Aviral Kumar

According to our database, Aviral Kumar authored at least 90 papers between 2017 and 2025.

Collaborative distances:

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

2025

Compute-Optimal Scaling for Value-Based Deep RL.

Preston Fu, Oleh Rybkin, Zhiyuan Zhou, Michal Nauman, Pieter Abbeel, Sergey Levine, Aviral Kumar

CoRR, August, 2025

Reasoning as an Adaptive Defense for Safety.

Taeyoun Kim, Fahim Tajwar, Aditi Raghunathan, Aviral Kumar

CoRR, July, 2025

e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs.

Amrith Setlur, Matthew Y. R. Yang, Charlie Snell, Jeremy Greer, Ian Wu, Virginia Smith, Max Simchowitz, Aviral Kumar

CoRR, June, 2025

Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction.

Junhong Shen, Hao Bai, Lunjun Zhang, Yifei Zhou, Amrith Setlur, Shengbang Tong, Diego Caples, Nan Jiang, Tong Zhang, Ameet Talwalkar, Aviral Kumar

CoRR, June, 2025

Horizon Reduction Makes RL Scalable.

Seohong Park, Kevin Frans, Deepinder Mann, Benjamin Eysenbach, Aviral Kumar, Sergey Levine

CoRR, June, 2025

Grounded Reinforcement Learning for Visual Reasoning.

Gabriel Sarch, Snigdha Saha, Naitik Khandelwal, Ayush Jain, Michael J. Tarr, Aviral Kumar, Katerina Fragkiadaki

CoRR, May, 2025

Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners.

Michal Nauman, Marek Cygan, Carmelo Sferrazza, Aviral Kumar, Pieter Abbeel

CoRR, May, 2025

Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning.

Yuxiao Qu, Matthew Y. R. Yang, Amrith Setlur, Lewis Tunstall, Edward Emanuel Beeching, Ruslan Salakhutdinov, Aviral Kumar

CoRR, March, 2025

Digi-Q: Learning Q-Value Functions for Training Device-Control Agents.

Hao Bai, Yifei Zhou, Li Erran Li, Sergey Levine, Aviral Kumar

CoRR, February, 2025

Scaling Test-Time Compute Without Verification or RL is Suboptimal.

Amrith Setlur, Nived Rajaraman, Sergey Levine, Aviral Kumar

CoRR, February, 2025

Value-Based Deep RL Scales Predictably.

Oleh Rybkin, Michal Nauman, Preston Fu, Charlie Snell, Pieter Abbeel, Sergey Levine, Aviral Kumar

CoRR, February, 2025

Vision-Language Models Provide Promptable Representations for Reinforcement Learning.

William Chen, Oier Mees, Aviral Kumar, Sergey Levine

Trans. Mach. Learn. Res., 2025

Unfamiliar Finetuning Examples Control How Language Models Hallucinate.

Katie Kang, Eric Wallace, Claire J. Tomlin, Aviral Kumar, Sergey Levine

Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, 2025

Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data.

Zhiyuan Zhou, Andy Peng, Qiyang Li, Sergey Levine, Aviral Kumar

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Generative Verifiers: Reward Modeling as Next-Token Prediction.

Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, Rishabh Agarwal

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Parameters for Reasoning.

Charlie Victor Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning.

Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, Aviral Kumar

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Training Language Models to Self-Correct via Reinforcement Learning.

Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D. Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M. Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal M. P. Behbahani, Aleksandra Faust

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models.

Yinlam Chow, Guy Tennenholtz, Izzeddin Gur, Vincent Zhuang, Bo Dai, Aviral Kumar, Rishabh Agarwal, Sridhar Thiagarajan, Craig Boutilier, Aleksandra Faust

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Digi-Q: Learning VLM Q-Value Functions for Training Device-Control Agents.

Hao Bai, Yifei Zhou, Li Erran Li, Sergey Levine, Aviral Kumar

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

RRM: Robust Reward Model Training Mitigates Reward Hacking.

Tianqi Liu, Wei Xiong, Jie Ren, Lichang Chen, Junru Wu, Rishabh Joshi, Yang Gao, Jiaming Shen, Zhen Qin, Tianhe Yu, Daniel Sohn, Anastasia Makarova, Jeremiah Zhe Liu, Yuan Liu, Bilal Piot, Abe Ittycheriah, Aviral Kumar, Mohammad Saleh

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone.

Max Sobol Mark, Tian Gao, Georgia Gabriela Sampaio, Mohan Kumar Srirama, Archit Sharma, Chelsea Finn, Aviral Kumar

CoRR, 2024

What Do Learning Dynamics Reveal About Generalization in LLM Reasoning?

Katie Kang, Amrith Setlur, Dibya Ghosh, Jacob Steinhardt, Claire J. Tomlin, Sergey Levine, Aviral Kumar

CoRR, 2024

RRM: Robust Reward Model Training Mitigates Reward Hacking.

Tianqi Liu, Wei Xiong, Jie Ren, Lichang Chen, Junru Wu, Rishabh Joshi, Yang Gao, Jiaming Shen, Zhen Qin, Tianhe Yu, Daniel Sohn, Anastasiia Makarova, Jeremiah Z. Liu, Yuan Liu, Bilal Piot, Abe Ittycheriah, Aviral Kumar, Mohammad Saleh

CoRR, 2024

Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Charlie Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar

CoRR, 2024

D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning.

Rafael Rafailov, Kyle Beltran Hatch, Anikait Singh, Aviral Kumar, Laura M. Smith, Ilya Kostrikov, Philippe Hansen-Estruch, Victor Kolev, Philip J. Ball, Jiajun Wu, Sergey Levine, Chelsea Finn

RLJ, 2024

RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.

Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Designing Cell-Type-Specific Promoter Sequences Using Conservative Model-Based Optimization.

Aniketh Janardhan Reddy, Xinyang Geng, Michael Herschl, Sathvik Kolli, Aviral Kumar, Patrick Hsu, Sergey Levine, Nilah Ioannidis

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Recursive Introspection: Teaching Language Model Agents How to Self-Improve.

Yuxiao Qu, Tianjun Zhang, Naman Garg, Aviral Kumar

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Is Value Learning Really the Main Bottleneck in Offline RL?

Seohong Park, Kevin Frans, Sergey Levine, Aviral Kumar

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning.

Hao Bai, Yifei Zhou, Jiayi Pan, Mert Cemri, Alane Suhr, Sergey Levine, Aviral Kumar

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Robotic Offline RL from Internet Videos via Value-Function Learning.

Chethan Bhateja, Derek Guo, Dibya Ghosh, Anikait Singh, Manan Tomar, Quan Vuong, Yevgen Chebotar, Sergey Levine, Aviral Kumar

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL.

Yifei Zhou, Andrea Zanette, Jiayi Pan, Sergey Levine, Aviral Kumar

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data.

Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Stop Regressing: Training Value Functions via Classification for Scalable Deep RL.

Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Zero-Shot Robotic Manipulation with Pre-Trained Image-Editing Diffusion Models.

Kevin Black, Mitsuhiko Nakamoto, Pranav Atreya, Homer Rich Walke, Chelsea Finn, Aviral Kumar, Sergey Levine

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance.

Mitsuhiko Nakamoto, Oier Mees, Aviral Kumar, Sergey Levine

Proceedings of the Conference on Robot Learning, 6-9 November 2024, Munich, Germany., 2024

2023

Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models.

Kevin Black, Mitsuhiko Nakamoto, Pranav Atreya, Homer Walke, Chelsea Finn, Aviral Kumar, Sergey Levine

CoRR, 2023

Latent Conservative Objective Models for Data-Driven Crystal Structure Prediction.

Han Qi, Xinyang Geng, Stefano Rando, Iku Ohama, Aviral Kumar, Sergey Levine

CoRR, 2023

Robotic Offline RL from Internet Videos via Value-Function Pre-Training.

Chethan Bhateja, Derek Guo, Dibya Ghosh, Anikait Singh, Manan Tomar, Quan Vuong, Yevgen Chebotar, Sergey Levine, Aviral Kumar

CoRR, 2023

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning.

Mitsuhiko Nakamoto, Yuexiang Zhai, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, Sergey Levine

CoRR, 2023

Pre-Training for Robots: Offline RL Enables Learning New Tasks in a Handful of Trials.

Aviral Kumar, Anikait Singh, Frederik D. Ebert, Mitsuhiko Nakamoto, Yanlai Yang, Chelsea Finn, Sergey Levine

Proceedings of the Robotics: Science and Systems XIX, Daegu, 2023

ReDS: Offline RL With Heteroskedastic Datasets via Support Constraints.

Anikait Singh, Aviral Kumar, Quan Vuong, Yevgen Chebotar, Sergey Levine

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

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning.

Mitsuhiko Nakamoto, Simon Zhai, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, Sergey Levine

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

Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets.

Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni Pajarinen, Romain Laroche, Abhishek Gupta, Pulkit Agrawal

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

Efficient Deep Reinforcement Learning Requires Regulating Overfitting.

Qiyang Li, Aviral Kumar, Ilya Kostrikov, Sergey Levine

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes.

Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Confidence-Conditioned Value Functions for Offline Reinforcement Learning.

Joey Hong, Aviral Kumar, Sergey Levine

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning.

Jianlan Luo, Perry Dong, Jeffrey Wu, Aviral Kumar, Xinyang Geng, Sergey Levine

Proceedings of the Conference on Robot Learning, 2023

Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions.

Yevgen Chebotar, Quan Vuong, Karol Hausman, Fei Xia, Yao Lu, Alex Irpan, Aviral Kumar, Tianhe Yu, Alexander Herzog, Karl Pertsch, Keerthana Gopalakrishnan, Julian Ibarz, Ofir Nachum, Sumedh Anand Sontakke, Grecia Salazar, Huong T. Tran, Jodilyn Peralta, Clayton Tan, Deeksha Manjunath, Jaspiar Singh, Brianna Zitkovich, Tomas Jackson, Kanishka Rao, Chelsea Finn, Sergey Levine

Proceedings of the Conference on Robot Learning, 2023

2022

Dual Generator Offline Reinforcement Learning.

Quan Vuong, Aviral Kumar, Sergey Levine, Yevgen Chebotar

CoRR, 2022

Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints.

Anikait Singh, Aviral Kumar, Quan Vuong, Yevgen Chebotar, Sergey Levine

CoRR, 2022

Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials.

Aviral Kumar, Anikait Singh, Frederik Ebert, Yanlai Yang, Chelsea Finn, Sergey Levine

CoRR, 2022

When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?

Aviral Kumar, Joey Hong, Anikait Singh, Sergey Levine

CoRR, 2022

Off-Policy Actor-critic for Recommender Systems.

Minmin Chen, Can Xu, Vince Gatto, Devanshu Jain, Aviral Kumar, Ed H. Chi

Proceedings of the RecSys '22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18, 2022

DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning.

Quan Vuong, Aviral Kumar, Sergey Levine, Yevgen Chebotar

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

Data-Driven Offline Decision-Making via Invariant Representation Learning.

Han Qi, Yi Su, Aviral Kumar, Sergey Levine

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

How to Leverage Unlabeled Data in Offline Reinforcement Learning.

Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, Sergey Levine

Proceedings of the International Conference on Machine Learning, 2022

Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization.

Brandon Trabucco, Xinyang Geng, Aviral Kumar, Sergey Levine

Proceedings of the International Conference on Machine Learning, 2022

Data-Driven Offline Optimization for Architecting Hardware Accelerators.

Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine

Proceedings of the Tenth International Conference on Learning Representations, 2022

Should I Run Offline Reinforcement Learning or Behavioral Cloning?

Aviral Kumar, Joey Hong, Anikait Singh, Sergey Levine

Proceedings of the Tenth International Conference on Learning Representations, 2022

DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization.

Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine

Proceedings of the Tenth International Conference on Learning Representations, 2022

Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning.

Homer Walke, Jonathan Yang, Albert Yu, Aviral Kumar, Jedrzej Orbik, Avi Singh, Sergey Levine

Proceedings of the Conference on Robot Learning, 2022

2021

COMBO: Conservative Offline Model-Based Policy Optimization.

Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn

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

Conservative Data Sharing for Multi-Task Offline Reinforcement Learning.

Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn

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

Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability.

Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine

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

Conservative Objective Models for Effective Offline Model-Based Optimization.

Brandon Trabucco, Aviral Kumar, Xinyang Geng, Sergey Levine

Proceedings of the 38th International Conference on Machine Learning, 2021

Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning.

Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine

Proceedings of the 9th International Conference on Learning Representations, 2021

Benchmarks for Deep Off-Policy Evaluation.

Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R. Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Tom Le Paine

Proceedings of the 9th International Conference on Learning Representations, 2021

Conservative Safety Critics for Exploration.

Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg

Proceedings of the 9th International Conference on Learning Representations, 2021

OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning.

Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, Ofir Nachum

Proceedings of the 9th International Conference on Learning Representations, 2021

A Workflow for Offline Model-Free Robotic Reinforcement Learning.

Aviral Kumar, Anikait Singh, Stephen Tian, Chelsea Finn, Sergey Levine

Proceedings of the Conference on Robot Learning, 8-11 November 2021, London, UK., 2021

2020

COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning.

Avi Singh, Albert Yu, Jonathan Yang, Jesse Zhang, Aviral Kumar, Sergey Levine

CoRR, 2020

Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Sergey Levine, Aviral Kumar, George Tucker, Justin Fu

CoRR, 2020

D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, Sergey Levine

CoRR, 2020

Conservative Q-Learning for Offline Reinforcement Learning.

Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Model Inversion Networks for Model-Based Optimization.

Aviral Kumar, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL.

Saurabh Kumar, Aviral Kumar, Sergey Levine, Chelsea Finn

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction.

Aviral Kumar, Abhishek Gupta, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Chaining Behaviors from Data with Model-Free Reinforcement Learning.

Avi Singh, Albert Yu, Jonathan Yang, Jesse Zhang, Aviral Kumar, Sergey Levine

Proceedings of the 4th Conference on Robot Learning, 2020

2019

Reward-Conditioned Policies.

Aviral Kumar, Xue Bin Peng, Sergey Levine

CoRR, 2019

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning.

Xue Bin Peng, Aviral Kumar, Grace Zhang, Sergey Levine

CoRR, 2019

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction.

Aviral Kumar, Justin Fu, George Tucker, Sergey Levine

CoRR, 2019

Calibration of Encoder Decoder Models for Neural Machine Translation.

Aviral Kumar, Sunita Sarawagi

CoRR, 2019

Graph Normalizing Flows.

Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction.

Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Diagnosing Bottlenecks in Deep Q-learning Algorithms.

Justin Fu, Aviral Kumar, Matthew Soh, Sergey Levine

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

2018

Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings.

Aviral Kumar, Sunita Sarawagi, Ujjwal Jain

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Challenges and Tool Implementation of Hybrid Rapidly-Exploring Random Trees.

Stanley Bak, Sergiy Bogomolov, Thomas A. Henzinger, Aviral Kumar

Proceedings of the Numerical Software Verification - 10th International Workshop, 2017

The Reach-Avoid Problem for Constant-Rate Multi-mode Systems.

Shankara Narayanan Krishna, Aviral Kumar, Fabio Somenzi, Behrouz Touri, Ashutosh Trivedi

Proceedings of the Automated Technology for Verification and Analysis, 2017