Aviv Tamar

According to our database, Aviv Tamar authored at least 84 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning.

Zohar Rimon, Tom Jurgenson, Orr Krupnik, Gilad Adler, Aviv Tamar

CoRR, 2024

2023

Deep Bribe: Predicting the Rise of Bribery in Blockchain Mining with Deep RL.

Roi Bar Zur, Danielle Dori, Sharon Vardi, Ittay Eyal, Aviv Tamar

IACR Cryptol. ePrint Arch., 2023

DDLP: Unsupervised Object-Centric Video Prediction with Deep Dynamic Latent Particles.

Tal Daniel, Aviv Tamar

CoRR, 2023

Goal-Conditioned Supervised Learning with Sub-Goal Prediction.

Tom Jurgenson, Aviv Tamar

CoRR, 2023

A Deep Learning Perspective on Network Routing.

Yarin Perry, Felipe Vieira Frujeri, Chaim Hoch, Srikanth Kandula, Ishai Menache, Michael Schapira, Aviv Tamar

CoRR, 2023

Towards Deployable RL - What's Broken with RL Research and a Potential Fix.

Shie Mannor, Aviv Tamar

CoRR, 2023

DOTE: Rethinking (Predictive) WAN Traffic Engineering.

Yarin Perry, Felipe Vieira Frujeri, Chaim Hoch, Srikanth Kandula, Ishai Menache, Michael Schapira, Aviv Tamar

Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, 2023

Explore to Generalize in Zero-Shot RL.

Ev Zisselman, Itai Lavie, Daniel Soudry, Aviv Tamar

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Online Tool Selection with Learned Grasp Prediction Models.

Khashayar Rohanimanesh, Jake Metzger, William Richards, Aviv Tamar

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

TGRL: An Algorithm for Teacher Guided Reinforcement Learning.

Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal

Proceedings of the International Conference on Machine Learning, 2023

Learning Control by Iterative Inversion.

Gal Leibovich, Guy Jacob, Or Avner, Gal Novik, Aviv Tamar

Proceedings of the International Conference on Machine Learning, 2023

ContraBAR: Contrastive Bayes-Adaptive Deep RL.

Era Choshen, Aviv Tamar

Proceedings of the International Conference on Machine Learning, 2023

Hierarchical Planning for Rope Manipulation using Knot Theory and a Learned Inverse Model.

Matan Sudry, Tom Jurgenson, Aviv Tamar, Erez Karpas

Proceedings of the Conference on Robot Learning, 2023

Fine-Tuning Generative Models as an Inference Method for Robotic Tasks.

Orr Krupnik, Elisei Shafer, Tom Jurgenson, Aviv Tamar

Proceedings of the Conference on Robot Learning, 2023

2022

WeRLman: To Tackle Whale (Transactions), Go Deep (RL).

Roi Bar Zur, Ameer Abu-Hanna, Ittay Eyal, Aviv Tamar

IACR Cryptol. ePrint Arch., 2022

Meta Reinforcement Learning with Finite Training Tasks - a Density Estimation Approach.

Zohar Rimon, Aviv Tamar, Gilad Adler

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Validate on Sim, Detect on Real - Model Selection for Domain Randomization.

Gal Leibovich, Guy Jacob, Shadi Endrawis, Gal Novik, Aviv Tamar

Proceedings of the 2022 International Conference on Robotics and Automation, 2022

Unsupervised Image Representation Learning with Deep Latent Particles.

Tal Daniel, Aviv Tamar

Proceedings of the International Conference on Machine Learning, 2022

Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability.

Aviv Tamar, Daniel Soudry, Ev Zisselman

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Offline Meta Reinforcement Learning - Identifiability Challenges and Effective Data Collection Strategies.

Ron Dorfman, Idan Shenfeld, Aviv Tamar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Unsupervised Feature Learning for Manipulation with Contrastive Domain Randomization.

Carmel Rabinovitz, Niko A. Grupen, Aviv Tamar

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

Efficient Self-Supervised Data Collection for Offline Robot Learning.

Shadi Endrawis, Gal Leibovich, Guy Jacob, Gal Novik, Aviv Tamar

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder.

Tal Daniel, Aviv Tamar

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

Online Safety Assurance for Deep Reinforcement Learning.

Noga H. Rotman, Michael Schapira, Aviv Tamar

CoRR, 2020

Offline Meta Reinforcement Learning.

Ron Dorfman, Aviv Tamar

CoRR, 2020

Deep Residual Flow for Novelty Detection.

Ev Zisselman, Aviv Tamar

CoRR, 2020

Constrained Policy Improvement for Efficient Reinforcement Learning.

Elad Sarafian, Aviv Tamar, Sarit Kraus

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Hallucinative Topological Memory for Zero-Shot Visual Planning.

Kara Liu, Thanard Kurutach, Christine Tung, Pieter Abbeel, Aviv Tamar

Proceedings of the 37th International Conference on Machine Learning, 2020

Sub-Goal Trees a Framework for Goal-Based Reinforcement Learning.

Tom Jurgenson, Or Avner, Edward Groshev, Aviv Tamar

Proceedings of the 37th International Conference on Machine Learning, 2020

Online Safety Assurance for Learning-Augmented Systems.

Noga H. Rotman, Michael Schapira, Aviv Tamar

Proceedings of the HotNets '20: The 19th ACM Workshop on Hot Topics in Networks, 2020

Deep Residual Flow for Out of Distribution Detection.

Ev Zisselman, Aviv Tamar

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Efficient MDP Analysis for Selfish-Mining in Blockchains.

Roi Bar Zur, Ittay Eyal, Aviv Tamar

Proceedings of the AFT '20: 2nd ACM Conference on Advances in Financial Technologies, 2020

2019

Deep Variational Semi-Supervised Novelty Detection.

Tal Daniel, Thanard Kurutach, Aviv Tamar

CoRR, 2019

Sub-Goal Trees - a Framework for Goal-Directed Trajectory Prediction and Optimization.

Tom Jurgenson, Edward Groshev, Aviv Tamar

CoRR, 2019

Learning Robotic Manipulation through Visual Planning and Acting.

Angelina Wang, Thanard Kurutach, Kara Liu, Pieter Abbeel, Aviv Tamar

CoRR, 2019

Learning Robotic Manipulation through Visual Planning and Acting.

Angelina Wang, Thanard Kurutach, Pieter Abbeel, Aviv Tamar

Proceedings of the Robotics: Science and Systems XV, 2019

Harnessing Reinforcement Learning for Neural Motion Planning.

Tom Jurgenson, Aviv Tamar

Proceedings of the Robotics: Science and Systems XV, 2019

Domain Randomization for Active Pose Estimation.

Xinyi Ren, Jianlan Luo, Eugen Solowjow, Juan Aparicio Ojea, Abhishek Gupta, Aviv Tamar, Pieter Abbeel

Proceedings of the International Conference on Robotics and Automation, 2019

Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly.

Jianlan Luo, Eugen Solowjow, Chengtao Wen, Juan Aparicio Ojea, Alice M. Agogino, Aviv Tamar, Pieter Abbeel

Proceedings of the International Conference on Robotics and Automation, 2019

A Deep Reinforcement Learning Perspective on Internet Congestion Control.

Nathan Jay, Noga H. Rotman, Brighten Godfrey, Michael Schapira, Aviv Tamar

Proceedings of the 36th International Conference on Machine Learning, 2019

Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN.

Dror Freirich, Tzahi Shimkin, Ron Meir, Aviv Tamar

Proceedings of the 36th International Conference on Machine Learning, 2019

Bayesian Relational Memory for Semantic Visual Navigation.

Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Multi-Agent Reinforcement Learning with Multi-Step Generative Models.

Orr Krupnik, Igor Mordatch, Aviv Tamar

Proceedings of the 3rd Annual Conference on Robot Learning, 2019

Robust 2D Assembly Sequencing via Geometric Planning with Learned Scores.

Tzvika Geft, Aviv Tamar, Ken Goldberg, Dan Halperin

Proceedings of the 15th IEEE International Conference on Automation Science and Engineering, 2019

A Risk-Sensitive Finite-Time Reachability Approach for Safety of Stochastic Dynamic Systems.

Margaret P. Chapman, Jonathan Lacotte, Aviv Tamar, Donggun Lee, Kevin M. Smith, Victoria Cheng, Jaime F. Fisac, Susmit Jha, Marco Pavone, Claire J. Tomlin

Proceedings of the 2019 American Control Conference, 2019

2018

Internet Congestion Control via Deep Reinforcement Learning.

Nathan Jay, Noga H. Rotman, Philip Brighten Godfrey, Michael Schapira, Aviv Tamar

CoRR, 2018

Learning and Planning with a Semantic Model.

Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian

CoRR, 2018

Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN.

Dror Freirich, Ron Meir, Aviv Tamar

CoRR, 2018

Safe Policy Learning from Observations.

Elad Sarafian, Aviv Tamar, Sarit Kraus

CoRR, 2018

Learning Plannable Representations with Causal InfoGAN.

Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart J. Russell, Pieter Abbeel

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning Robotic Assembly from CAD.

Garrett Thomas, Melissa Chien, Aviv Tamar, Juan Aparicio Ojea, Pieter Abbeel

Proceedings of the 2018 IEEE International Conference on Robotics and Automation, 2018

Imitation Learning from Visual Data with Multiple Intentions.

Aviv Tamar, Khashayar Rohanimanesh, Yinlam Chow, Chris Vigorito, Ben Goodrich, Michael Kahane, Derik Pridmore

Proceedings of the 6th International Conference on Learning Representations, 2018

Model-Ensemble Trust-Region Policy Optimization.

Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel

Proceedings of the 6th International Conference on Learning Representations, 2018

Learning Generalized Reactive Policies using Deep Neural Networks.

Edward Groshev, Aviv Tamar, Maxwell Goldstein, Siddharth Srivastava, Pieter Abbeel

Proceedings of the 2018 AAAI Spring Symposia, 2018

2017

Sequential Decision Making With Coherent Risk.

Aviv Tamar, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor

IEEE Trans. Autom. Control., 2017

Safer Classification by Synthesis.

William Wang, Angelina Wang, Aviv Tamar, Xi Chen, Pieter Abbeel

CoRR, 2017

Situationally Aware Options.

Daniel J. Mankowitz, Aviv Tamar, Shie Mannor

CoRR, 2017

Learning Generalized Reactive Policies using Deep Neural Networks.

Edward Groshev, Aviv Tamar, Siddharth Srivastava, Pieter Abbeel

CoRR, 2017

A Machine Learning Approach to Routing.

Asaf Valadarsky, Michael Schapira, Dafna Shahaf, Aviv Tamar

CoRR, 2017

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments.

Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, Igor Mordatch

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Shallow Updates for Deep Reinforcement Learning.

Nir Levine, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, Shie Mannor

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Learning from the hindsight plan - Episodic MPC improvement.

Aviv Tamar, Garrett Thomas, Tianhao Zhang, Sergey Levine, Pieter Abbeel

Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

Constrained Policy Optimization.

Joshua Achiam, David Held, Aviv Tamar, Pieter Abbeel

Proceedings of the 34th International Conference on Machine Learning, 2017

Learning to Route.

Asaf Valadarsky, Michael Schapira, Dafna Shahaf, Aviv Tamar

Proceedings of the 16th ACM Workshop on Hot Topics in Networks, Palo Alto, CA, USA, 2017

2016

Learning the Variance of the Reward-To-Go.

Aviv Tamar, Dotan Di Castro, Shie Mannor

J. Mach. Learn. Res., 2016

Value Iteration Networks.

Aviv Tamar, Sergey Levine, Pieter Abbeel

CoRR, 2016

Situational Awareness by Risk-Conscious Skills.

Daniel J. Mankowitz, Aviv Tamar, Shie Mannor

CoRR, 2016

Value Iteration Networks.

Aviv Tamar, Sergey Levine, Pieter Abbeel, Yi Wu, Garrett Thomas

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Generalized Emphatic Temporal Difference Learning: Bias-Variance Analysis.

Assaf Hallak, Aviv Tamar, Rémi Munos, Shie Mannor

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Bayesian Reinforcement Learning: A Survey.

Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, Aviv Tamar

Found. Trends Mach. Learn., 2015

Emphatic TD Bellman Operator is a Contraction.

Assaf Hallak, Aviv Tamar, Shie Mannor

CoRR, 2015

Policy Gradient for Coherent Risk Measures.

Aviv Tamar, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach.

Yinlam Chow, Aviv Tamar, Shie Mannor, Marco Pavone

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Optimizing the CVaR via Sampling.

Aviv Tamar, Yonatan Glassner, Shie Mannor

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Implicit Temporal Differences.

Aviv Tamar, Panos Toulis, Shie Mannor, Edoardo M. Airoldi

CoRR, 2014

Policy Gradients Beyond Expectations: Conditional Value-at-Risk.

Aviv Tamar, Yonatan Glassner, Shie Mannor

CoRR, 2014

Scaling Up Robust MDPs using Function Approximation.

Aviv Tamar, Shie Mannor, Huan Xu

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Policy Evaluation with Variance Related Risk Criteria in Markov Decision Processes

Aviv Tamar, Dotan Di Castro, Shie Mannor

CoRR, 2013

Scaling Up Robust MDPs by Reinforcement Learning.

Aviv Tamar, Huan Xu, Shie Mannor

CoRR, 2013

Variance Adjusted Actor Critic Algorithms.

Aviv Tamar, Shie Mannor

CoRR, 2013

Temporal Difference Methods for the Variance of the Reward To Go.

Aviv Tamar, Dotan Di Castro, Shie Mannor

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Integrating a Partial Model into Model Free Reinforcement Learning.

Aviv Tamar, Dotan Di Castro, Ron Meir

J. Mach. Learn. Res., 2012

Policy Gradients with Variance Related Risk Criteria.

Dotan Di Castro, Aviv Tamar, Shie Mannor

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Integrating Partial Model Knowledge in Model Free RL Algorithms.

Aviv Tamar, Dotan Di Castro, Ron Meir

Proceedings of the 28th International Conference on Machine Learning, 2011