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Nikos Aréchiga
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2020 – today
- 2024
- [j5]Ruya Karagulle
, Nikos Aréchiga
A Safe Preference Learning Approach for Personalization With Applications to Autonomous Vehicles. IEEE Robotics Autom. Lett. 9(5): 4226-4233 (2024) - [c29]Ruya Karagulle
Incorporating Logic in Online Preference Learning for Safe Personalization of Autonomous Vehicles. HSCC 2024: 5:1-5:11 - [c28]Mohamad Louai Shehab, Antoine Aspeel, Nikos Aréchiga, Andrew Best, Necmiye Ozay:
Learning true objectives: Linear algebraic characterizations of identifiability in inverse reinforcement learning. L4DC 2024: 1266-1277 - [i20]Keiichi Namikoshi, Alex Filipowicz, David A. Shamma, Rumen Iliev, Candice L. Hogan, Nikos Aréchiga:
Using LLMs to Model the Beliefs and Preferences of Targeted Populations. CoRR abs/2403.20252 (2024) - [i19]Rui Zhou, Chenyang Yuan, Frank Permenter, Yanxia Zhang, Nikos Aréchiga, Matt Klenk, Faez Ahmed:
Bridging Design Gaps: A Parametric Data Completion Approach With Graph Guided Diffusion Models. CoRR abs/2406.11934 (2024) - [i18]Jingchao Fang, Nikos Aréchiga, Keiichi Namaoshi, Nayeli Bravo, Candice Hogan, David A. Shamma:
On LLM Wizards: Identifying Large Language Models' Behaviors for Wizard of Oz Experiments. CoRR abs/2407.08067 (2024) - [i17]Keiichi Namikoshi, David A. Shamma, Rumen Iliev, Jingchao Fang, Alexandre L. S. Filipowicz, Candice L. Hogan, Charlene C. Wu, Nikos Aréchiga:
Leveraging Language Models and Bandit Algorithms to Drive Adoption of Battery-Electric Vehicles. CoRR abs/2410.23371 (2024) - 2023
- [j4]Karen Leung
Backpropagation through signal temporal logic specifications: Infusing logical structure into gradient-based methods. Int. J. Robotics Res. 42(6): 356-370 (2023) - [c27]Anna Kawakami
Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge. CI 2023: 63-78 - [c26]Rumen Iliev, Alexandre L. S. Filipowicz, Emily Sarah Sumner, Francine Chen, Nikos Aréchiga, Scott A. Carter, Totte Harinen, Kate A. Sieck, Charlene C. Wu:
Can Behavioral Experts Predict Outcome Heterogeneity? CogSci 2023 - [c25]Ruya Karagulle
Poster Abstract: Safety Guaranteed Preference Learning Approach for Autonomous Vehicles. HSCC 2023: 24:1-24:2 - [c24]Xin Qin, Nikos Aréchiga, Jyotirmoy Deshmukh, Andrew Best:
Robust Testing for Cyber-Physical Systems using Reinforcement Learning. MEMOCODE 2023: 36-46 - [i16]Binyang Song, Chenyang Yuan, Frank Permenter, Nikos Aréchiga, Faez Ahmed:
Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings. CoRR abs/2306.06110 (2023) - [i15]Nikos Aréchiga, Frank Permenter, Binyang Song, Chenyang Yuan:
Drag-guided diffusion models for vehicle image generation. CoRR abs/2306.09935 (2023) - [i14]Anna Kawakami, Luke Guerdan, Yanghuidi Cheng, Matthew L. Lee, Scott A. Carter, Nikos Aréchiga, Kate Glazko, Haiyi Zhu, Kenneth Holstein:
Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge. CoRR abs/2308.15700 (2023) - [i13]Ruya Karagulle, Nikos Aréchiga, Andrew Best, Jonathan A. DeCastro, Necmiye Ozay:
A Preference Learning Approach to Develop Safe and Personalizable Autonomous Vehicles. CoRR abs/2311.02099 (2023) - 2022
- [c23]Robert Dyro, Edward Schmerling, Nikos Aréchiga, Marco Pavone:
Second-Order Sensitivity Analysis for Bilevel Optimization. AISTATS 2022: 9166-9181 - [c22]Ruya Karagulle, Nikos Aréchiga, Jonathan A. DeCastro, Necmiye Ozay
Classification of Driving Behaviors Using STL Formulas: A Comparative Study. FORMATS 2022: 153-162 - [c21]Dan Li, Mohamad Louai Shehab, Zexiang Liu, Nikos Aréchiga, Jonathan A. DeCastro, Necmiye Ozay:
Outlier-robust Inverse Reinforcement Learning and Reward-based Detection of Anomalous Driving Behaviors. ITSC 2022: 4175-4182 - [c20]Daniel Kang
Finding Label and Model Errors in Perception Data With Learned Observation Assertions. SIGMOD Conference 2022: 496-505 - [i12]Daniel Kang, Nikos Aréchiga, Sudeep Pillai, Peter Bailis, Matei Zaharia:
Finding Label and Model Errors in Perception Data With Learned Observation Assertions. CoRR abs/2201.05797 (2022) - [i11]Nikos Aréchiga, Francine Chen, Rumen Iliev, Emily Sumner, Scott A. Carter, Alexandre L. S. Filipowicz, Nayeli Suseth Bravo, Monica P. Van, Kate Glazko, Kalani Murakami, Laurent Denoue, Candice Hogan, Katharine Sieck, Charlene C. Wu, Kent Lyons:
Understanding and Shifting Preferences for Battery Electric Vehicles. CoRR abs/2202.08963 (2022) - [i10]Robert Dyro, Edward Schmerling, Nikos Aréchiga, Marco Pavone:
Second-Order Sensitivity Analysis for Bilevel Optimization. CoRR abs/2205.02329 (2022) - [i9]Anna Kawakami, Luke Guerdan, Yang Cheng, Anita Sun, Alison Hu, Kate Glazko, Nikos Aréchiga, Matthew L. Lee, Scott A. Carter, Haiyi Zhu, Kenneth Holstein:
Towards a Learner-Centered Explainable AI: Lessons from the learning sciences. CoRR abs/2212.05588 (2022) - 2021
- [j3]Jan-David Quesel, Stefan Mitsch, Sarah M. Loos, Nikos Aréchiga, André Platzer:
Correction to: How to model and prove hybrid systems with KeYmaera: a tutorial on safety. Int. J. Softw. Tools Technol. Transf. 23(5): 827 (2021) - [c19]Kaidi Cao, Yining Chen, Junwei Lu, Nikos Aréchiga, Adrien Gaidon, Tengyu Ma:
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization. ICLR 2021 - [c18]Karen Leung, Nikos Aréchiga, Marco Pavone:
Back-Propagation Through Signal Temporal Logic Specifications: Infusing Logical Structure into Gradient-Based Methods. WAFR 2021: 432-449 - [i8]Daniel Jackson, Valerie Richmond, Mike Wang, Jeff Chow, Uriel Guajardo, Soonho Kong, Sergio Campos, Geoffrey Litt, Nikos Aréchiga:
Certified Control: An Architecture for Verifiable Safety of Autonomous Vehicles. CoRR abs/2104.06178 (2021) - [i7]Nikos Aréchiga, Francine Chen, Yan-Ying Chen, Yanxia Zhang, Rumen Iliev, Heishiro Toyoda, Kent Lyons:
Accelerating Understanding of Scientific Experiments with End to End Symbolic Regression. CoRR abs/2112.04023 (2021) - 2020
- [c17]Jonathan A. DeCastro, Karen Leung, Nikos Aréchiga, Marco Pavone:
Interpretable Policies from Formally-Specified Temporal Properties. ITSC 2020: 1-7 - [i6]Kaidi Cao, Yining Chen, Junwei Lu, Nikos Aréchiga, Adrien Gaidon, Tengyu Ma:
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization. CoRR abs/2006.15766 (2020) - [i5]Karen Leung, Nikos Aréchiga, Marco Pavone:
Back-propagation through Signal Temporal Logic Specifications: Infusing Logical Structure into Gradient-Based Methods. CoRR abs/2008.00097 (2020)
2010 – 2019
- 2019
- [c16]Sicun Gao, James Kapinski, Jyotirmoy V. Deshmukh, Nima Roohi, Armando Solar-Lezama, Nikos Aréchiga, Soonho Kong:
Numerically-Robust Inductive Proof Rules for Continuous Dynamical Systems. CAV (2) 2019: 137-154 - [c15]Nikos Aréchiga:
Specifying Safety of Autonomous Vehicles in Signal Temporal Logic. IV 2019: 58-63 - [c14]Karen Leung, Nikos Aréchiga, Marco Pavone:
Backpropagation for Parametric STL. IV 2019: 185-192 - [c13]Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Aréchiga, Tengyu Ma:
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. NeurIPS 2019: 1565-1576 - [i4]Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Aréchiga, Tengyu Ma:
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. CoRR abs/1906.07413 (2019) - [i3]Nikos Aréchiga, Jonathan A. DeCastro, Soonho Kong, Karen Leung:
Better AI through Logical Scaffolding. CoRR abs/1909.06965 (2019) - [i2]Xin Qin, Nikos Aréchiga, Andrew Best, Jyotirmoy V. Deshmukh:
Automatic Testing and Falsification with Dynamically Constrained Reinforcement Learning. CoRR abs/1910.13645 (2019) - 2017
- [c12]Nikos Aréchiga, Sumanth Dathathri, Shashank Vernekar, Nagesh Kathare, Sicun Gao, Shinichi Shiraishi:
Osiris: A Tool for Abstraction and Verification of Control Software with Lookup Tables. SCAV@CPSWeek 2017: 11-18 - [c11]Sumanth Dathathri, Nikos Aréchiga, Sicun Gao, Richard M. Murray:
Learning-Based Abstractions for Nonlinear Constraint Solving. IJCAI 2017: 592-599 - [c10]Siyuan Dai, Joseph Hite, Takato Masuda, Yusuke Kashiba, Nikos Aréchiga, Shinichi Shiraishi, Scott Eisele, Jason Scott, Ted Bapty:
Control Parameter Optimization for Autonomous Vehicle Software Using Virtual Prototyping. ISSRE Workshops 2017: 73-76 - [c9]Ashlie B. Hocking, M. Anthony Aiello, John C. Knight, Nikos Aréchiga:
Input Space Partitioning to Enable Massively Parallel Proof. NFM 2017: 139-145 - 2016
- [j2]Jan-David Quesel, Stefan Mitsch
How to model and prove hybrid systems with KeYmaera: a tutorial on safety. Int. J. Softw. Tools Technol. Transf. 18(1): 67-91 (2016) - [c8]Ashlie B. Hocking, M. Anthony Aiello, John C. Knight, Nikos Aréchiga:
Proving Critical Properties of Simulink Models. HASE 2016: 189-196 - [c7]Weijing Shi, Mohamed Baker Alawieh
Efficient statistical validation of machine learning systems for autonomous driving. ICCAD 2016: 36 - 2015
- [c6]Nikos Aréchiga, James Kapinski, Jyotirmoy V. Deshmukh, André Platzer
Forward invariant cuts to simplify proofs of safety. EMSOFT 2015: 227-236 - [i1]Nikos Aréchiga, James Kapinski, Jyotirmoy V. Deshmukh, André Platzer, Bruce H. Krogh:
Forward Invariant Cuts to Simplify Proofs of Safety. CoRR abs/1507.05133 (2015) - 2014
- [c5]Nikos Aréchiga, Bruce H. Krogh:
Using verified control envelopes for safe controller design. ACC 2014: 2918-2923 - [c4]James Kapinski, Jyotirmoy V. Deshmukh, Sriram Sankaranarayanan, Nikos Aréchiga:
Simulation-guided lyapunov analysis for hybrid dynamical systems. HSCC 2014: 133-142 - [c3]Nikos Aréchiga, James Kapinski, Jyotirmoy V. Deshmukh, André Platzer
Numerically-aided Deductive Safety Proof for a Powertrain Control System. NSV 2014: 19-25 - 2012
- [c2]Nikos Aréchiga, Sarah M. Loos, André Platzer, Bruce H. Krogh:
Using theorem provers to guarantee closed-loop system properties. ACC 2012: 3573-3580 - 2010
- [j1]Nikolaus Correll, Nikos Aréchiga, Adrienne Bolger, Mario Bollini, Ben Charrow, Adam Clayton, Felipe Dominguez, Kenneth Donahue, Samuel Dyar, Luke Johnson, Huan Liu, Alexander Patrikalakis, Timothy Robertson, Jeremy Smith, Daniel E. Soltero, Melissa Tanner, Lauren White, Daniela Rus:
Indoor robot gardening: design and implementation. Intell. Serv. Robotics 3(4): 219-232 (2010)
2000 – 2009
- 2009
- [c1]Nikolaus Correll, Nikos Aréchiga, Adrienne Bolger, Mario Bollini, Benjamin Charrow, Adam Clayton, Felipe Dominguez, Kenneth Donahue, Samuel Dyar, Luke Johnson, Huan Liu, Alexander Patrikalakis, Timothy Robertson, Jeremy Smith, Daniel E. Soltero, Melissa Tanner, Lauren White, Daniela Rus:
Building a distributed robot garden. IROS 2009: 1509-1516
Coauthor Index
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