Effects of Varying the Delay Distribution in Random, Scale-free, and Small-world Networks. - Bum Soon Jang; Timothy A. Mann; Yoonsuck Choe (GrC, 2008)

Prenatal to postnatal transfer of motor skills through motor-compatible sensory representations. - Timothy A. Mann; Yoonsuck Choe (ICDL, 2010)

Scaling Up Reinforcement Learning through Targeted Exploration. - Timothy A. Mann; Yoonsuck Choe (AAAI, 2011)

Directed Exploration in Reinforcement Learning with Transferred Knowledge. - Timothy A. Mann; Yoonsuck Choe (EWRL, 2012)

Autonomous and Interactive Improvement of Binocular Visual Depth Estimation through Sensorimotor Interaction. - Timothy A. Mann; Yunjung Park; Sungmoon Jeong; Minho Lee 001; Yoonsuck Choe (IEEE Trans. Auton. Ment. Dev., 2013)

Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations. - Timothy A. Mann; Shie Mannor (ICML, 2014)

Time-Regularized Interrupting Options (TRIO). - Timothy A. Mann; Daniel J. Mankowitz; Shie Mannor (ICML, 2014)

How hard is my MDP?" The distribution-norm to the rescue". - Odalric-Ambrym Maillard; Timothy A. Mann; Shie Mannor (NIPS, 2014)

Learning When to Switch between Skills in a High Dimensional Domain. - Timothy A. Mann; Daniel J. Mankowitz; Shie Mannor (AAAI Workshop: Learning for General Competency in Video Games, 2015)

Off-policy Model-based Learning under Unknown Factored Dynamics. - Assaf Hallak; François Schnitzler; Timothy A. Mann; Shie Mannor (ICML, 2015)

Off-policy evaluation for MDPs with unknown structure. - Assaf Hallak; François Schnitzler; Timothy A. Mann; Shie Mannor (CoRR, 2015)

Actively Learning to Attract Followers on Twitter. - Nir Levine; Timothy A. Mann; Shie Mannor (CoRR, 2015)

Bootstrapping Skills. - Daniel J. Mankowitz; Timothy A. Mann; Shie Mannor (CoRR, 2015)

Approximate Value Iteration with Temporally Extended Actions. - Timothy A. Mann; Shie Mannor; Doina Precup (J. Artif. Intell. Res., 2015)

Adaptive Skills Adaptive Partitions (ASAP). - Daniel J. Mankowitz; Timothy A. Mann; Shie Mannor (NIPS, 2016)

Iterative Hierarchical Optimization for Misspecified Problems (IHOMP). - Daniel J. Mankowitz; Timothy A. Mann; Shie Mannor (CoRR, 2016)

Adaptive Lambda Least-Squares Temporal Difference Learning. - Timothy A. Mann; Hugo Penedones; Shie Mannor; Todd Hester (CoRR, 2016)

Approximate Value Iteration with Temporally Extended Actions (Extended Abstract). - Timothy A. Mann; Shie Mannor; Doina Precup (IJCAI, 2017)

Learning Robust Options. - Daniel J. Mankowitz; Timothy A. Mann; Pierre-Luc Bacon; Doina Precup; Shie Mannor (AAAI, 2018)

Soft-Robust Actor-Critic Policy-Gradient. - Esther Derman; Daniel J. Mankowitz; Timothy A. Mann; Shie Mannor (UAI, 2018)

A Dual Approach to Scalable Verification of Deep Networks. - Krishnamurthy Dvijotham; Robert Stanforth; Sven Gowal; Timothy A. Mann; Pushmeet Kohli (UAI, 2018)

Optimizing Slate Recommendations via Slate-CVAE. - Ray Jiang; Sven Gowal; Timothy A. Mann; Danilo J. Rezende (CoRR, 2018)

Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem. - Hugo Penedones; Damien Vincent; Hartmut Maennel; Sylvain Gelly; Timothy A. Mann; André Barreto 001 (CoRR, 2018)

Learning from Delayed Outcomes with Intermediate Observations. - Timothy A. Mann; Sven Gowal; Ray Jiang; Huiyi Hu; Balaji Lakshminarayanan; András György 001 (CoRR, 2018)

On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models. - Sven Gowal; Krishnamurthy Dvijotham; Robert Stanforth; Rudy Bunel; Chongli Qin; Jonathan Uesato; Relja Arandjelovic; Timothy A. Mann; Pushmeet Kohli (CoRR, 2018)

Scalable Verified Training for Provably Robust Image Classification. - Sven Gowal; Krishnamurthy Dvijotham; Robert Stanforth; Rudy Bunel; Chongli Qin; Jonathan Uesato; Relja Arandjelovic; Timothy Arthur Mann; Pushmeet Kohli (ICCV, 2019)

Beyond Greedy Ranking: Slate Optimization via List-CVAE. - Ray Jiang; Sven Gowal; Yuqiu Qian; Timothy A. Mann; Danilo J. Rezende (ICLR (Poster), 2019)

Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems. - Timothy A. Mann; Sven Gowal; András György 001; Huiyi Hu; Ray Jiang; Balaji Lakshminarayanan; Prav Srinivasan (ICML, 2019)

A Dual Approach to Verify and Train Deep Networks. - Sven Gowal; Krishnamurthy Dvijotham; Robert Stanforth; Timothy A. Mann; Pushmeet Kohli (IJCAI, 2019)

Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. - Carlos Riquelme; Hugo Penedones; Damien Vincent; Hartmut Maennel; Sylvain Gelly; Timothy A. Mann; André Barreto 001; Gergely Neu (NeurIPS, 2019)

A Bayesian Approach to Robust Reinforcement Learning. - Esther Derman; Daniel J. Mankowitz; Timothy A. Mann; Shie Mannor (UAI, 2019)

Robust Reinforcement Learning for Continuous Control with Model Misspecification. - Daniel J. Mankowitz; Nir Levine; Rae Jeong; Abbas Abdolmaleki; Jost Tobias Springenberg; Timothy A. Mann; Todd Hester; Martin A. Riedmiller (CoRR, 2019)

An Alternative Surrogate Loss for PGD-based Adversarial Testing. - Sven Gowal; Jonathan Uesato; Chongli Qin; Po-Sen Huang; Timothy A. Mann; Pushmeet Kohli (CoRR, 2019)

Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations. - Sven Gowal; Chongli Qin; Po-Sen Huang; A. Taylan Cemgil; Krishnamurthy Dvijotham; Timothy A. Mann; Pushmeet Kohli (CVPR, 2020)

Robust Reinforcement Learning for Continuous Control with Model Misspecification. - Daniel J. Mankowitz; Nir Levine; Rae Jeong; Abbas Abdolmaleki; Jost Tobias Springenberg; Yuanyuan Shi; Jackie Kay; Todd Hester; Timothy A. Mann; Martin A. Riedmiller (ICLR, 2020)

Non-Stationary Delayed Bandits with Intermediate Observations. - Claire Vernade; András György 001; Timothy A. Mann (ICML, 2020)

The NodeHopper: Enabling Low Latency Ranking with Constraints via a Fast Dual Solver. - Anton Zhernov; Krishnamurthy (Dj) Dvijotham; Ivan Lobov; Dan A. Calian; Michelle X. Gong; Natarajan Chandrashekar; Timothy A. Mann (KDD, 2020)

Non-Stationary Bandits with Intermediate Observations. - Claire Vernade; András György 001; Timothy A. Mann (CoRR, 2020)

Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples. - Sven Gowal; Chongli Qin; Jonathan Uesato; Timothy A. Mann; Pushmeet Kohli (CoRR, 2020)

Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification. - Daniel J. Mankowitz; Dan A. Calian; Rae Jeong; Cosmin Paduraru; Nicolas Heess; Sumanth Dathathri; Martin A. Riedmiller; Timothy A. Mann (CoRR, 2020)

Balancing Constraints and Rewards with Meta-Gradient D4PG. - Dan A. Calian; Daniel J. Mankowitz; Tom Zahavy; Zhongwen Xu; Junhyuk Oh; Nir Levine; Timothy A. Mann (ICLR, 2021)

Self-supervised Adversarial Robustness for the Low-label, High-data Regime. - Sven Gowal; Po-Sen Huang; Aäron van den Oord; Timothy A. Mann; Pushmeet Kohli (ICLR, 2021)

Improving Robustness using Generated Data. - Sven Gowal; Sylvestre-Alvise Rebuffi; Olivia Wiles; Florian Stimberg; Dan Andrei Calian; Timothy A. Mann (NeurIPS, 2021)

Data Augmentation Can Improve Robustness. - Sylvestre-Alvise Rebuffi; Sven Gowal; Dan Andrei Calian; Florian Stimberg; Olivia Wiles; Timothy A. Mann (NeurIPS, 2021)

Fixing Data Augmentation to Improve Adversarial Robustness. - Sylvestre-Alvise Rebuffi; Sven Gowal; Dan A. Calian; Florian Stimberg; Olivia Wiles; Timothy A. Mann (CoRR, 2021)

Defending Against Image Corruptions Through Adversarial Augmentations. - Dan Andrei Calian; Florian Stimberg; Olivia Wiles; Sylvestre-Alvise Rebuffi; András György 001; Timothy A. Mann; Sven Gowal (ICLR, 2022)

MuZero with Self-competition for Rate Control in VP9 Video Compression. - Amol Mandhane; Anton Zhernov; Maribeth Rauh; Chenjie Gu; Miaosen Wang; Flora Xue; Wendy Shang; Derek Pang; Rene Claus; Ching-Han Chiang; Cheng Chen; Jingning Han; et al. (CoRR, 2022)