Nikos Aréchiga

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 45

Top Topics

Autonomous Vehicles

Diffusion Models

Preference Learning

Top Venues

Int. J. Softw. Tools Technol. Transf.

Publications

Ruya Karagulle, Nikos Aréchiga, Andrew Best, Jonathan A. DeCastro, Necmiye Ozay
A Safe Preference Learning Approach for Personalization With Applications to Autonomous Vehicles. IEEE Robotics Autom. Lett. 9 (5) (2024)
Ruya Karagulle, Necmiye Ozay, Nikos Aréchiga, Jonathan A. DeCastro, Andrew Best
Incorporating Logic in Online Preference Learning for Safe Personalization of Autonomous Vehicles. HSCC (2024)
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 (2024)
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 (2024)
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)
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 (2024)
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)
Anna Kawakami, Luke Guerdan, Yanghuidi Cheng, Kate Glazko, Matthew L. Lee, Scott A. Carter, Nikos Aréchiga, Haiyi Zhu, Kenneth Holstein
Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge. CI (2023)
Nikos Aréchiga, Frank Permenter, Binyang Song, Chenyang Yuan
Drag-guided diffusion models for vehicle image generation. CoRR (2023)
Ruya Karagulle, Nikos Aréchiga, Andrew Best, Jonathan A. DeCastro, Necmiye Ozay
A Preference Learning Approach to Develop Safe and Personalizable Autonomous Vehicles. CoRR (2023)
Karen Leung, Nikos Aréchiga, Marco Pavone
Backpropagation through signal temporal logic specifications: Infusing logical structure into gradient-based methods. Int. J. Robotics Res. 42 (6) (2023)
Binyang Song, Chenyang Yuan, Frank Permenter, Nikos Aréchiga, Faez Ahmed
Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings. CoRR (2023)
Ruya Karagulle, Nikos Aréchiga, Andrew Best, Jonathan A. DeCastro, Necmiye Ozay
Poster Abstract: Safety Guaranteed Preference Learning Approach for Autonomous Vehicles. HSCC (2023)
Xin Qin, Nikos Aréchiga, Jyotirmoy Deshmukh, Andrew Best
Robust Testing for Cyber-Physical Systems using Reinforcement Learning. MEMOCODE (2023)
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 (2023)
Ruya Karagulle, Nikos Aréchiga, Jonathan DeCastro, Necmiye Ozay
Classification of Driving Behaviors Using STL Formulas: A Comparative Study. FORMATS (2022)
Robert Dyro, Edward Schmerling, Nikos Aréchiga, Marco Pavone
Second-Order Sensitivity Analysis for Bilevel Optimization. AISTATS (2022)
Robert Dyro, Edward Schmerling, Nikos Aréchiga, Marco Pavone
Second-Order Sensitivity Analysis for Bilevel Optimization. CoRR (2022)
Daniel Kang, Nikos Aréchiga, Sudeep Pillai, Peter Bailis, Matei Zaharia
Finding Label and Model Errors in Perception Data With Learned Observation Assertions. CoRR (2022)
Daniel Kang, Nikos Aréchiga, Sudeep Pillai, Peter D. Bailis, Matei Zaharia
Finding Label and Model Errors in Perception Data With Learned Observation Assertions. SIGMOD Conference (2022)
Dan Li, Mohamad Louai Shehab, Zexiang Liu, Nikos Aréchiga, Jonathan DeCastro, Necmiye Ozay
Outlier-robust Inverse Reinforcement Learning and Reward-based Detection of Anomalous Driving Behaviors. ITSC (2022)
Nikos Aréchiga, Francine Chen, Rumen Iliev, Emily Sumner, Scott A. Carter, Alexandre Filipowicz, Nayeli Bravo, Monica Van, Kate Glazko, Kalani Murakami, Laurent Denoue, Candice Hogan, Katharine Sieck, Charlene Wu, Kent Lyons
Understanding and Shifting Preferences for Battery Electric Vehicles. CoRR (2022)
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 (2022)
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 (2021)
Kaidi Cao, Yining Chen, Junwei Lu, Nikos Aréchiga, Adrien Gaidon, Tengyu Ma
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization. ICLR (2021)
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) (2021)
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 (2021)
Karen Leung, Nikos Aréchiga, Marco Pavone
Back-Propagation Through Signal Temporal Logic Specifications: Infusing Logical Structure into Gradient-Based Methods. WAFR (2021)
Jonathan DeCastro, Karen Leung, Nikos Aréchiga, Marco Pavone
Interpretable Policies from Formally-Specified Temporal Properties. ITSC (2020)
Karen Leung, Nikos Aréchiga, Marco Pavone
Back-propagation through Signal Temporal Logic Specifications: Infusing Logical Structure into Gradient-Based Methods. CoRR (2020)
Kaidi Cao, Yining Chen, Junwei Lu, Nikos Aréchiga, Adrien Gaidon, Tengyu Ma
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization. CoRR (2020)
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Aréchiga, Tengyu Ma
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. CoRR (2019)
Karen Leung, Nikos Aréchiga, Marco Pavone
Backpropagation for Parametric STL. IV (2019)
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Aréchiga, Tengyu Ma
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. NeurIPS (2019)
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)
Xin Qin, Nikos Aréchiga, Andrew Best, Jyotirmoy V. Deshmukh
Automatic Testing and Falsification with Dynamically Constrained Reinforcement Learning. CoRR (2019)
Nikos Aréchiga, Jonathan DeCastro, Soonho Kong, Karen Leung
Better AI through Logical Scaffolding. CoRR (2019)
Nikos Aréchiga
Specifying Safety of Autonomous Vehicles in Signal Temporal Logic. IV (2019)
Sumanth Dathathri, Nikos Aréchiga, Sicun Gao, Richard M. Murray
Learning-Based Abstractions for Nonlinear Constraint Solving. IJCAI (2017)
Ashlie B. Hocking, M. Anthony Aiello, John C. Knight, Nikos Aréchiga
Input Space Partitioning to Enable Massively Parallel Proof. NFM (2017)
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)
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)
Jan-David Quesel, Stefan Mitsch, Sarah M. Loos, Nikos Aréchiga, André Platzer
How to model and prove hybrid systems with KeYmaera: a tutorial on safety. Int. J. Softw. Tools Technol. Transf. 18 (1) (2016)
Weijing Shi, Mohamed Baker Alawieh, Xin Li, Huafeng Yu, Nikos Aréchiga, Nobuyuki Tomatsu
Efficient statistical validation of machine learning systems for autonomous driving. ICCAD (2016)
Ashlie B. Hocking, M. Anthony Aiello, John C. Knight, Nikos Aréchiga
Proving Critical Properties of Simulink Models. HASE (2016)
Nikos Aréchiga, James Kapinski, Jyotirmoy V. Deshmukh, André Platzer, Bruce H. Krogh
Forward Invariant Cuts to Simplify Proofs of Safety. CoRR (2015)
Nikos Aréchiga, James Kapinski, Jyotirmoy V. Deshmukh, André Platzer, Bruce H. Krogh
Forward invariant cuts to simplify proofs of safety. EMSOFT (2015)
Nikos Aréchiga, James Kapinski, Jyotirmoy V. Deshmukh, André Platzer, Bruce H. Krogh
Numerically-aided Deductive Safety Proof for a Powertrain Control System. Electron. Notes Theor. Comput. Sci. 317 (2015)
James Kapinski, Jyotirmoy V. Deshmukh, Sriram Sankaranarayanan, Nikos Aréchiga
Simulation-guided lyapunov analysis for hybrid dynamical systems. HSCC (2014)
Nikos Aréchiga, Bruce H. Krogh
Using verified control envelopes for safe controller design. ACC (2014)
Nikos Aréchiga, Sarah M. Loos, André Platzer, Bruce H. Krogh
Using theorem provers to guarantee closed-loop system properties. ACC (2012)
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) (2010)
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)