Building a distributed robot garden. - Nikolaus Correll; Nikos Aréchiga; Adrienne Bolger; Mario Bollini; Benjamin Charrow; Adam Clayton; Felipe Dominguez; Kenneth Donahue; Samuel Dyar; Luke Johnson; Huan Liu; Alexander Patrikalakis; et al. (IROS, 2009)

Indoor robot gardening: design and implementation. - Nikolaus Correll; Nikos Aréchiga; Adrienne Bolger; Mario Bollini; Ben Charrow; Adam Clayton; Felipe Dominguez; Kenneth Donahue; Samuel Dyar; Luke Johnson; Huan Liu; Alexander Patrikalakis; et al. (Intell. Serv. Robotics, 2010)

Using theorem provers to guarantee closed-loop system properties. - Nikos Aréchiga; Sarah M. Loos; André Platzer; Bruce H. Krogh (ACC, 2012)

Using verified control envelopes for safe controller design. - Nikos Aréchiga; Bruce H. Krogh (ACC, 2014)

Simulation-guided lyapunov analysis for hybrid dynamical systems. - James Kapinski; Jyotirmoy V. Deshmukh; Sriram Sankaranarayanan 001; Nikos Aréchiga (HSCC, 2014)

Numerically-aided Deductive Safety Proof for a Powertrain Control System. - Nikos Aréchiga; James Kapinski; Jyotirmoy V. Deshmukh; André Platzer; Bruce H. Krogh (NSV, 2014)

Forward invariant cuts to simplify proofs of safety. - Nikos Aréchiga; James Kapinski; Jyotirmoy V. Deshmukh; André Platzer; Bruce H. Krogh (EMSOFT, 2015)

Proving Critical Properties of Simulink Models. - Ashlie B. Hocking; M. Anthony Aiello; John C. Knight; Nikos Aréchiga (HASE, 2016)

Efficient statistical validation of machine learning systems for autonomous driving. - Weijing Shi; Mohamed Baker Alawieh; Xin Li 001; Huafeng Yu; Nikos Aréchiga; Nobuyuki Tomatsu (ICCAD, 2016)

How to model and prove hybrid systems with KeYmaera: a tutorial on safety. - Jan-David Quesel; Stefan Mitsch; Sarah M. Loos; Nikos Aréchiga; André Platzer (Int. J. Softw. Tools Technol. Transf., 2016)

Osiris: A Tool for Abstraction and Verification of Control Software with Lookup Tables. - Nikos Aréchiga; Sumanth Dathathri; Shashank Vernekar; Nagesh Kathare; Sicun Gao; Shinichi Shiraishi (SCAV@CPSWeek, 2017)

Learning-Based Abstractions for Nonlinear Constraint Solving. - Sumanth Dathathri; Nikos Aréchiga; Sicun Gao; Richard M. Murray (IJCAI, 2017)

Control Parameter Optimization for Autonomous Vehicle Software Using Virtual Prototyping. - Siyuan Dai; Joseph Hite; Takato Masuda; Yusuke Kashiba; Nikos Aréchiga; Shinichi Shiraishi; Scott Eisele; Jason Scott; Ted Bapty (ISSRE Workshops, 2017)

Input Space Partitioning to Enable Massively Parallel Proof. - Ashlie B. Hocking; M. Anthony Aiello; John C. Knight; Nikos Aréchiga (NFM, 2017)

Numerically-Robust Inductive Proof Rules for Continuous Dynamical Systems. - Sicun Gao; James Kapinski; Jyotirmoy V. Deshmukh; Nima Roohi; Armando Solar-Lezama; Nikos Aréchiga; Soonho Kong (CAV (2), 2019)

Specifying Safety of Autonomous Vehicles in Signal Temporal Logic. - Nikos Aréchiga (IV, 2019)

Backpropagation for Parametric STL. - Karen Leung; Nikos Aréchiga; Marco Pavone 001 (IV, 2019)

Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. - Kaidi Cao; Colin Wei; Adrien Gaidon; Nikos Aréchiga; Tengyu Ma 001 (NeurIPS, 2019)

Better AI through Logical Scaffolding. - Nikos Aréchiga; Jonathan A. DeCastro; Soonho Kong; Karen Leung (CoRR, 2019)

Automatic Testing and Falsification with Dynamically Constrained Reinforcement Learning. - Xin Qin; Nikos Aréchiga; Andrew Best; Jyotirmoy V. Deshmukh (CoRR, 2019)

Interpretable Policies from Formally-Specified Temporal Properties. - Jonathan A. DeCastro; Karen Leung; Nikos Aréchiga; Marco Pavone 001 (ITSC, 2020)

Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization. - Kaidi Cao; Yining Chen; Junwei Lu; Nikos Aréchiga; Adrien Gaidon; Tengyu Ma 001 (ICLR, 2021)

Back-Propagation Through Signal Temporal Logic Specifications: Infusing Logical Structure into Gradient-Based Methods. - Karen Leung; Nikos Aréchiga; Marco Pavone 001 (WAFR, 2021)

Certified Control: An Architecture for Verifiable Safety of Autonomous Vehicles. - Daniel Jackson 001; Valerie Richmond; Mike Wang; Jeff Chow; Uriel Guajardo; Soonho Kong; Sergio Campos; Geoffrey Litt; Nikos Aréchiga (CoRR, 2021)

Accelerating Understanding of Scientific Experiments with End to End Symbolic Regression. - Nikos Aréchiga; Francine Chen 001; Yan-Ying Chen; Yanxia Zhang; Rumen Iliev; Heishiro Toyoda; Kent Lyons (CoRR, 2021)

Correction to: How to model and prove hybrid systems with KeYmaera: a tutorial on safety. - Jan-David Quesel; Stefan Mitsch; Sarah M. Loos; Nikos Aréchiga; André Platzer (Int. J. Softw. Tools Technol. Transf., 2021)

Second-Order Sensitivity Analysis for Bilevel Optimization. - Robert Dyro; Edward Schmerling; Nikos Aréchiga; Marco Pavone 001 (AISTATS, 2022)

Classification of Driving Behaviors Using STL Formulas: A Comparative Study. - Ruya Karagulle; Nikos Aréchiga; Jonathan A. DeCastro; Necmiye Ozay (FORMATS, 2022)

Outlier-robust Inverse Reinforcement Learning and Reward-based Detection of Anomalous Driving Behaviors. - Dan Li; Mohamad Louai Shehab; Zexiang Liu; Nikos Aréchiga; Jonathan A. DeCastro; Necmiye Ozay (ITSC, 2022)

Finding Label and Model Errors in Perception Data With Learned Observation Assertions. - Daniel Kang; Nikos Aréchiga; Sudeep Pillai; Peter D. Bailis; Matei Zaharia (SIGMOD Conference, 2022)

Understanding and Shifting Preferences for Battery Electric Vehicles. - Nikos Aréchiga; Francine Chen 001; 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; et al. (CoRR, 2022)

Towards a Learner-Centered Explainable AI: Lessons from the learning sciences. - Anna Kawakami; Luke Guerdan; Yang Cheng; Anita Sun; Alison Hu; Kate Glazko; Nikos Aréchiga; Matthew L. Lee; Scott A. Carter; Haiyi Zhu; Kenneth Holstein (CoRR, 2022)

Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge. - Anna Kawakami; Luke Guerdan; Yanghuidi Cheng; Kate Glazko; Matthew L. Lee; Scott A. Carter; Nikos Aréchiga; Haiyi Zhu; Kenneth Holstein (CI, 2023)

Can Behavioral Experts Predict Outcome Heterogeneity? - Rumen Iliev; Alexandre L. S. Filipowicz; Emily Sarah Sumner; Francine Chen 001; Nikos Aréchiga; Scott A. Carter; Totte Harinen; Kate A. Sieck; Charlene C. Wu (CogSci, 2023)

Poster Abstract: Safety Guaranteed Preference Learning Approach for Autonomous Vehicles. - Ruya Karagulle; Nikos Aréchiga; Andrew Best; Jonathan A. DeCastro; Necmiye Ozay (HSCC, 2023)

Robust Testing for Cyber-Physical Systems using Reinforcement Learning. - Xin Qin; Nikos Aréchiga; Jyotirmoy Deshmukh; Andrew Best (MEMOCODE, 2023)

Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings. - Binyang Song; Chenyang Yuan 001; Frank Permenter; Nikos Aréchiga; Faez Ahmed (CoRR, 2023)

Drag-guided diffusion models for vehicle image generation. - Nikos Aréchiga; Frank Permenter; Binyang Song; Chenyang Yuan 001 (CoRR, 2023)

A Preference Learning Approach to Develop Safe and Personalizable Autonomous Vehicles. - Ruya Karagulle; Nikos Aréchiga; Andrew Best; Jonathan A. DeCastro; Necmiye Ozay (CoRR, 2023)

Backpropagation through signal temporal logic specifications: Infusing logical structure into gradient-based methods. - Karen Leung; Nikos Aréchiga; Marco Pavone 001 (Int. J. Robotics Res., 2023)

Incorporating Logic in Online Preference Learning for Safe Personalization of Autonomous Vehicles. - Ruya Karagulle; Necmiye Ozay; Nikos Aréchiga; Jonathan A. DeCastro; Andrew Best (HSCC, 2024)

On LLM Wizards: Identifying Large Language Models' Behaviors for Wizard of Oz Experiments. - Jingchao Fang; Nikos Aréchiga; Keiichi Namikoshi; Nayeli Bravo; Candice Hogan; David A. Shamma (IVA, 2024)

Learning true objectives: Linear algebraic characterizations of identifiability in inverse reinforcement learning. - Mohamad Louai Shehab; Antoine Aspeel; Nikos Aréchiga; Andrew Best; Necmiye Ozay (L4DC, 2024)

Using LLMs to Model the Beliefs and Preferences of Targeted Populations. - Keiichi Namikoshi; Alex Filipowicz; David A. Shamma; Rumen Iliev; Candice L. Hogan; Nikos Aréchiga (CoRR, 2024)

Bridging Design Gaps: A Parametric Data Completion Approach With Graph Guided Diffusion Models. - Rui Zhou; Chenyang Yuan 001; Frank Permenter; Yanxia Zhang; Nikos Aréchiga; Matthew Klenk 001; Faez Ahmed (CoRR, 2024)

On LLM Wizards: Identifying Large Language Models' Behaviors for Wizard of Oz Experiments. - Jingchao Fang; Nikos Aréchiga; Keiichi Namaoshi; Nayeli Bravo; Candice Hogan; David A. Shamma (CoRR, 2024)

Leveraging Language Models and Bandit Algorithms to Drive Adoption of Battery-Electric Vehicles. - Keiichi Namikoshi; David A. Shamma; Rumen Iliev; Jingchao Fang; Alexandre L. S. Filipowicz; Candice L. Hogan; Charlene C. Wu; Nikos Aréchiga (CoRR, 2024)

Parametric-ControlNet: Multimodal Control in Foundation Models for Precise Engineering Design Synthesis. - Rui Zhou; Yanxia Zhang; Chenyang Yuan 001; Frank Permenter; Nikos Aréchiga; Matt Klenk; Faez Ahmed (CoRR, 2024)

Stylish and Functional: Guided Interpolation Subject to Physical Constraints. - Yan-Ying Chen; Nikos Aréchiga; Chenyang Yuan; Matthew Hong 001; Matt Klenk; Charlene C. Wu (CoRR, 2024)

A Safe Preference Learning Approach for Personalization With Applications to Autonomous Vehicles. - Ruya Karagulle; Nikos Aréchiga; Andrew Best; Jonathan A. DeCastro; Necmiye Ozay (IEEE Robotics Autom. Lett., 2024)

ConjointNet: Enhancing Conjoint Analysis for Preference Prediction with Representation Learning. - Yanxia Zhang; Francine Chen 001; Shabnam Hakimi; Totte Harinen; Alex Filipowicz; Yan-Ying Chen; Rumen Iliev; Nikos Aréchiga; Kalani Murakami; Kent Lyons; Charlene C. Wu; Matt Klenk (CoRR, 2025)