Fri 11:50 a.m. - 12:00 p.m.
|
Opening
(
Opening
)
>
SlidesLive Video
|
๐
|
Fri 12:00 p.m. - 12:30 p.m.
|
Una-May O'Reilly
(
Keynote
)
>
SlidesLive Video
|
Una-May O'Reilly
๐
|
Fri 12:30 p.m. - 1:00 p.m.
|
Lea Schรถnherr
(
Keynote
)
>
SlidesLive Video
|
Lea Schรถnherr
๐
|
Fri 1:00 p.m. - 1:10 p.m.
|
Adversarial Training Should Be Cast as a Non-Zero-Sum Game
(
Oral
)
>
link
SlidesLive Video
|
Alex Robey ยท Fabian Latorre ยท George J. Pappas ยท Hamed Hassani ยท Volkan Cevher
๐
|
Fri 1:10 p.m. - 1:20 p.m.
|
Evading Black-box Classifiers Without Breaking Eggs
(
Oral
)
>
link
SlidesLive Video
|
Edoardo Debenedetti ยท Nicholas Carlini ยท Florian Tramer
๐
|
Fri 1:20 p.m. - 1:30 p.m.
|
Tunable Dual-Objective GANs for Stable Training
(
Oral
)
>
link
SlidesLive Video
|
Monica Welfert ยท Kyle Otstot ยท Gowtham Kurri ยท Lalitha Sankar
๐
|
Fri 1:30 p.m. - 2:00 p.m.
|
Jihun Hamm
(
Keynote
)
>
SlidesLive Video
|
Jihun Hamm
๐
|
Fri 2:00 p.m. - 2:30 p.m.
|
Kamalika Chaudhuri
(
Keynote
)
>
SlidesLive Video
|
Kamalika Chaudhuri
๐
|
Fri 2:30 p.m. - 4:00 p.m.
|
Posters
(
Posters
)
>
|
๐
|
Fri 4:00 p.m. - 4:30 p.m.
|
Atlas Wang
(
Keynote
)
>
SlidesLive Video
|
Zhangyang โAtlasโ Wang
๐
|
Fri 4:30 p.m. - 5:00 p.m.
|
Stacy Hobson
(
Keynote
)
>
SlidesLive Video
|
Stacy Fay Hobson
๐
|
Fri 5:00 p.m. - 5:10 p.m.
|
Visual Adversarial Examples Jailbreak Aligned Large Language Models
(
Oral
)
>
link
SlidesLive Video
|
Xiangyu Qi ยท Kaixuan Huang ยท Ashwinee Panda ยท Mengdi Wang ยท Prateek Mittal
๐
|
Fri 5:10 p.m. - 5:20 p.m.
|
Learning Shared Safety Constraints from Multi-task Demonstrations
(
Oral
)
>
link
SlidesLive Video
|
Konwoo Kim ยท Gokul Swamy ยท Zuxin Liu ยท Ding Zhao ยท Sanjiban Choudhury ยท Steven Wu
๐
|
Fri 5:20 p.m. - 5:25 p.m.
|
MLSMM: Machine Learning Security Maturity Model
(
Bluesky Oral
)
>
link
SlidesLive Video
|
Felix Jedrzejewski ยท Davide Fucci ยท Oleksandr Adamov
๐
|
Fri 5:25 p.m. - 5:30 p.m.
|
Deceptive Alignment Monitoring
(
Bluesky Oral
)
>
link
SlidesLive Video
|
Andres Carranza ยท Dhruv Pai ยท Rylan Schaeffer ยท Arnuv Tandon ยท Sanmi Koyejo
๐
|
Fri 5:30 p.m. - 6:00 p.m.
|
Aditi Raghunathan
(
Keynote
)
>
SlidesLive Video
|
Aditi Raghunathan
๐
|
Fri 6:00 p.m. - 6:30 p.m.
|
Zico Kolter
(
Keynote
)
>
SlidesLive Video
|
Zico Kolter
๐
|
Fri 6:30 p.m. - 6:35 p.m.
|
How Can Neuroscience Help Us Build More Robust Deep Neural Networks?
(
Bluesky Oral
)
>
link
SlidesLive Video
|
Sayanton Dibbo ยท Siddharth Mansingh ยท Jocelyn Rego ยท Garrett T Kenyon ยท Juston Moore ยท Michael Teti
๐
|
Fri 6:35 p.m. - 6:40 p.m.
|
The Future of Cyber Systems: Human-AI Reinforcement Learning with Adversarial Robustness
(
Bluesky Oral
)
>
link
SlidesLive Video
|
Nicole Nichols
๐
|
Fri 6:40 p.m. - 6:45 p.m.
|
Announcement of AdvML Rising Star Award
(
Announcement
)
>
SlidesLive Video
|
๐
|
Fri 6:45 p.m. - 7:00 p.m.
|
Tianlong Chen
(
Award presentation
)
>
SlidesLive Video
|
๐
|
Fri 7:00 p.m. - 7:15 p.m.
|
Vikash Sehwag
(
Award presentation
)
>
SlidesLive Video
|
๐
|
Fri 7:15 p.m. - 8:00 p.m.
|
Posters
(
Posters
)
>
|
๐
|
Fri 8:00 p.m. - 8:00 p.m.
|
Closing
(
Closing
)
>
|
๐
|
-
|
The Challenge of Differentially Private Screening Rules
(
Poster
)
>
link
|
Amol Khanna ยท Fred Lu ยท Edward Raff
๐
|
-
|
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
(
Poster
)
>
link
|
Zhihang Yuan ยท Jiawei Liu ยท Jiaxiang Wu ยท Dawei Yang ยท Qiang Wu ยท Guangyu Sun ยท Wenyu Liu ยท Xinggang Wang ยท Bingzhe Wu
๐
|
-
|
Benchmarking Adversarial Robustness of Compressed Deep Learning Models
(
Poster
)
>
link
|
Brijesh Vora ยท Kartik Patwari ยท Syed Mahbub Hafiz ยท Zubair Shafiq ยท Chen-Nee Chuah
๐
|
-
|
Robustness through Data Augmentation Loss Consistency
(
Poster
)
>
link
|
Tianjian Huang ยท Shaunak Halbe ยท Chinnadhurai Sankar ยท Pooyan Amini ยท Satwik Kottur ยท Alborz Geramifard ยท Meisam Razaviyayn ยท Ahmad Beirami
๐
|
-
|
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness
(
Poster
)
>
link
|
Francesco Campi ยท Lukas Gosch ยท Tom Wollschlรคger ยท Yan Scholten ยท Stephan Gรผnnemann
๐
|
-
|
Provably Robust Cost-Sensitive Learning via Randomized Smoothing
(
Poster
)
>
link
|
Yuan Xin ยท Michael Backes ยท Xiao Zhang
๐
|
-
|
Like Oil and Water: Group Robustness and Poisoning Defenses Donโt Mix
(
Poster
)
>
link
|
Michael-Andrei Panaitescu-Liess ยท Yigitcan Kaya ยท Tudor Dumitras
๐
|
-
|
Provable Instance Specific Robustness via Linear Constraints
(
Poster
)
>
link
|
Ahmed Imtiaz Humayun ยท Josue Casco-Rodriguez ยท Randall Balestriero ยท Richard Baraniuk
๐
|
-
|
Adversarial Training in Continuous-Time Models and Irregularly Sampled Time-Series
(
Poster
)
>
link
|
Alvin Li ยท Mathias Lechner ยท Alexander Amini ยท Daniela Rus
๐
|
-
|
Few-shot Anomaly Detection via Personalization
(
Poster
)
>
link
|
Sangkyung Kwak ยท Jongheon Jeong ยท Hankook Lee ยท Woohyuck Kim ยท Jinwoo Shin
๐
|
-
|
Rethinking Label Poisoning for GNNs: Pitfalls and Attacks
(
Poster
)
>
link
|
Vijay Lingam ยท Mohammad Sadegh Akhondzadeh ยท Aleksandar Bojchevski
๐
|
-
|
Shrink & Cert: Bi-level Optimization for Certified Robustness
(
Poster
)
>
link
|
Kavya Gupta ยท Sagar Verma
๐
|
-
|
Preventing Reward Hacking with Occupancy Measure Regularization
(
Poster
)
>
link
|
Cassidy Laidlaw ยท Shivam Singhal ยท Anca Dragan
๐
|
-
|
Baselines for Identifying Watermarked Large Language Models
(
Poster
)
>
link
|
Leonard Tang ยท Gavin Uberti ยท Tom Shlomi
๐
|
-
|
Why do universal adversarial attacks work on large language models?: Geometry might be the answer
(
Poster
)
>
link
|
Varshini Subhash ยท Anna Bialas ยท Siddharth Swaroop ยท Weiwei Pan ยท Finale Doshi-Velez
๐
|
-
|
FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation
(
Poster
)
>
link
|
Dhruv Pai ยท Andres Carranza ยท Rylan Schaeffer ยท Arnuv Tandon ยท Sanmi Koyejo
๐
|
-
|
Robust Deep Learning via Layerwise Tilted Exponentials
(
Poster
)
>
link
|
Bhagyashree Puranik ยท Ahmad Beirami ยท Yao Qin ยท Upamanyu Madhow
๐
|
-
|
Teach GPT To Phish
(
Poster
)
>
link
|
Ashwinee Panda ยท Zhengming Zhang ยท Yaoqing Yang ยท Prateek Mittal
๐
|
-
|
Physics-oriented adversarial attacks on SAR image target recognition
(
Poster
)
>
link
|
Jiahao Cui ยท wang Guo ยท Run Shao ยท tiandong Shi ยท Haifeng Li
๐
|
-
|
Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage
(
Poster
)
>
link
|
Catherine Huang ยท Chelse Swoopes ยท Christina Xiao ยท Jiaqi Ma ยท Himabindu Lakkaraju
๐
|
-
|
Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks
(
Poster
)
>
link
|
Ashish Hooda ยท Neal Mangaokar ยท Ryan Feng ยท Kassem Fawaz ยท Somesh Jha ยท Atul Prakash
๐
|
-
|
DiffScene: Diffusion-Based Safety-Critical Scenario Generation for Autonomous Vehicles
(
Poster
)
>
link
|
Chejian Xu ยท Ding Zhao ยท Alberto Sngiovanni Vincentelli ยท Bo Li
๐
|
-
|
Improving Adversarial Training for Multiple Perturbations through the Lens of Uniform Stability
(
Poster
)
>
link
|
Jiancong Xiao ยท Zeyu Qin ยท Yanbo Fan ยท Baoyuan Wu ยท Jue Wang ยท Zhi-Quan Luo
๐
|
-
|
A Theoretical Perspective on the Robustness of Feature Extractors
(
Poster
)
>
link
|
Arjun Nitin Bhagoji ยท Daniel Cullina ยท Ben Zhao
๐
|
-
|
Characterizing the Optimal 0โ1 Loss for Multi-class Classification with a Test-time Attacker
(
Poster
)
>
link
|
Sophie Dai ยท Wenxin Ding ยท Arjun Nitin Bhagoji ยท Daniel Cullina ยท Ben Zhao ยท Heather Zheng ยท Prateek Mittal
๐
|
-
|
RODEO: Robust Out-of-distribution Detection via Exposing Adaptive Outliers
(
Poster
)
>
link
|
Hossein Mirzaei ยท Mohammad Jafari ยท Hamid Reza Dehbashi ยท Ali Ansari ยท Sepehr Ghobadi ยท Masoud Hadi ยท Arshia Soltani Moakhar ยท Mohammad Azizmalayeri ยท Mahdieh Soleymani Baghshah ยท Mohammad H Rohban
๐
|
-
|
Rethinking Robust Contrastive Learning from the Adversarial Perspective
(
Poster
)
>
link
|
Fatemeh Ghofrani ยท Mehdi Yaghouti ยท Pooyan Jamshidi
๐
|
-
|
TMI! Finetuned Models Spill Secrets from Pretraining
(
Poster
)
>
link
|
John Abascal ยท Stanley Wu ยท Alina Oprea ยท Jonathan Ullman
๐
|
-
|
A First Order Meta Stackelberg Method for Robust Federated Learning
(
Poster
)
>
link
|
Yunian Pan ยท Tao Li ยท Henger Li ยท Tianyi Xu ยท Quanyan Zhu ยท Zizhan Zheng
๐
|
-
|
Backdoor Attacks for In-Context Learning with Language Models
(
Poster
)
>
link
|
Nikhil Kandpal ยท Matthew Jagielski ยท Florian Tramer ยท Nicholas Carlini
๐
|
-
|
R-LPIPS: An Adversarially Robust Perceptual Similarity Metric
(
Poster
)
>
link
|
Sara Ghazanfari ยท Siddharth Garg ยท Prashanth Krishnamurthy ยท Farshad Khorrami ยท Alexandre Araujo
๐
|
-
|
Risk-Averse Predictions on Unseen Domains via Neural Style Smoothing
(
Poster
)
>
link
|
Akshay Mehra ยท Yunbei Zhang ยท Bhavya Kailkhura ยท Jihun Hamm
๐
|
-
|
A Simple and Yet Fairly Effective Defense for Graph Neural Networks
(
Poster
)
>
link
|
Sofiane ENNADIR ยท Yassine Abbahaddou ยท Michalis Vazirgiannis ยท Henrik Bostrรถm
๐
|
-
|
Incentivizing Honesty among Competitors in Collaborative Learning
(
Poster
)
>
link
|
Florian Dorner ยท Nikola Konstantinov ยท Georgi Pashaliev ยท Martin Vechev
๐
|
-
|
Towards Effective Data Poisoning for Imbalanced Classification
(
Poster
)
>
link
|
Snigdha Sushil Mishra ยท Hao He ยท Hao Wang
๐
|
-
|
Black Box Adversarial Prompting for Foundation Models
(
Poster
)
>
link
|
Natalie Maus ยท Patrick Chao ยท Eric Wong ยท Jacob Gardner
๐
|
-
|
Exposing the Fake: Effective Diffusion-Generated Images Detection
(
Poster
)
>
link
|
RuiPeng Ma ยท Jinhao Duan ยท Fei Kong ยท Xiaoshuang Shi ยท Kaidi Xu
๐
|
-
|
AdversNLP: A Practical Guide to Assessing NLP Robustness Against Text Adversarial Attacks
(
Poster
)
>
link
|
Othmane BELMOUKADAM
๐
|
-
|
Proximal Compositional Optimization for Distributionally Robust Learning
(
Poster
)
>
link
|
Prashant Khanduri ยท Chengyin Li ยท RAFI IBN SULTAN ยท Yao Qiang ยท Joerg Kliewer ยท Dongxiao Zhu
๐
|
-
|
PIAT: Parameter Interpolation based Adversarial Training for Image Classification
(
Poster
)
>
link
|
Kun He ยท Xin Liu ยท Yichen Yang ยท Zhou Qin ยท Weigao Wen ยท Hui Xue' ยท John Hopcroft
๐
|
-
|
Mathematical Theory of Adversarial Deep Learning
(
Poster
)
>
link
|
Xiao-Shan Gao ยท Lijia Yu ยท Shuang Liu
๐
|
-
|
Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations
(
Poster
)
>
link
|
Yongyuan Liang ยท Yanchao Sun ยท Ruijie Zheng ยท Xiangyu Liu ยท Tuomas Sandholm ยท Furong Huang ยท Stephen Mcaleer
๐
|
-
|
Navigating Graph Robust Learning against All-Intensity Attacks
(
Poster
)
>
link
|
Xiangchi Yuan ยท Chunhui Zhang ยท Yijun Tian ยท Chuxu Zhang
๐
|
-
|
Towards Out-of-Distribution Adversarial Robustness
(
Poster
)
>
link
|
Adam Ibrahim ยท Charles Guille-Escuret ยท Ioannis Mitliagkas ยท Irina Rish ยท David Krueger ยท Pouya Bashivan
๐
|
-
|
Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations
(
Poster
)
>
link
|
Hyeonjeong Ha ยท Minseon Kim ยท Sung Ju Hwang
๐
|
-
|
Adversarial Robustness for Tabular Data through Cost and Utility Awareness
(
Poster
)
>
link
|
Klim Kireev ยท Bogdan Kulynych ยท Carmela Troncoso
๐
|
-
|
Scoring Black-Box Models for Adversarial Robustness
(
Poster
)
>
link
|
Jian Vora ยท Pranay Reddy Samala
๐
|
-
|
When Can Linear Learners be Robust to Indiscriminate Poisoning Attacks?
(
Poster
)
>
link
|
Fnu Suya ยท Xiao Zhang ยท Yuan Tian ยท David Evans
๐
|
-
|
Context-Aware Self-Adaptation for Domain Generalization
(
Poster
)
>
link
|
Hao Yan ยท Yuhong Guo
๐
|
-
|
Label Noise: Correcting a Correction Loss
(
Poster
)
>
link
|
William Toner ยท Amos Storkey
๐
|
-
|
Robust Semantic Segmentation: Strong Adversarial Attacks and Fast Training of Robust Models
(
Poster
)
>
link
|
Francesco Croce ยท Naman Singh ยท Matthias Hein
๐
|
-
|
Model-tuning Via Prompts Makes NLP Models Adversarially Robust
(
Poster
)
>
link
|
Mrigank Raman ยท Pratyush Maini ยท Zico Kolter ยท Zachary Lipton ยท Danish Pruthi
๐
|
-
|
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
(
Poster
)
>
link
|
Suraj Srinivas ยท Sebastian Bordt ยท Himabindu Lakkaraju
๐
|
-
|
Refined and Enriched Physics-based Captions for Unseen Dynamic Changes
(
Poster
)
>
link
|
Hidetomo Sakaino
๐
|
-
|
Adaptive Certified Training: Towards Better Accuracy-Robustness Tradeoffs
(
Poster
)
>
link
|
Zhakshylyk Nurlanov ยท Frank R Schmidt ยท Florian Bernard
๐
|
-
|
Illusory Attacks: Detectability Matters in Adversarial Attacks on Sequential Decision-Makers
(
Poster
)
>
link
|
Tim Franzmeyer ยท Stephen Mcaleer ยท Joao Henriques ยท Jakob Foerster ยท Phil Torr ยท Adel Bibi ยท Christian Schroeder
๐
|
-
|
Certified Calibration: Bounding Worst-Case Calibration under Adversarial Attacks
(
Poster
)
>
link
|
Cornelius Emde ยท Francesco Pinto ยท Thomas Lukasiewicz ยท Phil Torr ยท Adel Bibi
๐
|
-
|
Don't trust your eyes: on the (un)reliability of feature visualizations
(
Poster
)
>
link
|
Robert Geirhos ยท Roland S. Zimmermann ยท Blair Bilodeau ยท Wieland Brendel ยท Been Kim
๐
|
-
|
Classifier Robustness Enhancement Via Test-Time Transformation
(
Poster
)
>
link
|
Tsachi Blau ยท Roy Ganz ยท Chaim Baskin ยท Michael Elad ยท Alex Bronstein
๐
|
-
|
CertViT: Certified Robustness of Pre-Trained Vision Transformers
(
Poster
)
>
link
|
Kavya Gupta ยท Sagar Verma
๐
|
-
|
Transferable Adversarial Perturbations between Self-Supervised Speech Recognition Models
(
Poster
)
>
link
|
Raphaรซl Olivier ยท Hadi Abdullah ยท Bhiksha Raj
๐
|
-
|
Unsupervised Adversarial Detection without Extra Model: Training Loss Should Change
(
Poster
)
>
link
|
Chien Cheng Chyou ยท Hung-Ting Su ยท Winston Hsu
๐
|
-
|
Stabilizing GNN for Fairness via Lipschitz Bounds
(
Poster
)
>
link
|
Yaning Jia ยท Chunhui Zhang
๐
|
-
|
Equal Long-term Benefit Rate: Adapting Static Fairness Notions to Sequential Decision Making
(
Poster
)
>
link
|
Yuancheng Xu ยท Chenghao Deng ยท Yanchao Sun ยท Ruijie Zheng ยท xiyao wang ยท Jieyu Zhao ยท Furong Huang
๐
|
-
|
Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks
(
Poster
)
>
link
|
Daniel Kang ยท Xuechen Li ยท Ion Stoica ยท Carlos Guestrin ยท Matei Zaharia ยท Tatsunori Hashimoto
๐
|
-
|
Certifying Ensembles: A General Certification Theory with S-Lipschitzness
(
Poster
)
>
link
|
Aleksandar Petrov ยท Francisco Eiras ยท Amartya Sanyal ยท Phil Torr ยท Adel Bibi
๐
|
-
|
On the Limitations of Model Stealing with Uncertainty Quantification Models
(
Poster
)
>
link
|
David Pape ยท Sina Dรคubener ยท Thorsten Eisenhofer ยท Antonio Emanuele Cinร ยท Lea Schรถnherr
๐
|
-
|
PAC-Bayesian Adversarially Robust Generalization Bounds for Deep Neural Networks
(
Poster
)
>
link
|
Jiancong Xiao ยท Ruoyu Sun ยท Zhi-Quan Luo
๐
|
-
|
Sentiment Perception Adversarial Attacks on Neural Machine Translation Systems
(
Poster
)
>
link
|
Vyas Raina ยท Mark Gales
๐
|
-
|
(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy
(
Poster
)
>
link
|
Elan Rosenfeld ยท Saurabh Garg
๐
|
-
|
Feature Partition Aggregation: A Fast Certified Defense Against a Union of โ0 Attacks
(
Poster
)
>
link
|
Zayd S Hammoudeh ยท Daniel Lowd
๐
|
-
|
Near Optimal Adversarial Attack on UCB Bandits
(
Poster
)
>
link
|
Shiliang Zuo
๐
|
-
|
Learning Exponential Families from Truncated Samples
(
Poster
)
>
link
|
Jane Lee ยท Andre Wibisono ยท Manolis Zampetakis
๐
|
-
|
Identifying Adversarially Attackable and Robust Samples
(
Poster
)
>
link
|
Vyas Raina ยท Mark Gales
๐
|
-
|
Toward Testing Deep Learning Library via Model Fuzzing
(
Poster
)
>
link
|
Wei Kong ยท huayang cao ยท Tong Wang ยท Yuanping Nie ยท hu li ยท Xiaohui Kuang
๐
|
-
|
Adversarial Attacks and Defenses in Explainable Artificial Intelligence: A Survey
(
Poster
)
>
link
|
Hubert Baniecki ยท Przemyslaw Biecek
๐
|
-
|
Sharpness-Aware Minimization Alone can Improve Adversarial Robustness
(
Poster
)
>
link
|
Zeming Wei ยท Jingyu Zhu ยท Yihao Zhang
๐
|
-
|
On feasibility of intent obfuscating attacks
(
Poster
)
>
link
|
ZhaoBin Li ยท Patrick Shafto
๐
|
-
|
Adversarial Training with Generated Data in High-Dimensional Regression: An Asymptotic Study
(
Poster
)
>
link
|
Yue Xing
๐
|
-
|
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
(
Oral
)
>
link
|
๐
|
-
|
Establishing a Benchmark for Adversarial Robustness of Compressed Deep Learning Models after Pruning
(
Oral
)
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Robustness through Loss Consistency Regularization
(
Oral
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Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness
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Oral
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Introducing Vision into Large Language Models Expands Attack Surfaces and Failure Implications
(
Oral
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The Future of Cyber Systems: Human-AI Reinforcement Learning with Adversarial Robustness
(
Oral
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Provably Robust Cost-Sensitive Learning via Randomized Smoothing
(
Oral
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Like Oil and Water: Group Robustness and Poisoning Defenses Donโt Mix
(
Oral
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Provable Instance Specific Robustness via Linear Constraints
(
Oral
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Adversarial Training in Continuous-Time Models and Irregularly Sampled Time-Series: A First Look
(
Oral
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Few-shot Anomaly Detection via Personalization
(
Oral
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Rethinking Label Poisoning for GNNs: Pitfalls and Attacks
(
Oral
)
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Shrink & Cert: Bi-level Optimization for Certified Robustness
(
Oral
)
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Preventing Reward Hacking with Occupancy Measure Regularization
(
Oral
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Evading Black-box Classifiers Without Breaking Eggs
(
Oral
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Deceptive Alignment Monitoring
(
Oral
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Baselines for Identifying Watermarked Large Language Models
(
Oral
)
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Why do universal adversarial attacks work on large language models?: Geometry might be the answer
(
Oral
)
>
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FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation
(
Oral
)
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Robust Deep Learning via Layerwise Tilted Exponentials
(
Oral
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Learning Shared Safety Constraints from Multi-task Demonstrations
(
Oral
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Teach GPT To Phish
(
Oral
)
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How Can Neuroscience Help Us Build More Robust Deep Neural Networks?
(
Oral
)
>
link
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A physics-orientd method for attacking SAR images using salient regions
(
Oral
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>
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Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage
(
Oral
)
>
link
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Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks
(
Oral
)
>
link
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๐
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DiffScene: Diffusion-Based Safety-Critical Scenario Generation for Autonomous Vehicles
(
Oral
)
>
link
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Improving Adversarial Training for Multiple Perturbations through the Lens of Uniform Stability
(
Oral
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>
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A Theoretical Perspective on the Robustness of Feature Extractors
(
Oral
)
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Characterizing the Optimal 0โ1 Loss for Multi-class Classification with a Test-time Attacker
(
Oral
)
>
link
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RODEO: Robust Out-of-distribution Detection via Exposing Adaptive Outliers
(
Oral
)
>
link
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Rethinking Robust Contrastive Learning from the Adversarial Perspective
(
Oral
)
>
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TMI! Finetuned Models Spill Secrets from Pretraining
(
Oral
)
>
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A First Order Meta Stackelberg Method for Robust Federated Learning
(
Oral
)
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link
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Backdoor Attacks for In-Context Learning with Language Models
(
Oral
)
>
link
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R-LPIPS: An Adversarially Robust Perceptual Similarity Metric
(
Oral
)
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link
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๐
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Risk-Averse Predictions on Unseen Domains via Neural Style Smoothing
(
Oral
)
>
link
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๐
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A Simple and Yet Fairly Effective Defense for Graph Neural Networks
(
Oral
)
>
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Incentivizing Honesty among Competitors in Collaborative Learning
(
Oral
)
>
link
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Towards Effective Data Poisoning for Imbalanced Classification
(
Oral
)
>
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๐
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Black Box Adversarial Prompting for Foundation Models
(
Oral
)
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Exposing the Fake: Effective Diffusion-Generated Images Detection
(
Oral
)
>
link
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AdversNLP: A Practical Guide to Assessing NLP Robustness Against Text Adversarial Attacks
(
Oral
)
>
link
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๐
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Proximal Compositional Optimization for Distributionally Robust Learning
(
Oral
)
>
link
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๐
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PIAT: Parameter Interpolation based Adversarial Training for Image Classification
(
Oral
)
>
link
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Mathematical Theory of Adversarial Deep Learning
(
Oral
)
>
link
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Adapting Robust Reinforcement Learning to Handle Temporally-Coupled Perturbations
(
Oral
)
>
link
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๐
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Navigating Graph Robust Learning against All-Intensity Attacks
(
Oral
)
>
link
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๐
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Towards Out-of-Distribution Adversarial Robustness
(
Oral
)
>
link
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๐
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Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations
(
Oral
)
>
link
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๐
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Adversarial Robustness for Tabular Data through Cost and Utility Awareness
(
Oral
)
>
link
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๐
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Scoring Black-Box Models for Adversarial Robustness
(
Oral
)
>
link
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๐
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When Can Linear Learners be Robust to Indiscriminate Poisoning Attacks?
(
Oral
)
>
link
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๐
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Context-Aware Self-Adaptation for Domain Generalization
(
Oral
)
>
link
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Label Noise: Correcting a Correction Loss
(
Oral
)
>
link
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๐
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Robust Semantic Segmentation: Strong Adversarial Attacks and Fast Training of Robust Models
(
Oral
)
>
link
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๐
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Model-tuning Via Prompts Makes NLP Models Adversarially Robust
(
Oral
)
>
link
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๐
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Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
(
Oral
)
>
link
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๐
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Refined and Enriched Physics-based Captions For Unseen Dynamic Changes
(
Oral
)
>
link
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๐
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Adaptive Certified Training: Towards Better Accuracy-Robustness Tradeoffs
(
Oral
)
>
link
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๐
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Illusory Attacks: Detectability Matters in Adversarial Attacks on Sequential Decision-Makers
(
Oral
)
>
link
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๐
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Certified Calibration: Bounding Worst-Case Calibration under Adversarial Attacks
(
Oral
)
>
link
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๐
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Don't trust your eyes: on the (un)reliability of feature visualizations
(
Oral
)
>
link
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๐
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Classifier Robustness Enhancement Via Test-Time Transformation
(
Oral
)
>
link
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๐
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CertViT: Certified Robustness of Pre-Trained Vision Transformers
(
Oral
)
>
link
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๐
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Transferable Adversarial Perturbations between Self-Supervised Speech Recognition Models
(
Oral
)
>
link
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๐
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Tunable Dual-Objective GANs for Stable Training
(
Oral
)
>
link
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๐
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MLSMM: Machine Learning Security Maturity Model
(
Oral
)
>
link
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๐
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Adversarial Training Should Be Cast as a Non-Zero-Sum Game
(
Oral
)
>
link
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๐
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Unsupervised Adversarial Detection without Extra Model: Training Loss Should Change
(
Oral
)
>
link
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๐
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Stabilizing GNN for Fairness via Lipschitz Bounds
(
Oral
)
>
link
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๐
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Equal Long-term Benefit Rate: Adapting Static Fairness Notions to Sequential Decision Making
(
Oral
)
>
link
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๐
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Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks
(
Oral
)
>
link
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๐
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Certifying Ensembles: A General Certification Theory with S-Lipschitzness
(
Oral
)
>
link
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๐
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On the Limitations of Model Stealing with Uncertainty Quantification Models
(
Oral
)
>
link
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๐
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The Challenge of Differentially Private Screening Rules
(
Oral
)
>
link
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๐
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PAC-Bayesian Adversarially Robust Generalization Bounds for Deep Neural Networks
(
Oral
)
>
link
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๐
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Sentiment Perception Adversarial Attacks on Neural Machine Translation Systems
(
Oral
)
>
link
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๐
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(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy
(
Oral
)
>
link
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๐
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Feature Partition Aggregation: A Fast Certified Defense Against a Union of โ0 Attacks
(
Oral
)
>
link
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๐
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-
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Near Optimal Adversarial Attack on UCB Bandits
(
Oral
)
>
link
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๐
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-
|
Learning Exponential Families from Truncated Samples
(
Oral
)
>
link
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๐
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-
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Identifying Adversarially Attackable and Robust Samples
(
Oral
)
>
link
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๐
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-
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Toward Testing Deep Learning Library via Model Fuzzing
(
Oral
)
>
link
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๐
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-
|
Adversarial Attacks and Defenses in Explainable Artificial Intelligence: A Survey
(
Oral
)
>
link
|
๐
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-
|
Sharpness-Aware Minimization Alone can Improve Adversarial Robustness
(
Oral
)
>
link
|
๐
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-
|
On feasibility of intent obfuscating attacks
(
Oral
)
>
link
|
๐
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|
Adversarial Training with Generated Data in High-Dimensional Regression: An Asymptotic Study
(
Oral
)
>
link
|
๐
|