Pappas, George J.

48 publications

ICML 2025 Adversarial Reasoning at Jailbreaking Time Mahdi Sabbaghi, Paul Kassianik, George J. Pappas, Amin Karbasi, Hamed Hassani

TMLR 2025 Automated Black-Box Prompt Engineering for Personalized Text-to-Image Generation Yutong He, Alexander Robey, Naoki Murata, Yiding Jiang, Joshua Nathaniel Williams, George J. Pappas, Hamed Hassani, Yuki Mitsufuji, Ruslan Salakhutdinov, J Zico Kolter

ICLR 2025 CViT: Continuous Vision Transformer for Operator Learning Sifan Wang, Jacob H Seidman, Shyam Sankaran, Hanwen Wang, George J. Pappas, Paris Perdikaris

NeurIPS 2025 Conformal Inference Under High-Dimensional Covariate Shifts via Likelihood-Ratio Regularization Sunay Joshi, Shayan Kiyani, George J. Pappas, Edgar Dobriban, Hamed Hassani

NeurIPS 2025 Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models Sima Noorani, Shayan Kiyani, George J. Pappas, Hamed Hassani

ICML 2025 Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents Shayan Kiyani, George J. Pappas, Aaron Roth, Hamed Hassani

CoRL 2025 Distilling On-Device Language Models for Robot Planning with Minimal Human Intervention Zachary Ravichandran, Ignacio Hounie, Fernando Cladera, Alejandro Ribeiro, George J. Pappas, Vijay Kumar

L4DC 2025 Domain Randomization Is Sample Efficient for Linear Quadratic Control Tesshu Fujinami, Bruce D. Lee, Nikolai Matni, George J. Pappas

TMLR 2025 SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks Alexander Robey, Eric Wong, Hamed Hassani, George J. Pappas

NeurIPS 2025 Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands Vasiliki Tassopoulou, Charis Stamouli, Haochang Shou, George J. Pappas, Christos Davatzikos

ICLR 2024 Adversarial Training Should Be Cast as a Non-Zero-Sum Game Alexander Robey, Fabian Latorre, George J. Pappas, Hamed Hassani, Volkan Cevher

AAAI 2024 Conformal Prediction Regions for Time Series Using Linear Complementarity Programming Matthew Cleaveland, Insup Lee, George J. Pappas, Lars Lindemann

ICML 2024 Conformal Prediction with Learned Features Shayan Kiyani, George J. Pappas, Hamed Hassani

TMLR 2024 Federated TD Learning with Linear Function Approximation Under Environmental Heterogeneity Han Wang, Aritra Mitra, Hamed Hassani, George J. Pappas, James Anderson

ICML 2024 Guarantees for Nonlinear Representation Learning: Non-Identical Covariates, Dependent Data, Fewer Samples Thomas Tck Zhang, Bruce D Lee, Ingvar Ziemann, George J. Pappas, Nikolai Matni

NeurIPS 2024 JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramèr, Hamed Hassani, Eric Wong

ICMLW 2024 JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramèr, Hamed Hassani, Eric Wong

ICML 2024 Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss Ingvar Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni

AISTATS 2024 Stochastic Approximation with Delayed Updates: Finite-Time Rates Under Markovian Sampling Arman Adibi, Nicolò Fabbro, Luca Schenato, Sanjeev Kulkarni, H. Vincent Poor, George J. Pappas, Hamed Hassani, Aritra Mitra

TMLR 2024 Temporal Difference Learning with Compressed Updates: Error-Feedback Meets Reinforcement Learning Aritra Mitra, George J. Pappas, Hamed Hassani

L4DC 2024 Uncertainty Quantification and Robustification of Model-Based Controllers Using Conformal Prediction Kong Yao Chee, Thales C. Silva, M. Ani Hsieh, George J. Pappas

L4DC 2023 Adaptive Conformal Prediction for Motion Planning Among Dynamic Agents Anushri Dixit, Lars Lindemann, Skylar X Wei, Matthew Cleaveland, George J. Pappas, Joel W. Burdick

ICMLW 2023 Adversarial Training Should Be Cast as a Non-Zero-Sum Game Alexander Robey, Fabian Latorre, George J. Pappas, Hamed Hassani, Volkan Cevher

L4DC 2023 Certified Invertibility in Neural Networks via Mixed-Integer Programming Tianqi Cui, Thomas Bertalan, George J. Pappas, Manfred Morari, Yannis Kevrekidis, Mahyar Fazlyab

NeurIPSW 2023 Jailbreaking Black Box Large Language Models in Twenty Queries Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong

L4DC 2023 Linear Stochastic Bandits over a Bit-Constrained Channel Aritra Mitra, Hamed Hassani, George J. Pappas

L4DC 2023 Physics-Enhanced Gaussian Process Variational Autoencoder Thomas Beckers, Qirui Wu, George J. Pappas

NeurIPS 2023 The Noise Level in Linear Regression with Dependent Data Ingvar Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni

ICML 2023 Variational Autoencoding Neural Operators Jacob H Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris

L4DC 2022 Adaptive Stochastic MPC Under Unknown Noise Distribution Charis Stamouli, Anastasios Tsiamis, Manfred Morari, George J. Pappas

NeurIPS 2022 Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds Aritra Mitra, Arman Adibi, George J. Pappas, Hamed Hassani

ICLR 2022 Do Deep Networks Transfer Invariances Across Classes? Allan Zhou, Fahim Tajwar, Alexander Robey, Tom Knowles, George J. Pappas, Hamed Hassani, Chelsea Finn

JMLR 2022 Learning Operators with Coupled Attention Georgios Kissas, Jacob H. Seidman, Leonardo Ferreira Guilhoto, Victor M. Preciado, George J. Pappas, Paris Perdikaris

COLT 2022 Learning to Control Linear Systems Can Be Hard Anastasios Tsiamis, Ingvar M Ziemann, Manfred Morari, Nikolai Matni, George J. Pappas

NeurIPS 2022 NOMAD: Nonlinear Manifold Decoders for Operator Learning Jacob Seidman, Georgios Kissas, Paris Perdikaris, George J. Pappas

ICML 2022 Probabilistically Robust Learning: Balancing Average and Worst-Case Performance Alexander Robey, Luiz Chamon, George J. Pappas, Hamed Hassani

NeurIPS 2022 Probable Domain Generalization via Quantile Risk Minimization Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen, Hamed Hassani, George J. Pappas, Bernhard Schölkopf

NeurIPS 2021 Adversarial Robustness with Semi-Infinite Constrained Learning Alexander Robey, Luiz Chamon, George J. Pappas, Hamed Hassani, Alejandro Ribeiro

NeurIPS 2021 Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients Aritra Mitra, Rayana Jaafar, George J. Pappas, Hamed Hassani

NeurIPS 2021 Model-Based Domain Generalization Alexander Robey, George J. Pappas, Hamed Hassani

L4DC 2021 Optimal Algorithms for Submodular Maximization with Distributed Constraints Alexander Robey, Arman Adibi, Brent Schlotfeldt, Hamed Hassani, George J. Pappas

L4DC 2021 Preface Ali Jadbabaie, John Lygeros, George J. Pappas, Pablo A. Parrilo, Benjamin Recht, Claire J. Tomlin, Melanie N. Zeilinger

NeurIPS 2021 Safe Pontryagin Differentiable Programming Wanxin Jin, Shaoshuai Mou, George J. Pappas

L4DC 2020 Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees Jacob H. Seidman, Mahyar Fazlyab, Victor M. Preciado, George J. Pappas

IJCAI 2019 Assumed Density Filtering Q-Learning Heejin Jeong, Clark Zhang, George J. Pappas, Daniel D. Lee

IJCAI 2018 A Unifying View of Geometry, Semantics, and Data Association in SLAM Nikolay Atanasov, Sean L. Bowman, Kostas Daniilidis, George J. Pappas

ECCV 2014 Active Deformable Part Models Inference Menglong Zhu, Nikolay Atanasov, George J. Pappas, Kostas Daniilidis

CVPRW 2009 3D Segmentation of Rodent Brains Using Deformable Models and Variational Methods Shaoting Zhang, Jinghao Zhou, Xiaoxu Wang, Sukmoon Chang, Dimitris N. Metaxas, George J. Pappas, Foteini Delis, Nora D. Volkow, Gene-Jack Wang, Panayotis K. Thanos, Chandra Kambhamettu