text Data with (Apparent) Overlap Violations. CoRR Lin Gui, Victor Veitch
2022	J	`jnl`	Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond. Trans. Assoc. Comput. Linguistics 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
2022	J	`jnl`	Efficient Conditionally Invariant Representation Learning. CoRR Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton
2022	A*	`conf`	Invariant and Transportable Representations for Anti-Causal Domain Shifts. NeurIPS Yibo Jiang, Victor Veitch
2022	J	`jnl`	Invariant and Transportable Representations for Anti-Causal Domain Shifts. CoRR Yibo Jiang, Victor Veitch
2022	J	`jnl`	The Holdout Randomization Test for Feature Selection in Black Box Models. J. Comput. Graph. Stat. Wesley Tansey, Victor Veitch, Haoran Zhang, Raul Rabadan, David M. Blei
2022	J	`jnl`	Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 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
2022	A*	`conf`	Using Embeddings for Causal Estimation of Peer Influence in Social Networks. NeurIPS Irina Cristali, Victor Veitch
2022	J	`jnl`	Using Embeddings for Causal Estimation of Peer Influence in Social Networks. CoRR Irina Cristali, Victor Veitch
2021	A*	`conf`	Assessing the Effects of Friend-to-Friend Texting onTurnout in the 2018 US Midterm Elections. WWW Aaron Schein, Keyon Vafa, Dhanya Sridhar, Victor Veitch, Jeffrey Quinn, James Moffet, David M. Blei, Donald P. Green
2021	—	`conf`	Causal Effects of Linguistic Properties. NAACL-HLT Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar
2021	J	`jnl`	Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond. CoRR 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
2021	A*	`conf`	Counterfactual Invariance to Spurious Correlations in Text Classification. NeurIPS Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein
2021	J	`jnl`	Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests. CoRR Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein
2021	A	`conf`	Invariant representation learning for treatment effect estimation. UAI Claudia Shi, Victor Veitch, David M. Blei
2021	A*	`conf`	Valid Causal Inference with (Some) Invalid Instruments. ICML Jason S. Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown
2020	A	`conf`	Adapting Text Embeddings for Causal Inference. UAI Victor Veitch, Dhanya Sridhar, David M. Blei
2020	J	`jnl`	Causal Effects of Linguistic Properties. CoRR Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar
2020	J	`jnl`	Invariant Representation Learning for Treatment Effect Estimation. CoRR Claudia Shi, Victor Veitch, David M. Blei
2020	A*	`conf`	Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding. NeurIPS Victor Veitch, Anisha Zaveri
2020	J	`jnl`	Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding. CoRR Victor Veitch, Anisha Zaveri
2020	J	`jnl`	Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR 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
2020	J	`jnl`	Valid Causal Inference with (Some) Invalid Instruments. CoRR Jason S. Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown
2019	A*	`conf`	Adapting Neural Networks for the Estimation of Treatment Effects. NeurIPS Claudia Shi, David M. Blei, Victor Veitch
2019	J	`jnl`	Adapting Neural Networks for the Estimation of Treatment Effects. CoRR Claudia Shi, David M. Blei, Victor Veitch
2019	A	`conf`	Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. AISTATS Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz
2019	—	`conf`	Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach. ICLR (Poster) Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz
2019	A*	`conf`	Using Embeddings to Correct for Unobserved Confounding in Networks. NeurIPS Victor Veitch, Yixin Wang, David M. Blei
2019	J	`jnl`	Using Embeddings to Correct for Unobserved Confounding. CoRR Victor Veitch, Yixin Wang, David M. Blei
2019	J	`jnl`	Using Text Embeddings for Causal Inference. CoRR Victor Veitch, Dhanya Sridhar, David M. Blei
2018	J	`jnl`	Compressibility and Generalization in Large-Scale Deep Learning. CoRR Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz
2018	J	`jnl`	Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. CoRR Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz
2016	J	`jnl`	Sampling and Estimation for (Sparse) Exchangeable Graphs. CoRR Victor Veitch, Daniel M. Roy
2015	—	`conf`	Contextuality Supplies the Magic for Quantum Computation. ISMVL Mark Howard, Joel Wallman, Victor Veitch, Joseph Emerson
2015	J	`jnl`	The Class of Random Graphs Arising from Exchangeable Random Measures. CoRR Victor Veitch, Daniel M. Roy
2014	J	`jnl`	Contextuality supplies the 'magic' for quantum computation. Nat. Mark Howard, Joel Wallman, Victor Veitch, Joseph Emerson