Author Information

Bo Li (UIUC)

Dr. Bo Li is an assistant professor in the Department of Computer Science at the University of Illinois at Urbana–Champaign. She is the recipient of the IJCAI Computers and Thought Award, Alfred P. Sloan Research Fellowship, AI’s 10 to Watch, NSF CAREER Award, MIT Technology Review TR-35 Award, Dean's Award for Excellence in Research, C.W. Gear Outstanding Junior Faculty Award, Intel Rising Star award, Symantec Research Labs Fellowship, Rising Star Award, Research Awards from Tech companies such as Amazon, Facebook, Intel, IBM, and eBay, and best paper awards at several top machine learning and security conferences. Her research focuses on both theoretical and practical aspects of trustworthy machine learning, which is at the intersection of machine learning, security, privacy, and game theory. She has designed several scalable frameworks for trustworthy machine learning and privacy-preserving data publishing. Her work has been featured by major publications and media outlets such as Nature, Wired, Fortune, and New York Times.

Nicholas Carlini (Google)

Andrzej Banburski (MIT)

Kamalika Chaudhuri (University of California at San Diego)

Will Xiao (Harvard University)

Cihang Xie (Johns Hopkins University)

More from the Same Authors

• 2021 : Understanding Instance-based Interpretability of Variational Auto-Encoders »
  · Zhifeng Kong · Kamalika Chaudhuri
• 2021 : Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples »
  Maura Pintor · Luca Demetrio · Angelo Sotgiu · Giovanni Manca · Ambra Demontis · Nicholas Carlini · Battista Biggio · Fabio Roli
• 2021 : Privacy Amplification by Bernoulli Sampling »
  Jacob Imola · Kamalika Chaudhuri
• 2021 : A Shuffling Framework For Local Differential Privacy »
  Casey M Meehan · Amrita Roy Chowdhury · Kamalika Chaudhuri · Somesh Jha
• 2021 : Privacy Amplification by Subsampling in Time Domain »
  Tatsuki Koga · Casey M Meehan · Kamalika Chaudhuri
• 2022 : Understanding Rare Spurious Correlations in Neural Networks »
  Yao-Yuan Yang · Chi-Ning Chou · Kamalika Chaudhuri
• 2022 : Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables »
  Mengdi Xu · Peide Huang · Visak Kumar · Jielin Qiu · Chao Fang · Kuan-Hui Lee · Xuewei Qi · Henry Lam · Bo Li · Ding Zhao
• 2022 : Paper 10: CausalAF: Causal Autoregressive Flow for Safety-Critical Scenes Generation »
  Wenhao Ding · Haohong Lin · Bo Li · Ding Zhao · Hitesh Arora
• 2023 : DiffScene: Diffusion-Based Safety-Critical Scenario Generation for Autonomous Vehicles »
  Chejian Xu · Ding Zhao · Alberto Sngiovanni Vincentelli · Bo Li
• 2023 : Backdoor Attacks for In-Context Learning with Language Models »
  Nikhil Kandpal · Matthew Jagielski · Florian Tramer · Nicholas Carlini
• 2023 : Machine Learning with Feature Differential Privacy »
  Saeed Mahloujifar · Chuan Guo · G. Edward Suh · Kamalika Chaudhuri
• 2023 : Semantically Adversarial Scene Generation with Explicit Knowledge Guidance for Autonomous Driving »
  Wenhao Ding · Haohong Lin · Bo Li · Ding Zhao
• 2023 : Can Public Large Language Models Help Private Cross-device Federated Learning? »
  Boxin Wang · Yibo J. Zhang · Yuan Cao · Bo Li · Hugh B McMahan · Sewoong Oh · Zheng Xu · Manzil Zaheer
• 2023 : Can Public Large Language Models Help Private Cross-device Federated Learning? »
  Boxin Wang · Yibo J. Zhang · Yuan Cao · Bo Li · Hugh B McMahan · Sewoong Oh · Zheng Xu · Manzil Zaheer
• 2023 : Visual-based Policy Learning with Latent Language Encoding »
  Jielin Qiu · Mengdi Xu · William Han · Bo Li · Ding Zhao
• 2023 : Can Brain Signals Reveal Inner Alignment with Human Languages? »
  Jielin Qiu · William Han · Jiacheng Zhu · Mengdi Xu · Douglas Weber · Bo Li · Ding Zhao
• 2023 : Panel Discussion »
  Peter Kairouz · Song Han · Kamalika Chaudhuri · Florian Tramer
• 2023 : Kamalika Chaudhuri »
  Kamalika Chaudhuri
• 2023 : Evading Black-box Classifiers Without Breaking Eggs »
  Edoardo Debenedetti · Nicholas Carlini · Florian Tramer
• 2023 Workshop: Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities »
  Zheng Xu · Peter Kairouz · Bo Li · Tian Li · John Nguyen · Jianyu Wang · Shiqiang Wang · Ayfer Ozgur
• 2023 Workshop: Knowledge and Logical Reasoning in the Era of Data-driven Learning »
  Nezihe Merve Gürel · Bo Li · Theodoros Rekatsinas · Beliz Gunel · Alberto Sngiovanni Vincentelli · Paroma Varma
• 2023 Poster: Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design »
  Chuan Guo · Kamalika Chaudhuri · Pierre Stock · Michael Rabbat
• 2023 Poster: UMD: Unsupervised Model Detection for X2X Backdoor Attacks »
  Zhen Xiang · Zidi Xiong · Bo Li
• 2023 Poster: Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics »
  Jiacheng Zhu · Jielin Qiu · Aritra Guha · Zhuolin Yang · XuanLong Nguyen · Bo Li · Ding Zhao
• 2023 Oral: Why does Throwing Away Data Improve Worst-Group Error? »
  Kamalika Chaudhuri · Kartik Ahuja · Martin Arjovsky · David Lopez-Paz
• 2023 Poster: Data-Copying in Generative Models: A Formal Framework »
  Robi Bhattacharjee · Sanjoy Dasgupta · Kamalika Chaudhuri
• 2023 Poster: A Two-Stage Active Learning Algorithm for k-Nearest Neighbors »
  Nicholas Rittler · Kamalika Chaudhuri
• 2023 Poster: Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems »
  Chawin Sitawarin · Florian Tramer · Nicholas Carlini
• 2023 Poster: Why does Throwing Away Data Improve Worst-Group Error? »
  Kamalika Chaudhuri · Kartik Ahuja · Martin Arjovsky · David Lopez-Paz
• 2023 Poster: Reconstructive Neuron Pruning for Backdoor Defense »
  Yige Li · XIXIANG LYU · Xingjun Ma · Nodens Koren · Lingjuan Lyu · Bo Li · Yu-Gang Jiang
• 2022 : Paper 15: On the Robustness of Safe Reinforcement Learning under Observational Perturbations »
  Zuxin Liu · Zhepeng Cen · Huan Zhang · Jie Tan · Bo Li · Ding Zhao
• 2022 Poster: Constrained Variational Policy Optimization for Safe Reinforcement Learning »
  Zuxin Liu · Zhepeng Cen · Vladislav Isenbaev · Wei Liu · Steven Wu · Bo Li · Ding Zhao
• 2022 Poster: Provable Domain Generalization via Invariant-Feature Subspace Recovery »
  Haoxiang Wang · Haozhe Si · Bo Li · Han Zhao
• 2022 Spotlight: Constrained Variational Policy Optimization for Safe Reinforcement Learning »
  Zuxin Liu · Zhepeng Cen · Vladislav Isenbaev · Wei Liu · Steven Wu · Bo Li · Ding Zhao
• 2022 Spotlight: Provable Domain Generalization via Invariant-Feature Subspace Recovery »
  Haoxiang Wang · Haozhe Si · Bo Li · Han Zhao
• 2022 Poster: Thompson Sampling for Robust Transfer in Multi-Task Bandits »
  Zhi Wang · Chicheng Zhang · Kamalika Chaudhuri
• 2022 Poster: How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection »
  Mantas Mazeika · Bo Li · David Forsyth
• 2022 Poster: Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization »
  Xiaojun Xu · Yibo Zhang · Evelyn Ma · Hyun Ho Son · Sanmi Koyejo · Bo Li
• 2022 Poster: Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond »
  Haoxiang Wang · Bo Li · Han Zhao
• 2022 Spotlight: How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection »
  Mantas Mazeika · Bo Li · David Forsyth
• 2022 Spotlight: Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization »
  Xiaojun Xu · Yibo Zhang · Evelyn Ma · Hyun Ho Son · Sanmi Koyejo · Bo Li
• 2022 Spotlight: Thompson Sampling for Robust Transfer in Multi-Task Bandits »
  Zhi Wang · Chicheng Zhang · Kamalika Chaudhuri
• 2022 Spotlight: Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond »
  Haoxiang Wang · Bo Li · Han Zhao
• 2022 Poster: Bounding Training Data Reconstruction in Private (Deep) Learning »
  Chuan Guo · Brian Karrer · Kamalika Chaudhuri · Laurens van der Maaten
• 2022 Poster: Certifying Out-of-Domain Generalization for Blackbox Functions »
  Maurice Weber · Linyi Li · Boxin Wang · Zhikuan Zhao · Bo Li · Ce Zhang
• 2022 Poster: Double Sampling Randomized Smoothing »
  Linyi Li · Jiawei Zhang · Tao Xie · Bo Li
• 2022 Poster: TPC: Transformation-Specific Smoothing for Point Cloud Models »
  Wenda Chu · Linyi Li · Bo Li
• 2022 Spotlight: TPC: Transformation-Specific Smoothing for Point Cloud Models »
  Wenda Chu · Linyi Li · Bo Li
• 2022 Spotlight: Double Sampling Randomized Smoothing »
  Linyi Li · Jiawei Zhang · Tao Xie · Bo Li
• 2022 Oral: Bounding Training Data Reconstruction in Private (Deep) Learning »
  Chuan Guo · Brian Karrer · Kamalika Chaudhuri · Laurens van der Maaten
• 2022 Spotlight: Certifying Out-of-Domain Generalization for Blackbox Functions »
  Maurice Weber · Linyi Li · Boxin Wang · Zhikuan Zhao · Bo Li · Ce Zhang
• 2021 : Invited Talk #11 »
  Will Xiao
• 2021 : Invited Talk #10 »
  Cihang Xie
• 2021 : Invited Talk #9 »
  Kamalika Chaudhuri
• 2021 : Invited Talk #8 »
  Andrzej Banburski
• 2021 : Invited Talk: Kamalika Chaudhuri »
  Kamalika Chaudhuri
• 2021 : Invited Talk #7 »
  Nicholas Carlini
• 2021 Workshop: A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning »
  Hang Su · Yinpeng Dong · Tianyu Pang · Eric Wong · Zico Kolter · Shuo Feng · Bo Li · Henry Liu · Dan Hendrycks · Francesco Croce · Leslie Rice · Tian Tian
• 2021 : Invited Talk: Kamalika Chaudhuri »
  Kamalika Chaudhuri
• 2021 : Live Panel Discussion »
  Thomas Dietterich · Chelsea Finn · Kamalika Chaudhuri · Yarin Gal · Uri Shalit
• 2021 Poster: Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability »
  Kaizhao Liang · Yibo Zhang · Boxin Wang · Zhuolin Yang · Sanmi Koyejo · Bo Li
• 2021 Poster: CRFL: Certifiably Robust Federated Learning against Backdoor Attacks »
  Chulin Xie · Minghao Chen · Pin-Yu Chen · Bo Li
• 2021 Poster: Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation »
  Jiawei Zhang · Linyi Li · Huichen Li · Xiaolu Zhang · Shuang Yang · Bo Li
• 2021 Poster: Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation »
  Haoxiang Wang · Han Zhao · Bo Li
• 2021 Spotlight: Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation »
  Jiawei Zhang · Linyi Li · Huichen Li · Xiaolu Zhang · Shuang Yang · Bo Li
• 2021 Spotlight: Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability »
  Kaizhao Liang · Yibo Zhang · Boxin Wang · Zhuolin Yang · Sanmi Koyejo · Bo Li
• 2021 Spotlight: Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation »
  Haoxiang Wang · Han Zhao · Bo Li
• 2021 Spotlight: CRFL: Certifiably Robust Federated Learning against Backdoor Attacks »
  Chulin Xie · Minghao Chen · Pin-Yu Chen · Bo Li
• 2021 Poster: Label-Only Membership Inference Attacks »
  Christopher Choquette-Choo · Florian Tramer · Nicholas Carlini · Nicolas Papernot
• 2021 Poster: Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks »
  Nezihe Merve Gürel · Xiangyu Qi · Luka Rimanic · Ce Zhang · Bo Li
• 2021 Spotlight: Label-Only Membership Inference Attacks »
  Christopher Choquette-Choo · Florian Tramer · Nicholas Carlini · Nicolas Papernot
• 2021 Spotlight: Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks »
  Nezihe Merve Gürel · Xiangyu Qi · Luka Rimanic · Ce Zhang · Bo Li
• 2021 Poster: Sample Complexity of Robust Linear Classification on Separated Data »
  Robi Bhattacharjee · Somesh Jha · Kamalika Chaudhuri
• 2021 Spotlight: Sample Complexity of Robust Linear Classification on Separated Data »
  Robi Bhattacharjee · Somesh Jha · Kamalika Chaudhuri
• 2021 Poster: Connecting Interpretability and Robustness in Decision Trees through Separation »
  Michal Moshkovitz · Yao-Yuan Yang · Kamalika Chaudhuri
• 2021 Spotlight: Connecting Interpretability and Robustness in Decision Trees through Separation »
  Michal Moshkovitz · Yao-Yuan Yang · Kamalika Chaudhuri
• 2020 Poster: Improving Robustness of Deep-Learning-Based Image Reconstruction »
  Ankit Raj · Yoram Bresler · Bo Li
• 2020 Poster: When are Non-Parametric Methods Robust? »
  Robi Bhattacharjee · Kamalika Chaudhuri
• 2019 Talk: Opening Remarks »
  Kamalika Chaudhuri · Ruslan Salakhutdinov
• 2018 Poster: Active Learning with Logged Data »
  Songbai Yan · Kamalika Chaudhuri · Tara Javidi
• 2018 Poster: Analyzing the Robustness of Nearest Neighbors to Adversarial Examples »
  Yizhen Wang · Somesh Jha · Kamalika Chaudhuri
• 2018 Oral: Active Learning with Logged Data »
  Songbai Yan · Kamalika Chaudhuri · Tara Javidi
• 2018 Oral: Analyzing the Robustness of Nearest Neighbors to Adversarial Examples »
  Yizhen Wang · Somesh Jha · Kamalika Chaudhuri
• 2017 Workshop: Picky Learners: Choosing Alternative Ways to Process Data. »
  Corinna Cortes · Kamalika Chaudhuri · Giulia DeSalvo · Ningshan Zhang · Chicheng Zhang
• 2017 Poster: Active Heteroscedastic Regression »
  Kamalika Chaudhuri · Prateek Jain · Nagarajan Natarajan
• 2017 Talk: Active Heteroscedastic Regression »
  Kamalika Chaudhuri · Prateek Jain · Nagarajan Natarajan