Estimating the progress of MapReduce pipelines. - Kristi Morton; Abram L. Friesen; Magdalena Balazinska; Dan Grossman (ICDE, 2010)

Imitation learning with hierarchical actions. - Abram L. Friesen; Rajesh P. N. Rao (ICDL, 2010)

Gaze Following as Goal Inference: A Bayesian Model. - Abram L. Friesen; Rajesh P. N. Rao (CogSci, 2011)

An ideal observer model for identifying the reference frame of objects. - Joseph L. Austerweil; Abram L. Friesen; Thomas L. Griffiths 001 (NIPS, 2011)

How Prior Probability Influences Decision Making: A Unifying Probabilistic Model. - Yanping Huang; Abram L. Friesen; Timothy D. Hanks; Michael N. Shadlen; Rajesh P. N. Rao (NIPS, 2012)

Recursive Decomposition for Nonconvex Optimization - IJCAI-15 Distinguished Paper. - Abram L. Friesen; Pedro M. Domingos (IJCAI, 2015)

The Sum-Product Theorem: A Foundation for Learning Tractable Models. - Abram L. Friesen; Pedro M. Domingos (ICML, 2016)

Recursive Decomposition for Nonconvex Optimization. - Abram L. Friesen; Pedro M. Domingos (CoRR, 2016)

The Sum-Product Theorem and its Applications. - Abram L. Friesen (2017)

Deep Learning as a Mixed Convex-Combinatorial Optimization Problem. - Abram L. Friesen; Pedro M. Domingos (ICLR (Poster), 2018)

Submodular Field Grammars: Representation, Inference, and Application to Image Parsing. - Abram L. Friesen; Pedro M. Domingos (NeurIPS, 2018)

Modular Meta-Learning with Shrinkage. - Yutian Chen 001; Abram L. Friesen; Feryal M. P. Behbahani; Arnaud Doucet; David Budden; Matthew Hoffman 002; Nando de Freitas (NeurIPS, 2020)

On the role of planning in model-based deep reinforcement learning. - Jessica B. Hamrick; Abram L. Friesen; Feryal M. P. Behbahani; Arthur Guez; Fabio Viola; Sims Witherspoon; Thomas Anthony 001; Lars Holger Buesing; Petar Velickovic; Theophane Weber (ICLR, 2021)

Introducing Symmetries to Black Box Meta Reinforcement Learning. - Louis Kirsch; Sebastian Flennerhag; Hado van Hasselt; Abram L. Friesen; Junhyuk Oh; Yutian Chen 001 (AAAI, 2022)

Model-Value Inconsistency as a Signal for Epistemic Uncertainty. - Angelos Filos; Eszter Vértes; Zita Marinho; Gregory Farquhar; Diana Borsa; Abram L. Friesen; Feryal M. P. Behbahani; Tom Schaul; André Barreto 001; Simon Osindero (ICML, 2022)

Retrieval-Augmented Reinforcement Learning. - Anirudh Goyal; Abram L. Friesen; Andrea Banino; Theophane Weber; Nan Rosemary Ke; Adrià Puigdomènech Badia; Arthur Guez; Mehdi Mirza; Peter Conway Humphreys; Ksenia Konyushkova; Michal Valko; Simon Osindero; et al. (ICML, 2022)

Retrieval-Augmented Reinforcement Learning. - Anirudh Goyal; Abram L. Friesen; Andrea Banino; Theophane Weber; Nan Rosemary Ke; Adrià Puigdomènech Badia; Arthur Guez; Mehdi Mirza; Ksenia Konyushkova; Michal Valko; Simon Osindero; Timothy P. Lillicrap; et al. (CoRR, 2022)

Multi-step Planning for Automated Hyperparameter Optimization with OptFormer. - Lucio M. Dery; Abram L. Friesen; Nando de Freitas; Marc'Aurelio Ranzato; Yutian Chen 001 (CoRR, 2022)

Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning. - Patrick Emedom-Nnamdi; Abram L. Friesen; Bobak Shahriari; Nando de Freitas; Matthew W. Hoffman (CoRR, 2023)

BOND: Aligning LLMs with Best-of-N Distillation. - Pier Giuseppe Sessa; Robert Dadashi-Tazehozi; Léonard Hussenot; Johan Ferret; Nino Vieillard; Alexandre Ramé; Bobak Shahriari; Sarah Perrin; Abram L. Friesen; Geoffrey Cideron; Sertan Girgin; Piotr Stanczyk; et al. (ICLR, 2025)