Bhavya Kailkhura
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
51
Venues
20
Active years
2013–2026
Best venue rank
A*
Where they publish
Papers
51 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | AAAI | STAR-1: Safer Alignment of Reasoning LLMs with 1K Data. | Zijun Wang, Haoqin Tu, Yuhan Wang, Juncheng Wu, Yanqing Liu, Jieru Mei, Brian R. Bartoldson, Bhavya Kailkhura, Cihang Xie |
| 2025 | ACL | GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models. | Mufan Qiu, Xinyu Hu, Fengwei Zhan, Sukwon Yun, Jie Peng, Ruichen Zhang, Bhavya Kailkhura, Jiekun Yang, Tianlong Chen |
| 2025 | CIKM | Socially Responsible and Trustworthy Generative Foundation Models: Principles, Challenges, and Practices. | Yue Huang, Canyu Chen, Lu Cheng, Bhavya Kailkhura, Nitesh V. Chawla, Xiangliang Zhang |
| 2025 | ICCV | TruthPrInt: Mitigating Large Vision-Language Models Object Hallucination via Latent Truthful-Guided Pre-Intervention. | Jinhao Duan, Fei Kong, Hao Cheng, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Xiaofeng Zhu, Xiaoshuang Shi, Kaidi Xu |
| 2025 | ICLR | ELFS: Label-Free Coreset Selection with Proxy Training Dynamics. | Haizhong Zheng, Elisa Tsai, Yifu Lu, Jiachen Sun, Brian R. Bartoldson, Bhavya Kailkhura, Atul Prakash |
| 2025 | NAACL | Extracting and Understanding the Superficial Knowledge in Alignment. | Runjin Chen, Gabriel J. Perin, Xuxi Chen, Xilun Chen, Yan Han, Nina S. T. Hirata, Junyuan Hong, Bhavya Kailkhura |
| 2025 | NAACL | Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion. | Jacob K. Christopher, Brian R. Bartoldson, Tal Ben-Nun, Michael Cardei, Bhavya Kailkhura, Ferdinando Fioretto |
| 2025 | NAACL | Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense. | Yang Ouyang, Hengrui Gu, Shuhang Lin, Wenyue Hua, Jie Peng, Bhavya Kailkhura, Meijun Gao, Tianlong Chen, Kaixiong Zhou |
| 2024 | ACL | Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models. | Jinhao Duan, Hao Cheng, Shiqi Wang, Alex Zavalny, Chenan Wang, Renjing Xu, Bhavya Kailkhura, Kaidi Xu |
| 2024 | ACL | RankMean: Module-Level Importance Score for Merging Fine-tuned LLM Models. | Gabriel J. Perin, Xuxi Chen, Shusen Liu, Bhavya Kailkhura, Zhangyang Wang, Brian Gallagher |
| 2024 | ECCV | Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation. | Haizhong Zheng, Jiachen Sun, Shutong Wu, Bhavya Kailkhura, Z. Morley Mao, Chaowei Xiao, Atul Prakash |
| 2024 | EMNLP | SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning. | Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, Sijia Liu |
| 2024 | ICLR | DeepZero: Scaling Up Zeroth-Order Optimization for Deep Model Training. | Aochuan Chen, Yimeng Zhang, Jinghan Jia, James Diffenderfer, Konstantinos Parasyris, Jiancheng Liu, Yihua Zhang, Zheng Zhang, Bhavya Kailkhura, Sijia Liu |
| 2024 | ICLR | NEFTune: Noisy Embeddings Improve Instruction Finetuning. | Neel Jain, Ping-yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein |
| 2024 | ICML | Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies. | Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura |
| 2024 | ICML | Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression. | Junyuan Hong, Jinhao Duan, Chenhui Zhang, Zhangheng Li, Chulin Xie, Kelsey Lieberman, James Diffenderfer, Brian R. Bartoldson, Ajay Kumar Jaiswal, Kaidi Xu, Bhavya Kailkhura, Dan Hendrycks, Dawn Song, Zhangyang Wang, Bo Li |
| 2024 | ICML | Position: TrustLLM: Trustworthiness in Large Language Models. | Yue Huang, Lichao Sun, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Hanchi Sun, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric P. Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, Joaquin Vanschoren, John C. Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yong Chen, Yue Zhao |
| 2024 | NAACL | ReTA: Recursively Thinking Ahead to Improve the Strategic Reasoning of Large Language Models. | Jinhao Duan, Shiqi Wang, James Diffenderfer, Lichao Sun, Tianlong Chen, Bhavya Kailkhura, Kaidi Xu |
| 2024 | WACV | On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization. | Akshay Mehra, Yunbei Zhang, Bhavya Kailkhura, Jihun Hamm |
| 2023 | AAAI | Less is More: Data Pruning for Faster Adversarial Training. | Yize Li, Pu Zhao, Xue Lin, Bhavya Kailkhura, Ryan A. Goldhahn |
| 2023 | WACV | Improving Diversity with Adversarially Learned Transformations for Domain Generalization. | Tejas Gokhale, Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Chitta Baral, Yezhou Yang |
| 2022 | DATE | Unsupervised Test-Time Adaptation of Deep Neural Networks at the Edge: A Case Study. | Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura, Maya B. Gokhale |
| 2022 | DATE | Fault-Tolerant Deep Neural Networks for Processing-In-Memory based Autonomous Edge Systems. | Siyue Wang, Geng Yuan, Xiaolong Ma, Yanyu Li, Xue Lin, Bhavya Kailkhura |
| 2022 | ECCV | A Spectral View of Randomized Smoothing Under Common Corruptions: Benchmarking and Improving Certified Robustness. | Jiachen Sun, Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Dan Hendrycks, Jihun Hamm, Z. Morley Mao |
| 2022 | ICLR | COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks. | Fan Wu, Linyi Li, Huan Zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, Bo Li |
| 2022 | ICLR | On the Certified Robustness for Ensemble Models and Beyond. | Zhuolin Yang, Linyi Li, Xiaojun Xu, Bhavya Kailkhura, Tao Xie, Bo Li |
| 2022 | ISPASS | Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices. | Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura, Maya B. Gokhale |
| 2022 | WACV | More or Less (MoL): Defending against Multiple Perturbation Attacks on Deep Neural Networks through Model Ensemble and Compression. | Hao Cheng, Kaidi Xu, Zhengang Li, Pu Zhao, Chenan Wang, Xue Lin, Bhavya Kailkhura, Ryan A. Goldhahn |
| 2021 | AAAI | Attribute-Guided Adversarial Training for Robustness to Natural Perturbations. | Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Chitta Baral, Yezhou Yang |
| 2021 | CCS | TSS: Transformation-Specific Smoothing for Robustness Certification. | Linyi Li, Maurice Weber, Xiaojun Xu, Luka Rimanic, Bhavya Kailkhura, Tao Xie, Ce Zhang, Bo Li |
| 2021 | CVPR | Scalability vs. Utility: Do We Have To Sacrifice One for the Other in Data Importance Quantification? | Ruoxi Jia, Fan Wu, Xuehui Sun, Jiacen Xu, David Dao, Bhavya Kailkhura, Ce Zhang, Bo Li, Dawn Song |
| 2021 | CVPR | How Robust Are Randomized Smoothing Based Defenses to Data Poisoning? | Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm |
| 2021 | ICCV | Can Shape Structure Features Improve Model Robustness under Diverse Adversarial Settings? | Mingjie Sun, Zichao Li, Chaowei Xiao, Haonan Qiu, Bhavya Kailkhura, Mingyan Liu, Bo Li |
| 2021 | ICLR | Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network. | James Diffenderfer, Bhavya Kailkhura |
| 2021 | MASS | Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing. | Cheng Chen, Bhavya Kailkhura, Ryan A. Goldhahn, Yi Zhou |
| 2021 | UAI | Deep kernels with probabilistic embeddings for small-data learning. | Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han |
| 2020 | ACSSC | Treeview and Disentangled Representations for Explaining Deep Neural Networks Decisions. | Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Bhavya Kailkhura |
| 2020 | ICASSP | Towards an Efficient and General Framework of Robust Training for Graph Neural Networks. | Kaidi Xu, Sijia Liu, Pin-Yu Chen, Mengshu Sun, Caiwen Ding, Bhavya Kailkhura, Xue Lin |
| 2020 | ICML | Adversarial Mutual Information for Text Generation. | Boyuan Pan, Yazheng Yang, Kaizhao Liang, Bhavya Kailkhura, Zhongming Jin, Xian-Sheng Hua, Deng Cai, Bo Li |
| 2020 | ICML | Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning. | Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han |
| 2019 | ICCV | On the Design of Black-Box Adversarial Examples by Leveraging Gradient-Free Optimization and Operator Splitting Method. | Pu Zhao, Sijia Liu, Pin-Yu Chen, Nghia Hoang, Kaidi Xu, Bhavya Kailkhura, Xue Lin |
| 2018 | ICASSP | Human-Machine Inference Networks for Smart Decision Making: Opportunities and Challenges. | Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney |
| 2017 | CVPR | Poisson Disk Sampling on the Grassmannnian: Applications in Subspace Optimization. | Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer |
| 2017 | SC | Performance modeling under resource constraints using deep transfer learning. | Aniruddha Marathe, Rushil Anirudh, Nikhil Jain, Abhinav Bhatele, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Jae-Seung Yeom, Barry Rountree, Todd Gamblin |
| 2016 | ICASSP | Theoretical guarantees for poisson disk sampling using pair correlation function. | Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Pramod K. Varshney |
| 2016 | ICDM | Robust Local Scaling Using Conditional Quantiles of Graph Similarities. | Jayaraman J. Thiagarajan, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Bhavya Kailkhura |
| 2015 | ACSSC | Joint sparsity pattern recovery with 1-bit compressive sensing in sensor networks. | Vipul Gupta, Bhavya Kailkhura, Thakshila Wimalajeewa, Sijia Liu, Pramod K. Varshney |
| 2014 | ACSSC | On physical layer secrecy of collaborative compressive detection. | Bhavya Kailkhura, Thakshila Wimalajeewa, Pramod K. Varshney |
| 2014 | ICASSP | On the performance analysis of data fusion schemes with Byzantines. | Bhavya Kailkhura, Swastik Brahma, Pramod K. Varshney |
| 2014 | MASS | Distributed Compressive Detection with Perfect Secrecy. | Bhavya Kailkhura, Thakshila Wimalajeewa, Lixin Shen, Pramod K. Varshney |
| 2013 | ICASSP | Optimal distributed detection in the presence of Byzantines. | Bhavya Kailkhura, Swastik Brahma, Yunghsiang S. Han, Pramod K. Varshney |