Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation. - Mathew Monfort; Anqi Liu; Brian D. Ziebart (AAAI, 2015)

Graph-Based Inverse Optimal Control for Robot Manipulation. - Arunkumar Byravan; Mathew Monfort; Brian D. Ziebart; Byron Boots; Dieter Fox (IJCAI, 2015)

Softstar: Heuristic-Guided Probabilistic Inference. - Mathew Monfort; Brenden M. Lake; Brian D. Ziebart; Patrick Lucey; Joshua B. Tenenbaum (NIPS, 2015)

Robust Covariate Shift Regression. - Xiangli Chen; Mathew Monfort; Anqi Liu; Brian D. Ziebart (AISTATS, 2016)

Adversarial Inverse Optimal Control for General Imitation Learning Losses and Embodiment Transfer. - Xiangli Chen; Mathew Monfort; Brian D. Ziebart; Peter Carr (UAI, 2016)

End to End Learning for Self-Driving Cars. - Mariusz Bojarski; Davide Del Testa; Daniel Dworakowski; Bernhard Firner; Beat Flepp; Prasoon Goyal; Lawrence D. Jackel; Mathew Monfort; Urs Muller; Jiakai Zhang; Xin Zhang; Jake Zhao; et al. (CoRR, 2016)

Asynchronous Data Aggregation for Training End to End Visual Control Networks. - Mathew Monfort; Matthew Johnson 003; Aude Oliva; Katja Hofmann (AAMAS, 2017)

Goal-predictive robotic teleoperation from noisy sensors. - Christopher Schultz; Sanket Gaurav; Mathew Monfort; Lingfei Zhang; Brian D. Ziebart (ICRA, 2017)

Identifying Interpretable Action Concepts in Deep Networks. - Kandan Ramakrishnan; Mathew Monfort; Barry A. McNamara; Alex Lascelles; Dan Gutfreund; Rogério Schmidt Feris; Aude Oliva (CVPR Workshops, 2019)

Multi-Agent Tensor Fusion for Contextual Trajectory Prediction. - Tianyang Zhao; Yifei Xu; Mathew Monfort; Wongun Choi; Chris L. Baker; Yibiao Zhao; Yizhou Wang 001; Ying Nian Wu (CVPR, 2019)

Reasoning About Human-Object Interactions Through Dual Attention Networks. - Tete Xiao; Quanfu Fan; Danny Gutfreund; Mathew Monfort; Aude Oliva; Bolei Zhou (ICCV, 2019)

We Have So Much in Common: Modeling Semantic Relational Set Abstractions in Videos. - Alex Andonian; Camilo Fosco; Mathew Monfort; Allen Lee; Rogério Feris; Carl Vondrick; Aude Oliva (ECCV (18), 2020)

Moments in Time Dataset: One Million Videos for Event Understanding. - Mathew Monfort; Carl Vondrick; Aude Oliva; Alex Andonian; Bolei Zhou; Kandan Ramakrishnan; Sarah Adel Bargal; Tom Yan; Lisa M. Brown; Quanfu Fan; Dan Gutfreund (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Spoken Moments: Learning Joint Audio-Visual Representations From Video Descriptions. - Mathew Monfort; SouYoung Jin; Alexander H. Liu; David Harwath; Rogério Feris; James R. Glass; Aude Oliva (CVPR, 2021)

Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding. - Mathew Monfort; Bowen Pan; Kandan Ramakrishnan; Alex Andonian; Barry A. McNamara; Alex Lascelles; Quanfu Fan; Dan Gutfreund; Rogério Schmidt Feris; Aude Oliva (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

A Framework for Efficient Model Evaluation Through Stratification, Sampling, and Estimation. - Riccardo Fogliato; Pratik Patil; Mathew Monfort; Pietro Perona (ECCV (88), 2024)

Precise Model Benchmarking with Only a Few Observations. - Riccardo Fogliato; Pratik Patil; Nil-Jana Akpinar; Mathew Monfort (EMNLP, 2024)

Model ChangeLists: Characterizing Updates to ML Models. - Sabri Eyuboglu; Karan Goel; Arjun Desai; Lingjiao Chen; Mathew Monfort; Christopher Ré; James Zou 001 (FAccT, 2024)