Fri 11:50 a.m. - 12:00 p.m.
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Opening
(
Opening
)
>
SlidesLive Video
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🔗
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Fri 12:00 p.m. - 12:30 p.m.
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Una-May O'Reilly
(
Keynote
)
>
SlidesLive Video
|
Una-May O'Reilly
🔗
|
Fri 12:30 p.m. - 1:00 p.m.
|
Lea Schönherr
(
Keynote
)
>
SlidesLive Video
|
Lea Schönherr
🔗
|
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 · Fabian Latorre · George J. Pappas · Hamed Hassani · Volkan Cevher
🔗
|
Fri 1:10 p.m. - 1:20 p.m.
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Evading Black-box Classifiers Without Breaking Eggs
(
Oral
)
>
link
SlidesLive Video
|
Edoardo Debenedetti · Nicholas Carlini · Florian Tramer
🔗
|
Fri 1:20 p.m. - 1:30 p.m.
|
Tunable Dual-Objective GANs for Stable Training
(
Oral
)
>
link
SlidesLive Video
|
Monica Welfert · Kyle Otstot · Gowtham Kurri · Lalitha Sankar
🔗
|
Fri 1:30 p.m. - 2:00 p.m.
|
Jihun Hamm
(
Keynote
)
>
SlidesLive Video
|
Jihun Hamm
🔗
|
Fri 2:00 p.m. - 2:30 p.m.
|
Kamalika Chaudhuri
(
Keynote
)
>
SlidesLive Video
|
Kamalika Chaudhuri
🔗
|
Fri 2:30 p.m. - 4:00 p.m.
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Posters
(
Posters
)
>
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🔗
|
Fri 4:00 p.m. - 4:30 p.m.
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Atlas Wang
(
Keynote
)
>
SlidesLive Video
|
Zhangyang “Atlas” Wang
🔗
|
Fri 4:30 p.m. - 5:00 p.m.
|
Stacy Hobson
(
Keynote
)
>
SlidesLive Video
|
Stacy Fay Hobson
🔗
|
Fri 5:00 p.m. - 5:10 p.m.
|
Visual Adversarial Examples Jailbreak Aligned Large Language Models
(
Oral
)
>
link
SlidesLive Video
|
Xiangyu Qi · Kaixuan Huang · Ashwinee Panda · Mengdi Wang · Prateek Mittal
🔗
|
Fri 5:10 p.m. - 5:20 p.m.
|
Learning Shared Safety Constraints from Multi-task Demonstrations
(
Oral
)
>
link
SlidesLive Video
|
Konwoo Kim · Gokul Swamy · Zuxin Liu · Ding Zhao · Sanjiban Choudhury · Steven Wu
🔗
|
Fri 5:20 p.m. - 5:25 p.m.
|
MLSMM: Machine Learning Security Maturity Model
(
Bluesky Oral
)
>
link
SlidesLive Video
|
Felix Jedrzejewski · Davide Fucci · Oleksandr Adamov
🔗
|
Fri 5:25 p.m. - 5:30 p.m.
|
Deceptive Alignment Monitoring
(
Bluesky Oral
)
>
link
SlidesLive Video
|
Andres Carranza · Dhruv Pai · Rylan Schaeffer · Arnuv Tandon · Sanmi Koyejo
🔗
|
Fri 5:30 p.m. - 6:00 p.m.
|
Aditi Raghunathan
(
Keynote
)
>
SlidesLive Video
|
Aditi Raghunathan
🔗
|
Fri 6:00 p.m. - 6:30 p.m.
|
Zico Kolter
(
Keynote
)
>
SlidesLive Video
|
Zico Kolter
🔗
|
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 · Siddharth Mansingh · Jocelyn Rego · Garrett T Kenyon · Juston Moore · Michael Teti
🔗
|
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
🔗
|
Fri 6:40 p.m. - 6:45 p.m.
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Announcement of AdvML Rising Star Award
(
Announcement
)
>
SlidesLive Video
|
🔗
|
Fri 6:45 p.m. - 7:00 p.m.
|
Tianlong Chen
(
Award presentation
)
>
SlidesLive Video
|
🔗
|
Fri 7:00 p.m. - 7:15 p.m.
|
Vikash Sehwag
(
Award presentation
)
>
SlidesLive Video
|
🔗
|
Fri 7:15 p.m. - 8:00 p.m.
|
Posters
(
Posters
)
>
|
🔗
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Fri 8:00 p.m. - 8:00 p.m.
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Closing
(
Closing
)
>
|
🔗
|
-
|
The Challenge of Differentially Private Screening Rules
(
Poster
)
>
link
|
Amol Khanna · Fred Lu · Edward Raff
🔗
|
-
|
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
(
Poster
)
>
link
|
Zhihang Yuan · Jiawei Liu · Jiaxiang Wu · Dawei Yang · Qiang Wu · Guangyu Sun · Wenyu Liu · Xinggang Wang · Bingzhe Wu
🔗
|
-
|
Benchmarking Adversarial Robustness of Compressed Deep Learning Models
(
Poster
)
>
link
|
Brijesh Vora · Kartik Patwari · Syed Mahbub Hafiz · Zubair Shafiq · Chen-Nee Chuah
🔗
|
-
|
Robustness through Data Augmentation Loss Consistency
(
Poster
)
>
link
|
Tianjian Huang · Shaunak Halbe · Chinnadhurai Sankar · Pooyan Amini · Satwik Kottur · Alborz Geramifard · Meisam Razaviyayn · Ahmad Beirami
🔗
|
-
|
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness
(
Poster
)
>
link
|
Francesco Campi · Lukas Gosch · Tom Wollschläger · Yan Scholten · Stephan Günnemann
🔗
|
-
|
Provably Robust Cost-Sensitive Learning via Randomized Smoothing
(
Poster
)
>
link
|
Yuan Xin · Michael Backes · Xiao Zhang
🔗
|
-
|
Like Oil and Water: Group Robustness and Poisoning Defenses Don’t Mix
(
Poster
)
>
link
|
Michael-Andrei Panaitescu-Liess · Yigitcan Kaya · Tudor Dumitras
🔗
|
-
|
Provable Instance Specific Robustness via Linear Constraints
(
Poster
)
>
link
|
Ahmed Imtiaz Humayun · Josue Casco-Rodriguez · Randall Balestriero · Richard Baraniuk
🔗
|
-
|
Adversarial Training in Continuous-Time Models and Irregularly Sampled Time-Series
(
Poster
)
>
link
|
Alvin Li · Mathias Lechner · Alexander Amini · Daniela Rus
🔗
|
-
|
Few-shot Anomaly Detection via Personalization
(
Poster
)
>
link
|
Sangkyung Kwak · Jongheon Jeong · Hankook Lee · Woohyuck Kim · Jinwoo Shin
🔗
|
-
|
Rethinking Label Poisoning for GNNs: Pitfalls and Attacks
(
Poster
)
>
link
|
Vijay Lingam · Mohammad Sadegh Akhondzadeh · Aleksandar Bojchevski
🔗
|
-
|
Shrink & Cert: Bi-level Optimization for Certified Robustness
(
Poster
)
>
link
|
Kavya Gupta · Sagar Verma
🔗
|
-
|
Preventing Reward Hacking with Occupancy Measure Regularization
(
Poster
)
>
link
|
Cassidy Laidlaw · Shivam Singhal · Anca Dragan
🔗
|
-
|
Baselines for Identifying Watermarked Large Language Models
(
Poster
)
>
link
|
Leonard Tang · Gavin Uberti · Tom Shlomi
🔗
|
-
|
Why do universal adversarial attacks work on large language models?: Geometry might be the answer
(
Poster
)
>
link
|
Varshini Subhash · Anna Bialas · Siddharth Swaroop · Weiwei Pan · Finale Doshi-Velez
🔗
|
-
|
FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation
(
Poster
)
>
link
|
Dhruv Pai · Andres Carranza · Rylan Schaeffer · Arnuv Tandon · Sanmi Koyejo
🔗
|
-
|
Robust Deep Learning via Layerwise Tilted Exponentials
(
Poster
)
>
link
|
Bhagyashree Puranik · Ahmad Beirami · Yao Qin · Upamanyu Madhow
🔗
|
-
|
Teach GPT To Phish
(
Poster
)
>
link
|
Ashwinee Panda · Zhengming Zhang · Yaoqing Yang · Prateek Mittal
🔗
|
-
|
Physics-oriented adversarial attacks on SAR image target recognition
(
Poster
)
>
link
|
Jiahao Cui · wang Guo · Run Shao · tiandong Shi · Haifeng Li
🔗
|
-
|
Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage
(
Poster
)
>
link
|
Catherine Huang · Chelse Swoopes · Christina Xiao · Jiaqi Ma · Himabindu Lakkaraju
🔗
|
-
|
Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks
(
Poster
)
>
link
|
Ashish Hooda · Neal Mangaokar · Ryan Feng · Kassem Fawaz · Somesh Jha · Atul Prakash
🔗
|
-
|
DiffScene: Diffusion-Based Safety-Critical Scenario Generation for Autonomous Vehicles
(
Poster
)
>
link
|
Chejian Xu · Ding Zhao · Alberto Sngiovanni Vincentelli · Bo Li
🔗
|
-
|
Improving Adversarial Training for Multiple Perturbations through the Lens of Uniform Stability
(
Poster
)
>
link
|
Jiancong Xiao · Zeyu Qin · Yanbo Fan · Baoyuan Wu · Jue Wang · Zhi-Quan Luo
🔗
|
-
|
A Theoretical Perspective on the Robustness of Feature Extractors
(
Poster
)
>
link
|
Arjun Nitin Bhagoji · Daniel Cullina · Ben Zhao
🔗
|
-
|
Characterizing the Optimal $0-1$ Loss for Multi-class Classification with a Test-time Attacker
(
Poster
)
>
link
|
Sophie Dai · Wenxin Ding · Arjun Nitin Bhagoji · Daniel Cullina · Ben Zhao · Heather Zheng · Prateek Mittal
🔗
|
-
|
RODEO: Robust Out-of-distribution Detection via Exposing Adaptive Outliers
(
Poster
)
>
link
|
Hossein Mirzaei · Mohammad Jafari · Hamid Reza Dehbashi · Ali Ansari · Sepehr Ghobadi · Masoud Hadi · Arshia Soltani Moakhar · Mohammad Azizmalayeri · Mahdieh Soleymani Baghshah · Mohammad H Rohban
🔗
|
-
|
Rethinking Robust Contrastive Learning from the Adversarial Perspective
(
Poster
)
>
link
|
Fatemeh Ghofrani · Mehdi Yaghouti · Pooyan Jamshidi
🔗
|
-
|
TMI! Finetuned Models Spill Secrets from Pretraining
(
Poster
)
>
link
|
John Abascal · Stanley Wu · Alina Oprea · Jonathan Ullman
🔗
|
-
|
A First Order Meta Stackelberg Method for Robust Federated Learning
(
Poster
)
>
link
|
Yunian Pan · Tao Li · Henger Li · Tianyi Xu · Quanyan Zhu · Zizhan Zheng
🔗
|
-
|
Backdoor Attacks for In-Context Learning with Language Models
(
Poster
)
>
link
|
Nikhil Kandpal · Matthew Jagielski · Florian Tramer · Nicholas Carlini
🔗
|
-
|
R-LPIPS: An Adversarially Robust Perceptual Similarity Metric
(
Poster
)
>
link
|
Sara Ghazanfari · Siddharth Garg · Prashanth Krishnamurthy · Farshad Khorrami · Alexandre Araujo
🔗
|
-
|
Risk-Averse Predictions on Unseen Domains via Neural Style Smoothing
(
Poster
)
>
link
|
Akshay Mehra · Yunbei Zhang · Bhavya Kailkhura · Jihun Hamm
🔗
|
-
|
A Simple and Yet Fairly Effective Defense for Graph Neural Networks
(
Poster
)
>
link
|
Sofiane ENNADIR · Yassine Abbahaddou · Michalis Vazirgiannis · Henrik Boström
🔗
|
-
|
Incentivizing Honesty among Competitors in Collaborative Learning
(
Poster
)
>
link
|
Florian Dorner · Nikola Konstantinov · Georgi Pashaliev · Martin Vechev
🔗
|
-
|
Towards Effective Data Poisoning for Imbalanced Classification
(
Poster
)
>
link
|
Snigdha Sushil Mishra · Hao He · Hao Wang
🔗
|
-
|
Black Box Adversarial Prompting for Foundation Models
(
Poster
)
>
link
|
Natalie Maus · Patrick Chao · Eric Wong · Jacob Gardner
🔗
|
-
|
Exposing the Fake: Effective Diffusion-Generated Images Detection
(
Poster
)
>
link
|
RuiPeng Ma · Jinhao Duan · Fei Kong · Xiaoshuang Shi · Kaidi Xu
🔗
|
-
|
AdversNLP: A Practical Guide to Assessing NLP Robustness Against Text Adversarial Attacks
(
Poster
)
>
link
|
Othmane BELMOUKADAM
🔗
|
-
|
Proximal Compositional Optimization for Distributionally Robust Learning
(
Poster
)
>
link
|
Prashant Khanduri · Chengyin Li · RAFI IBN SULTAN · Yao Qiang · Joerg Kliewer · Dongxiao Zhu
🔗
|
-
|
PIAT: Parameter Interpolation based Adversarial Training for Image Classification
(
Poster
)
>
link
|
Kun He · Xin Liu · Yichen Yang · Zhou Qin · Weigao Wen · Hui Xue' · John Hopcroft
🔗
|
-
|
Mathematical Theory of Adversarial Deep Learning
(
Poster
)
>
link
|
Xiao-Shan Gao · Lijia Yu · Shuang Liu
🔗
|
-
|
Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations
(
Poster
)
>
link
|
Yongyuan Liang · Yanchao Sun · Ruijie Zheng · Xiangyu Liu · Tuomas Sandholm · Furong Huang · Stephen Mcaleer
🔗
|
-
|
Navigating Graph Robust Learning against All-Intensity Attacks
(
Poster
)
>
link
|
Xiangchi Yuan · Chunhui Zhang · Yijun Tian · Chuxu Zhang
🔗
|
-
|
Towards Out-of-Distribution Adversarial Robustness
(
Poster
)
>
link
|
Adam Ibrahim · Charles Guille-Escuret · Ioannis Mitliagkas · Irina Rish · David Krueger · Pouya Bashivan
🔗
|
-
|
Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations
(
Poster
)
>
link
|
Hyeonjeong Ha · Minseon Kim · Sung Ju Hwang
🔗
|
-
|
Adversarial Robustness for Tabular Data through Cost and Utility Awareness
(
Poster
)
>
link
|
Klim Kireev · Bogdan Kulynych · Carmela Troncoso
🔗
|
-
|
Scoring Black-Box Models for Adversarial Robustness
(
Poster
)
>
link
|
Jian Vora · Pranay Reddy Samala
🔗
|
-
|
When Can Linear Learners be Robust to Indiscriminate Poisoning Attacks?
(
Poster
)
>
link
|
Fnu Suya · Xiao Zhang · Yuan Tian · David Evans
🔗
|
-
|
Context-Aware Self-Adaptation for Domain Generalization
(
Poster
)
>
link
|
Hao Yan · Yuhong Guo
🔗
|
-
|
Label Noise: Correcting a Correction Loss
(
Poster
)
>
link
|
William Toner · Amos Storkey
🔗
|
-
|
Robust Semantic Segmentation: Strong Adversarial Attacks and Fast Training of Robust Models
(
Poster
)
>
link
|
Francesco Croce · Naman Singh · Matthias Hein
🔗
|
-
|
Model-tuning Via Prompts Makes NLP Models Adversarially Robust
(
Poster
)
>
link
|
Mrigank Raman · Pratyush Maini · Zico Kolter · Zachary Lipton · Danish Pruthi
🔗
|
-
|
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
(
Poster
)
>
link
|
Suraj Srinivas · Sebastian Bordt · Himabindu Lakkaraju
🔗
|
-
|
Refined and Enriched Physics-based Captions for Unseen Dynamic Changes
(
Poster
)
>
link
|
Hidetomo Sakaino
🔗
|
-
|
Adaptive Certified Training: Towards Better Accuracy-Robustness Tradeoffs
(
Poster
)
>
link
|
Zhakshylyk Nurlanov · Frank R Schmidt · Florian Bernard
🔗
|
-
|
Illusory Attacks: Detectability Matters in Adversarial Attacks on Sequential Decision-Makers
(
Poster
)
>
link
|
Tim Franzmeyer · Stephen Mcaleer · Joao Henriques · Jakob Foerster · Phil Torr · Adel Bibi · Christian Schroeder
🔗
|
-
|
Certified Calibration: Bounding Worst-Case Calibration under Adversarial Attacks
(
Poster
)
>
link
|
Cornelius Emde · Francesco Pinto · Thomas Lukasiewicz · Phil Torr · Adel Bibi
🔗
|
-
|
Don't trust your eyes: on the (un)reliability of feature visualizations
(
Poster
)
>
link
|
Robert Geirhos · Roland S. Zimmermann · Blair Bilodeau · Wieland Brendel · Been Kim
🔗
|
-
|
Classifier Robustness Enhancement Via Test-Time Transformation
(
Poster
)
>
link
|
Tsachi Blau · Roy Ganz · Chaim Baskin · Michael Elad · Alex Bronstein
🔗
|
-
|
CertViT: Certified Robustness of Pre-Trained Vision Transformers
(
Poster
)
>
link
|
Kavya Gupta · Sagar Verma
🔗
|
-
|
Transferable Adversarial Perturbations between Self-Supervised Speech Recognition Models
(
Poster
)
>
link
|
Raphaël Olivier · Hadi Abdullah · Bhiksha Raj
🔗
|
-
|
Unsupervised Adversarial Detection without Extra Model: Training Loss Should Change
(
Poster
)
>
link
|
Chien Cheng Chyou · Hung-Ting Su · Winston Hsu
🔗
|
-
|
Stabilizing GNN for Fairness via Lipschitz Bounds
(
Poster
)
>
link
|
Yaning Jia · Chunhui Zhang
🔗
|
-
|
Equal Long-term Benefit Rate: Adapting Static Fairness Notions to Sequential Decision Making
(
Poster
)
>
link
|
Yuancheng Xu · Chenghao Deng · Yanchao Sun · Ruijie Zheng · xiyao wang · Jieyu Zhao · Furong Huang
🔗
|
-
|
Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks
(
Poster
)
>
link
|
Daniel Kang · Xuechen Li · Ion Stoica · Carlos Guestrin · Matei Zaharia · Tatsunori Hashimoto
🔗
|
-
|
Certifying Ensembles: A General Certification Theory with S-Lipschitzness
(
Poster
)
>
link
|
Aleksandar Petrov · Francisco Eiras · Amartya Sanyal · Phil Torr · Adel Bibi
🔗
|
-
|
On the Limitations of Model Stealing with Uncertainty Quantification Models
(
Poster
)
>
link
|
David Pape · Sina Däubener · Thorsten Eisenhofer · Antonio Emanuele Cinà · Lea Schönherr
🔗
|
-
|
PAC-Bayesian Adversarially Robust Generalization Bounds for Deep Neural Networks
(
Poster
)
>
link
|
Jiancong Xiao · Ruoyu Sun · Zhi-Quan Luo
🔗
|
-
|
Sentiment Perception Adversarial Attacks on Neural Machine Translation Systems
(
Poster
)
>
link
|
Vyas Raina · Mark Gales
🔗
|
-
|
(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy
(
Poster
)
>
link
|
Elan Rosenfeld · Saurabh Garg
🔗
|
-
|
Feature Partition Aggregation: A Fast Certified Defense Against a Union of $\ell_0$ Attacks
(
Poster
)
>
link
|
Zayd S Hammoudeh · Daniel Lowd
🔗
|
-
|
Near Optimal Adversarial Attack on UCB Bandits
(
Poster
)
>
link
|
Shiliang Zuo
🔗
|
-
|
Learning Exponential Families from Truncated Samples
(
Poster
)
>
link
|
Jane Lee · Andre Wibisono · Manolis Zampetakis
🔗
|
-
|
Identifying Adversarially Attackable and Robust Samples
(
Poster
)
>
link
|
Vyas Raina · Mark Gales
🔗
|
-
|
Toward Testing Deep Learning Library via Model Fuzzing
(
Poster
)
>
link
|
Wei Kong · huayang cao · Tong Wang · Yuanping Nie · hu li · Xiaohui Kuang
🔗
|
-
|
Adversarial Attacks and Defenses in Explainable Artificial Intelligence: A Survey
(
Poster
)
>
link
|
Hubert Baniecki · Przemyslaw Biecek
🔗
|
-
|
Sharpness-Aware Minimization Alone can Improve Adversarial Robustness
(
Poster
)
>
link
|
Zeming Wei · Jingyu Zhu · Yihao Zhang
🔗
|
-
|
On feasibility of intent obfuscating attacks
(
Poster
)
>
link
|
ZhaoBin Li · Patrick Shafto
🔗
|
-
|
Adversarial Training with Generated Data in High-Dimensional Regression: An Asymptotic Study
(
Poster
)
>
link
|
Yue Xing
🔗
|
-
|
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
(
Oral
)
>
link
|
🔗
|
-
|
Establishing a Benchmark for Adversarial Robustness of Compressed Deep Learning Models after Pruning
(
Oral
)
>
link
|
🔗
|
-
|
Robustness through Loss Consistency Regularization
(
Oral
)
>
link
|
🔗
|
-
|
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness
(
Oral
)
>
link
|
🔗
|
-
|
Introducing Vision into Large Language Models Expands Attack Surfaces and Failure Implications
(
Oral
)
>
link
|
🔗
|
-
|
The Future of Cyber Systems: Human-AI Reinforcement Learning with Adversarial Robustness
(
Oral
)
>
link
|
🔗
|
-
|
Provably Robust Cost-Sensitive Learning via Randomized Smoothing
(
Oral
)
>
link
|
🔗
|
-
|
Like Oil and Water: Group Robustness and Poisoning Defenses Don’t Mix
(
Oral
)
>
link
|
🔗
|
-
|
Provable Instance Specific Robustness via Linear Constraints
(
Oral
)
>
link
|
🔗
|
-
|
Adversarial Training in Continuous-Time Models and Irregularly Sampled Time-Series: A First Look
(
Oral
)
>
link
|
🔗
|
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Few-shot Anomaly Detection via Personalization
(
Oral
)
>
link
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Rethinking Label Poisoning for GNNs: Pitfalls and Attacks
(
Oral
)
>
link
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Shrink & Cert: Bi-level Optimization for Certified Robustness
(
Oral
)
>
link
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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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>
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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
)
>
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A physics-orientd method for attacking SAR images using salient regions
(
Oral
)
>
link
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Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage
(
Oral
)
>
link
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🔗
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Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks
(
Oral
)
>
link
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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
)
>
link
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A Theoretical Perspective on the Robustness of Feature Extractors
(
Oral
)
>
link
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Characterizing the Optimal $0-1$ 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
)
>
link
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🔗
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TMI! Finetuned Models Spill Secrets from Pretraining
(
Oral
)
>
link
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A First Order Meta Stackelberg Method for Robust Federated Learning
(
Oral
)
>
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
)
>
link
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Risk-Averse Predictions on Unseen Domains via Neural Style Smoothing
(
Oral
)
>
link
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🔗
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A Simple and Yet Fairly Effective Defense for Graph Neural Networks
(
Oral
)
>
link
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🔗
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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
)
>
link
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🔗
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Black Box Adversarial Prompting for Foundation Models
(
Oral
)
>
link
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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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🔗
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Proximal Compositional Optimization for Distributionally Robust Learning
(
Oral
)
>
link
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PIAT: Parameter Interpolation based Adversarial Training for Image Classification
(
Oral
)
>
link
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🔗
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Mathematical Theory of Adversarial Deep Learning
(
Oral
)
>
link
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🔗
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Adapting Robust Reinforcement Learning to Handle Temporally-Coupled Perturbations
(
Oral
)
>
link
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🔗
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Navigating Graph Robust Learning against All-Intensity Attacks
(
Oral
)
>
link
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🔗
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Towards Out-of-Distribution Adversarial Robustness
(
Oral
)
>
link
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🔗
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Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations
(
Oral
)
>
link
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🔗
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Adversarial Robustness for Tabular Data through Cost and Utility Awareness
(
Oral
)
>
link
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🔗
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Scoring Black-Box Models for Adversarial Robustness
(
Oral
)
>
link
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🔗
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When Can Linear Learners be Robust to Indiscriminate Poisoning Attacks?
(
Oral
)
>
link
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🔗
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Context-Aware Self-Adaptation for Domain Generalization
(
Oral
)
>
link
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🔗
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Label Noise: Correcting a Correction Loss
(
Oral
)
>
link
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🔗
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Robust Semantic Segmentation: Strong Adversarial Attacks and Fast Training of Robust Models
(
Oral
)
>
link
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🔗
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Model-tuning Via Prompts Makes NLP Models Adversarially Robust
(
Oral
)
>
link
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🔗
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Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
(
Oral
)
>
link
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🔗
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Refined and Enriched Physics-based Captions For Unseen Dynamic Changes
(
Oral
)
>
link
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🔗
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Adaptive Certified Training: Towards Better Accuracy-Robustness Tradeoffs
(
Oral
)
>
link
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🔗
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Illusory Attacks: Detectability Matters in Adversarial Attacks on Sequential Decision-Makers
(
Oral
)
>
link
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🔗
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Certified Calibration: Bounding Worst-Case Calibration under Adversarial Attacks
(
Oral
)
>
link
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🔗
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Don't trust your eyes: on the (un)reliability of feature visualizations
(
Oral
)
>
link
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🔗
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Classifier Robustness Enhancement Via Test-Time Transformation
(
Oral
)
>
link
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🔗
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CertViT: Certified Robustness of Pre-Trained Vision Transformers
(
Oral
)
>
link
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🔗
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Transferable Adversarial Perturbations between Self-Supervised Speech Recognition Models
(
Oral
)
>
link
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🔗
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Tunable Dual-Objective GANs for Stable Training
(
Oral
)
>
link
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🔗
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MLSMM: Machine Learning Security Maturity Model
(
Oral
)
>
link
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🔗
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Adversarial Training Should Be Cast as a Non-Zero-Sum Game
(
Oral
)
>
link
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🔗
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Unsupervised Adversarial Detection without Extra Model: Training Loss Should Change
(
Oral
)
>
link
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🔗
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Stabilizing GNN for Fairness via Lipschitz Bounds
(
Oral
)
>
link
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🔗
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Equal Long-term Benefit Rate: Adapting Static Fairness Notions to Sequential Decision Making
(
Oral
)
>
link
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🔗
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Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks
(
Oral
)
>
link
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🔗
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-
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Certifying Ensembles: A General Certification Theory with S-Lipschitzness
(
Oral
)
>
link
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🔗
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-
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On the Limitations of Model Stealing with Uncertainty Quantification Models
(
Oral
)
>
link
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🔗
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-
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The Challenge of Differentially Private Screening Rules
(
Oral
)
>
link
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🔗
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-
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PAC-Bayesian Adversarially Robust Generalization Bounds for Deep Neural Networks
(
Oral
)
>
link
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🔗
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-
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Sentiment Perception Adversarial Attacks on Neural Machine Translation Systems
(
Oral
)
>
link
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🔗
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-
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(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy
(
Oral
)
>
link
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🔗
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-
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Feature Partition Aggregation: A Fast Certified Defense Against a Union of $\ell_0$ Attacks
(
Oral
)
>
link
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🔗
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-
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Near Optimal Adversarial Attack on UCB Bandits
(
Oral
)
>
link
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🔗
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-
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Learning Exponential Families from Truncated Samples
(
Oral
)
>
link
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🔗
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-
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Identifying Adversarially Attackable and Robust Samples
(
Oral
)
>
link
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🔗
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-
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Toward Testing Deep Learning Library via Model Fuzzing
(
Oral
)
>
link
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🔗
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-
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Adversarial Attacks and Defenses in Explainable Artificial Intelligence: A Survey
(
Oral
)
>
link
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🔗
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-
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Sharpness-Aware Minimization Alone can Improve Adversarial Robustness
(
Oral
)
>
link
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🔗
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-
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On feasibility of intent obfuscating attacks
(
Oral
)
>
link
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🔗
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-
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Adversarial Training with Generated Data in High-Dimensional Regression: An Asymptotic Study
(
Oral
)
>
link
|
🔗
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