IsaPlanner: A Prototype Proof Planner in Isabelle. - Lucas Dixon; Jacques D. Fleuriot (CADE, 2003)

Higher Order Rippling in IsaPlanner. - Lucas Dixon; Jacques D. Fleuriot (TPHOLs, 2004)

Deductive synthesis of workflows for e-Science. - Bin Yang; Alan Bundy; Alan Smaill; Lucas Dixon (CCGRID, 2005)

Constructing Induction Rules for Deductive Synthesis Proofs. - Alan Bundy; Lucas Dixon; Jeremy Gow; Jacques D. Fleuriot (CLASE, 2005)

Best-First Rippling. - Moa Johansson; Alan Bundy; Lucas Dixon (Reasoning, Action and Interaction in AI Theories and Systems, 2006)

A proof-centric approach to mathematical assistants. - Lucas Dixon; Jacques D. Fleuriot (J. Appl. Log., 2006)

A proof planning framework for Isabelle. - Lucas Dixon (2006)

Extending Graphical Representations for Compact Closed Categories with Applications to Symbolic Quantum Computation. - Lucas Dixon; Ross Duncan (AISC/MKM/Calculemus, 2008)

Graphical reasoning in compact closed categories for quantum computation. - Lucas Dixon; Ross Duncan (Ann. Math. Artif. Intell., 2009)

Plans, Actions and Dialogues Using Linear Logic. - Lucas Dixon; Alan Smaill; Tracy Tsang (J. Log. Lang. Inf., 2009)

Dynamic Rippling, Middle-Out Reasoning and Lemma Discovery. - Moa Johansson; Lucas Dixon; Alan Bundy (Verification, Induction, Termination Analysis, 2010)

Refinement and Term Synthesis in Loop Invariant Generation. - Ewen Maclean; Andrew Ireland; Lucas Dixon; Robert Atkey (WING@ETAPS/IJCAR, 2010)

Case-Analysis for Rippling and Inductive Proof. - Moa Johansson; Lucas Dixon; Alan Bundy (ITP, 2010)

Scheme-Based Synthesis of Inductive Theories. - Omar Montaño-Rivas; Roy L. McCasland; Lucas Dixon; Alan Bundy (MICAI (1), 2010)

Open Graphs and Computational Reasoning - Lucas Dixon; Ross Duncan; Aleks Kissinger (DCM, 2010)

Towards Formal Proof Script Refactoring. - Iain Whiteside; David Aspinall 001; Lucas Dixon; Gudmund Grov (Calculemus/MKM, 2011)

Conjecture Synthesis for Inductive Theories. - Moa Johansson; Lucas Dixon; Alan Bundy (J. Autom. Reason., 2011)

Scheme-based theorem discovery and concept invention. - Omar Montaño-Rivas; Roy L. McCasland; Lucas Dixon; Alan Bundy (Expert Syst. Appl., 2012)

Open-graphs and monoidal theories. - Lucas Dixon; Aleks Kissinger (Math. Struct. Comput. Sci., 2013)

The Theory behind Theory Mine. - Alan Bundy; Flaminia Cavallo; Lucas Dixon; Moa Johansson; Roy L. McCasland (IEEE Intell. Syst., 2015)

Network Traffic Obfuscation and Automated Internet Censorship. - Lucas Dixon; Thomas Ristenpart; Thomas Shrimpton (IEEE Secur. Priv., 2016)

Ex Machina: Personal Attacks Seen at Scale. - Ellery Wulczyn; Nithum Thain; Lucas Dixon (WWW, 2017)

Conversations Gone Awry: Detecting Early Signs of Conversational Failure. - Justine Zhang; Jonathan P. Chang; Cristian Danescu-Niculescu-Mizil; Lucas Dixon; Yiqing Hua; Dario Taraborelli; Nithum Thain (ACL (1), 2018)

Measuring and Mitigating Unintended Bias in Text Classification. - Lucas Dixon; John Li; Jeffrey Sorensen; Nithum Thain; Lucy Vasserman (AIES, 2018)

Managing Deviant Behavior in Online Communities III. - Amy S. Bruckman; Jennifer E. Below; Lucas Dixon; Casey Fiesler; Eric E. Gilbert; Sarah A. Gilbert; J. Nathan Matias (CHI Extended Abstracts, 2018)

WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community. - Yiqing Hua; Cristian Danescu-Niculescu-Mizil; Dario Taraborelli; Nithum Thain; Jeffery Sorensen; Lucas Dixon (EMNLP, 2018)

ConvAI at SemEval-2019 Task 6: Offensive Language Identification and Categorization with Perspective and BERT. - John Pavlopoulos; Nithum Thain; Lucas Dixon; Ion Androutsopoulos (SemEval@NAACL-HLT, 2019)

Crowdsourcing Subjective Tasks: The Case Study of Understanding Toxicity in Online Discussions. - Lora Aroyo; Lucas Dixon; Nithum Thain; Olivia Redfield; Rachel Rosen (WWW (Companion Volume), 2019)

Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification. - Daniel Borkan; Lucas Dixon; Jeffrey Sorensen; Nithum Thain; Lucy Vasserman (WWW (Companion Volume), 2019)

Limitations of Pinned AUC for Measuring Unintended Bias. - Daniel Borkan; Lucas Dixon; John Li; Jeffrey Sorensen; Nithum Thain; Lucy Vasserman (CoRR, 2019)

Six Attributes of Unhealthy Conversations. - Ilan Price; Jordan Gifford-Moore; Jory Flemming; Saul Musker; Maayan Roichman; Guillaume Sylvain; Nithum Thain; Lucas Dixon; Jeffrey Sorensen (WOAH, 2020)

Toxicity Detection: Does Context Really Matter? - John Pavlopoulos; Jeffrey Sorensen; Lucas Dixon; Nithum Thain; Ion Androutsopoulos (ACL, 2020)

Six Attributes of Unhealthy Conversation. - Ilan Price; Jordan Gifford-Moore; Jory Flemming; Saul Musker; Maayan Roichman; Guillaume Sylvain; Nithum Thain; Lucas Dixon; Jeffrey Sorensen (CoRR, 2020)

Civil Rephrases Of Toxic Texts With Self-Supervised Transformers. - Léo Laugier; John Pavlopoulos; Jeffrey Sorensen; Lucas Dixon (EACL, 2021)

Augmenting the User-Item Graph with Textual Similarity Models. - Federico López 002; Martin Scholz; Jessica Yung; Marie Pellat; Michael Strube 001; Lucas Dixon (CoRR, 2021)

Toxicity Detection can be Sensitive to the Conversational Context. - Alexandros Xenos; John Pavlopoulos; Ion Androutsopoulos; Lucas Dixon; Jeffrey Sorensen; Léo Laugier (CoRR, 2021)

GLaM: Efficient Scaling of Language Models with Mixture-of-Experts. - Nan Du 002; Yanping Huang; Andrew M. Dai; Simon Tong; Dmitry Lepikhin; Yuanzhong Xu; Maxim Krikun; Yanqi Zhou; Adams Wei Yu; Orhan Firat; Barret Zoph; Liam Fedus; et al. (ICML, 2022)

Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis. - Shayegan Omidshafiei; Andrei Kapishnikov; Yannick Assogba; Lucas Dixon; Been Kim (NeurIPS, 2022)

On Natural Language User Profiles for Transparent and Scrutable Recommendation. - Filip Radlinski; Krisztian Balog; Fernando Diaz 001; Lucas Dixon; Ben Wedin (SIGIR, 2022)

Basic Elements of Logical Graphs. - Lucas Dixon (CoRR, 2022)

Toxicity detection sensitive to conversational context. - Alexandros Xenos; John Pavlopoulos; Ion Androutsopoulos; Lucas Dixon; Jeffrey Sorensen; Léo Laugier (First Monday, 2022)

KNNs of Semantic Encodings for Rating Prediction. - Léo Laugier; Raghuram Vadapalli; Thomas Bonald; Lucas Dixon (CIC, 2023)

Towards Agile Text Classifiers for Everyone. - Maximilian Mozes; Jessica Hoffmann; Katrin Tomanek; Muhamed Kouate; Nithum Thain; Ann Yuan; Tolga Bolukbasi; Lucas Dixon (EMNLP (Findings), 2023)

Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences. - Scott Sanner; Krisztian Balog; Filip Radlinski; Ben Wedin; Lucas Dixon (RecSys, 2023)

JUAGE at SemEval-2023 Task 10: Parameter Efficient Classification. - Jeffrey Sorensen; Katerina Korre; John Pavlopoulos; Katrin Tomanek; Nithum Thain; Lucas Dixon; Léo Laugier (SemEval@ACL, 2023)

Gradient-Based Automated Iterative Recovery for Parameter-Efficient Tuning. - Maximilian Mozes; Tolga Bolukbasi; Ann Yuan; Frederick Liu; Nithum Thain; Lucas Dixon (CoRR, 2023)

Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs. - Kelvin Guu; Albert Webson; Ellie Pavlick; Lucas Dixon; Ian Tenney; Tolga Bolukbasi (CoRR, 2023)

Large Language Models for User Interest Journeys. - Konstantina Christakopoulou; Alberto Lalama; Cj Adams; Iris Qu; Yifat Amir; Samer Chucri; Pierce Vollucci; Fabio Soldo; Dina Bseiso; Sarah Scodel; Lucas Dixon; Ed H. Chi; et al. (CoRR, 2023)

AI Alignment in the Design of Interactive AI: Specification Alignment, Process Alignment, and Evaluation Support. - Michael Terry; Chinmay Kulkarni 001; Martin Wattenberg; Lucas Dixon; Meredith Ringel Morris (CoRR, 2023)

Classifying constructive comments. - Varada Kolhatkar; Nithum Thain; Jeffrey Sorensen; Lucas Dixon; Maite Taboada (First Monday, 2023)

LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models. - Minsuk Kahng; Ian Tenney; Mahima Pushkarna; Michael Xieyang Liu; James Wexler; Emily Reif; Krystal Kallarackal; Minsuk Chang; Michael Terry; Lucas Dixon (CHI Extended Abstracts, 2024)

"We Need Structured Output": Towards User-centered Constraints on Large Language Model Output. - Michael Xieyang Liu; Frederick Liu; Alexander J. Fiannaca; Terry Koo; Lucas Dixon; Michael Terry; Carrie J. Cai (CHI Extended Abstracts, 2024)

Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics. - Tyler A. Chang; Katrin Tomanek; Jessica Hoffmann; Nithum Thain; Erin MacMurray van Liemt; Kathleen S. Meier-Hellstern; Lucas Dixon (LREC/COLING, 2024)

Interpretability Illusions in the Generalization of Simplified Models. - Dan Friedman; Andrew Kyle Lampinen; Lucas Dixon; Danqi Chen 001; Asma Ghandeharioun (ICML, 2024)

Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models. - Asma Ghandeharioun; Avi Caciularu; Adam Pearce; Lucas Dixon; Mor Geva (ICML, 2024)

Decoding-time Realignment of Language Models. - Tianlin Liu; Shangmin Guo; Leonardo Bianco; Daniele Calandriello; Quentin Berthet; Felipe Llinares-López; Jessica Hoffmann; Lucas Dixon; Michal Valko; Mathieu Blondel (ICML, 2024)

Who's asking? User personas and the mechanics of latent misalignment. - Asma Ghandeharioun; Ann Yuan; Marius Guerard; Emily Reif; Michael A. Lepori; Lucas Dixon (NeurIPS, 2024)

PERL: Parameter Efficient Reinforcement Learning from Human Feedback. - Hakim Sidahmed; Samrat Phatale; Alex Hutcheson; Zhuonan Lin; Zhang Chen; Zac Yu; Jarvis Jin; Roman Komarytsia; Christiane Ahlheim; Yonghao Zhu; Simral Chaudhary; Bowen Li; et al. (CoRR, 2024)

Interactive Prompt Debugging with Sequence Salience. - Ian Tenney; Ryan Mullins; Bin Du; Shree Pandya; Minsuk Kahng; Lucas Dixon (CoRR, 2024)

Scalable Influence and Fact Tracing for Large Language Model Pretraining. - Tyler A. Chang; Dheeraj Rajagopal; Tolga Bolukbasi; Lucas Dixon; Ian Tenney (ICLR, 2025)

LLM Comparator: Interactive Analysis of Side-by-Side Evaluation of Large Language Models. - Minsuk Kahng; Ian Tenney; Mahima Pushkarna; Michael Xieyang Liu; James Wexler; Emily Reif; Krystal Kallarackal; Minsuk Chang; Michael Terry; Lucas Dixon (IEEE Trans. Vis. Comput. Graph., 2025)

Improving Neutral Point of View Text Generation through Parameter-Efficient Reinforcement Learning and a Small-Scale High-Quality Dataset. - Jessica Hoffmann; Christiane Ahlheim; Zac Yu; Aria Walfrand; Jarvis Jin; Marie Tano; Ahmad Beirami; Erin van Liemt; Nithum Thain; Hakim Sidahmed; Lucas Dixon (CoRR, 2025)

Beyond the Rosetta Stone: Unification Forces in Generalization Dynamics. - Carter Blum; Katja Filippova; Ann Yuan; Asma Ghandeharioun; Julian Zimmert; Fred Zhang; Jessica Hoffmann; Tal Linzen; Martin Wattenberg; Lucas Dixon; Mor Geva (CoRR, 2025)