Taint Tracking for WebAssembly. - Aron Szanto; Timothy Tamm; Artidoro Pagnoni (CoRR, 2018)

Conditional Variational Autoencoder for Neural Machine Translation. - Artidoro Pagnoni; Kevin Liu; Shangyan Li (CoRR, 2018)

PAC Learning Guarantees Under Covariate Shift. - Artidoro Pagnoni; Stefan Gramatovici; Samuel Liu (CoRR, 2018)

Machine Learning at Microsoft with ML.NET. - Zeeshan Ahmed; Saeed Amizadeh; Mikhail Bilenko; Rogan Carr; Wei-Sheng Chin; Yael Dekel; Xavier Dupré; Vadim Eksarevskiy; Senja Filipi; Tom Finley; Abhishek Goswami; Monte Hoover; et al. (KDD, 2019)

Analyzing Branch-and-Bound Algorithms for the Multiprocessor Scheduling Problem. - Thomas Lively; William Long; Artidoro Pagnoni (CoRR, 2019)

Machine Learning at Microsoft with ML .NET. - Zeeshan Ahmed; Saeed Amizadeh; Mikhail Bilenko; Rogan Carr; Wei-Sheng Chin; Yael Dekel; Xavier Dupré; Vadim Eksarevskiy; Eric Erhardt; Costin Eseanu; Senja Filipi; Tom Finley; et al. (CoRR, 2019)

Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach. - Gyeong-In Yu; Saeed Amizadeh; Artidoro Pagnoni; Byung-Gon Chun; Markus Weimer; Matteo Interlandi (CoRR, 2019)

Definition Frames: Using Definitions for Hybrid Concept Representations. - Evangelia Spiliopoulou; Artidoro Pagnoni; Eduard H. Hovy (COLING, 2020)

StructSum: Incorporating Latent and Explicit Sentence Dependencies for Single Document Summarization. - Vidhisha Balachandran; Artidoro Pagnoni; Jay Yoon Lee; Dheeraj Rajagopal; Jaime G. Carbonell; Yulia Tsvetkov (CoRR, 2020)

StructSum: Summarization via Structured Representations. - Vidhisha Balachandran; Artidoro Pagnoni; Jay Yoon Lee; Dheeraj Rajagopal; Jaime G. Carbonell; Yulia Tsvetkov (EACL, 2021)

Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics. - Artidoro Pagnoni; Vidhisha Balachandran; Yulia Tsvetkov (NAACL-HLT, 2021)

WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Modele. - Gyeong-In Yu; Saeed Amizadeh; Sehoon Kim 001; Artidoro Pagnoni; Ce Zhang 001; Byung-Gon Chun; Markus Weimer; Matteo Interlandi (Proc. VLDB Endow., 2021)

Threat Scenarios and Best Practices to Detect Neural Fake News. - Artidoro Pagnoni; Martin Graciarena; Yulia Tsvetkov (COLING, 2022)

EvEntS ReaLM: Event Reasoning of Entity States via Language Models. - Evangelia Spiliopoulou; Artidoro Pagnoni; Yonatan Bisk; Eduard H. Hovy (EMNLP, 2022)

Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization. - Artidoro Pagnoni; Alexander R. Fabbri; Wojciech Kryscinski; Chien-Sheng Wu (ACL (1), 2023)

QLoRA: Efficient Finetuning of Quantized LLMs. - Tim Dettmers; Artidoro Pagnoni; Ari Holtzman; Luke Zettlemoyer (NeurIPS, 2023)

Predicting vs. Acting: A Trade-off Between World Modeling & Agent Modeling. - Margaret Li; Weijia Shi; Artidoro Pagnoni; Peter West; Ari Holtzman (CoRR, 2024)

Byte Latent Transformer: Patches Scale Better Than Tokens. - Artidoro Pagnoni; Ramakanth Pasunuru; Pedro Rodríguez 001; John Nguyen; Benjamin Muller; Margaret Li; Chunting Zhou; Lili Yu; Jason E. Weston; Luke Zettlemoyer; Gargi Ghosh; Mike Lewis; et al. (ACL (1), 2025)