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2020 – today
- 2025
[j29]Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, Yi Zhou:
Deep learning of PDE correction and mesh adaption without automatic differentiation. Mach. Learn. 114(3): 61 (2025)- [c66]Haizhong Zheng, Elisa Tsai, Yifu Lu, Jiachen Sun, Brian R. Bartoldson, Bhavya Kailkhura, Atul Prakash:
ELFS: Label-Free Coreset Selection with Proxy Training Dynamics. ICLR 2025 - [c65]Runjin Chen, Gabriel J. Perin, Xuxi Chen, Xilun Chen, Yan Han, Nina S. T. Hirata, Junyuan Hong, Bhavya Kailkhura:
Extracting and Understanding the Superficial Knowledge in Alignment. NAACL (Long Papers) 2025: 3265-3280 - [c64]Jacob K. Christopher, Brian R. Bartoldson, Tal Ben-Nun, Michael Cardei, Bhavya Kailkhura, Ferdinando Fioretto:
Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion. NAACL (Long Papers) 2025: 12042-12059 - [c63]Yang Ouyang, Hengrui Gu, Shuhang Lin, Wenyue Hua, Jie Peng, Bhavya Kailkhura, Meijun Gao, Tianlong Chen, Kaixiong Zhou:
Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense. NAACL (Long Papers) 2025: 12541-12554 - [i101]Evan R. Antoniuk, Peggy Li, Nathan Keilbart, Stephen Weitzner, Bhavya Kailkhura, Anna M. Hiszpanski:
Active Learning Enables Extrapolation in Molecular Generative Models. CoRR abs/2501.02059 (2025) - [i100]Yang Ouyang, Hengrui Gu, Shuhang Lin, Wenyue Hua, Jie Peng, Bhavya Kailkhura, Tianlong Chen, Kaixiong Zhou:
Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense. CoRR abs/2501.02629 (2025) - [i99]Zeyu Wang, Cihang Xie, Brian R. Bartoldson, Bhavya Kailkhura:
Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness. CoRR abs/2501.09446 (2025) - [i98]Runjin Chen, Gabriel J. Perin, Xuxi Chen, Xilun Chen, Yan Han, Nina S. T. Hirata, Junyuan Hong, Bhavya Kailkhura:
Extracting and Understanding the Superficial Knowledge in Alignment. CoRR abs/2502.04602 (2025) - [i97]Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Tom Goldstein:
Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach. CoRR abs/2502.05171 (2025) - [i96]Franck Cappello, Sandeep Madireddy, Robert Underwood, Neil Getty, Nicholas Lee-Ping Chia, Nesar Ramachandra, Josh Nguyen, Murat Keçeli, Tanwi Mallick, Zilinghan Li, Marieme Ngom, Chenhui Zhang, Angel Yanguas-Gil, Evan R. Antoniuk, Bhavya Kailkhura, Minyang Tian, Yufeng Du, Yuan-Sen Ting, Azton Wells, Bogdan Nicolae, Avinash Maurya, M. Mustafa Rafique, Eliu A. Huerta, Bo Li, Ian T. Foster, Rick Stevens:
EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants. CoRR abs/2502.20309 (2025) - [i95]Mufan Qiu, Xinyu Hu, Fengwei Zhan, Sukwon Yun, Jie Peng, Ruichen Zhang, Bhavya Kailkhura, Jiekun Yang, Tianlong Chen:
GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models. CoRR abs/2503.01682 (2025) - [i94]Michael Cardei, Jacob K. Christopher, Thomas Hartvigsen, Brian R. Bartoldson, Bhavya Kailkhura, Ferdinando Fioretto:
Constrained Language Generation with Discrete Diffusion Models. CoRR abs/2503.09790 (2025) - [i93]Jinhao Duan, Fei Kong, Hao Cheng, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Xiaofeng Zhu, Xiaoshuang Shi, Kaidi Xu:
TruthPrInt: Mitigating LVLM Object Hallucination Via Latent Truthful-Guided Pre-Intervention. CoRR abs/2503.10602 (2025) - [i92]Brian R. Bartoldson, Siddarth Venkatraman, James Diffenderfer, Moksh Jain, Tal Ben-Nun, Seanie Lee, Minsu Kim, Johan Obando-Ceron, Yoshua Bengio, Bhavya Kailkhura:
Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training. CoRR abs/2503.18929 (2025) - [i91]Zijun Wang, Haoqin Tu, Yuhan Wang, Juncheng Wu, Jieru Mei, Brian R. Bartoldson, Bhavya Kailkhura, Cihang Xie:
STAR-1: Safer Alignment of Reasoning LLMs with 1K Data. CoRR abs/2504.01903 (2025) - [i90]Soumyadeep Pal, Changsheng Wang, James Diffenderfer, Bhavya Kailkhura, Sijia Liu:
LLM Unlearning Reveals a Stronger-Than-Expected Coreset Effect in Current Benchmarks. CoRR abs/2504.10185 (2025) - [i89]Kun Wang, Guibin Zhang, Zhenhong Zhou, Jiahao Wu, Miao Yu, Shiqian Zhao, Chenlong Yin, Jinhu Fu, Yibo Yan, Hanjun Luo, Liang Lin, Zhihao Xu, Haolang Lu, Xinye Cao, Xinyun Zhou, Weifei Jin, Fanci Meng, Junyuan Mao, Hao Wu, Minghe Wang, Fan Zhang, Junfeng Fang, Chengwei Liu, Yifan Zhang, Qiankun Li, Chongye Guo, Yalan Qin, Yi Ding, Donghai Hong, Jiaming Ji, Xinfeng Li, Yifan Jiang, Dongxia Wang, Yihao Huang, Yufei Guo, Jen-tse Huang, Yanwei Yue, Wenke Huang, Guancheng Wan, Tianlin Li, Lei Bai, Jie Zhang, Qing Guo, Jingyi Wang, Tianlong Chen, Joey Tianyi Zhou, Xiaojun Jia, Weisong Sun, Cong Wu, Jing Chen, Xuming Hu, Yiming Li, Xiao Wang, Ningyu Zhang, Luu Anh Tuan, Guowen Xu, Tianwei Zhang, Xingjun Ma, Xiang Wang, Bo An, Jun Sun, Mohit Bansal, Shirui Pan, Yuval Elovici, Bhavya Kailkhura, Bo Li, Yaodong Yang, Hongwei Li, Wenyuan Xu, Yizhou Sun, Wei Wang, Qing Li, Ke Tang, Yu-Gang Jiang, Felix Juefei-Xu, Hui Xiong, Xiaofeng Wang, Shuicheng Yan, Dacheng Tao, Philip S. Yu, Qingsong Wen, Yang Liu:
A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment. CoRR abs/2504.15585 (2025) - 2024
- [c62]Gabriel J. Perin, Xuxi Chen, Shusen Liu, Bhavya Kailkhura, Zhangyang Wang, Brian Gallagher:
RankMean: Module-Level Importance Score for Merging Fine-tuned LLM Models. ACL (Findings) 2024: 1776-1782 - [c61]Jinhao Duan, Hao Cheng, Shiqi Wang, Alex Zavalny, Chenan Wang, Renjing Xu, Bhavya Kailkhura, Kaidi Xu:
Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models. ACL (1) 2024: 5050-5063 - [c60]Haizhong Zheng
, Jiachen Sun, Shutong Wu, Bhavya Kailkhura, Z. Morley Mao, Chaowei Xiao, Atul Prakash:
Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation. ECCV (24) 2024: 166-182 - [c59]Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, Sijia Liu:
SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning. EMNLP 2024: 4276-4292 - [c58]Aochuan Chen, Yimeng Zhang, Jinghan Jia, James Diffenderfer, Konstantinos Parasyris, Jiancheng Liu, Yihua Zhang, Zheng Zhang, Bhavya Kailkhura, Sijia Liu:
DeepZero: Scaling Up Zeroth-Order Optimization for Deep Model Training. ICLR 2024 - [c57]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:
NEFTune: Noisy Embeddings Improve Instruction Finetuning. ICLR 2024 - [c56]Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura:
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies. ICML 2024 - [c55]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:
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression. ICML 2024 - [c54]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:
Position: TrustLLM: Trustworthiness in Large Language Models. ICML 2024 - [c53]Zhenyu Zhang, Shiwei Liu, Runjin Chen, Bhavya Kailkhura, Beidi Chen, Atlas Wang:
Q-Hitter: A Better Token Oracle for Efficient LLM Inference via Sparse-Quantized KV Cache. MLSys 2024 - [c52]Jinhao Duan, Shiqi Wang, James Diffenderfer, Lichao Sun, Tianlong Chen, Bhavya Kailkhura, Kaidi Xu:
ReTA: Recursively Thinking Ahead to Improve the Strategic Reasoning of Large Language Models. NAACL-HLT 2024: 2232-2246 - [c51]Jinhao Duan, Renming Zhang, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Elias Stengel-Eskin, Mohit Bansal, Tianlong Chen, Kaidi Xu:
GTBench: Uncovering the Strategic Reasoning Capabilities of LLMs via Game-Theoretic Evaluations. NeurIPS 2024 - [c50]Sean McLeish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein:
Transformers Can Do Arithmetic with the Right Embeddings. NeurIPS 2024 - [c49]Hongru Yang, Bhavya Kailkhura, Zhangyang Wang, Yingbin Liang:
Training Dynamics of Transformers to Recognize Word Co-occurrence via Gradient Flow Analysis. NeurIPS 2024 - [c48]Akshay Mehra, Yunbei Zhang, Bhavya Kailkhura, Jihun Hamm:
On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization. WACV 2024: 3788-3799 - [i88]Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, 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, 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, Yue Zhao:
TrustLLM: Trustworthiness in Large Language Models. CoRR abs/2401.05561 (2024) - [i87]Jinhao Duan, Renming Zhang, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Elias Stengel-Eskin, Mohit Bansal, Tianlong Chen, Kaidi Xu:
GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations. CoRR abs/2402.12348 (2024) - [i86]Junyuan Hong, Jinhao Duan, Chenhui Zhang, Zhangheng Li, Chulin Xie, Kelsey Lieberman, James Diffenderfer, Brian R. Bartoldson, Ajay Jaiswal, Kaidi Xu, Bhavya Kailkhura, Dan Hendrycks, Dawn Song, Zhangyang Wang, Bo Li:
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression. CoRR abs/2403.15447 (2024) - [i85]Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura:
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies. CoRR abs/2404.09349 (2024) - [i84]Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, Yi Zhou:
End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver. CoRR abs/2404.11766 (2024) - [i83]Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, Victor Akinwande, Namir Al-Nuaimi, Najla Alfaraj, Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Borhane Blili-Hamelin, Kurt D. Bollacker, Rishi Bomassani, Marisa Ferrara Boston, Siméon Campos, Kal Chakra, Canyu Chen, Cody Coleman, Zacharie Delpierre Coudert, Leon Derczynski, Debojyoti Dutta, Ian Eisenberg, James Ezick, Heather Frase, Brian Fuller, Ram Gandikota, Agasthya Gangavarapu, Ananya Gangavarapu, James Gealy, Rajat Ghosh, James Goel, Usman Gohar, Subhra S. Goswami, Scott A. Hale, Wiebke Hutiri, Joseph Marvin Imperial, Surgan Jandial, Nick Judd, Felix Juefei-Xu, Foutse Khomh, Bhavya Kailkhura, Hannah Rose Kirk, Kevin Klyman, Chris Knotz, Michael Kuchnik, Shachi H. Kumar, Chris Lengerich, Bo Li, Zeyi Liao, Eileen Peters Long, Victor Lu, Yifan Mai, Priyanka Mary Mammen, Kelvin N. Manyeki, Sean McGregor, Virendra Mehta, Shafee Mohammed, Emanuel Moss, Lama Nachman, Dinesh Jinenhally Naganna, Amin Nikanjam, Besmira Nushi, Luis Oala, Iftach Orr, Alicia Parrish, Cigdem Patlak, William Pietri, Forough Poursabzi-Sangdeh, Eleonora Presani, Fabrizio Puletti, Paul Röttger, Saurav Sahay, Tim Santos, Nino Scherrer, Alice Schoenauer Sebag, Patrick Schramowski, Abolfazl Shahbazi, Vin Sharma, Xudong Shen, Vamsi Sistla, Leonard Tang, Davide Testuggine, Vithursan Thangarasa, Elizabeth Anne Watkins, Rebecca Weiss, Chris Welty, Tyler Wilbers, Adina Williams, Carole-Jean Wu, Poonam Yadav, Xianjun Yang, Yi Zeng, Wenhui Zhang, Fedor Zhdanov, Jiacheng Zhu, Percy Liang, Peter Mattson, Joaquin Vanschoren:
Introducing v0.5 of the AI Safety Benchmark from MLCommons. CoRR abs/2404.12241 (2024) - [i82]Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, Sijia Liu:
SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning. CoRR abs/2404.18239 (2024) - [i81]Sean McLeish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein:
Transformers Can Do Arithmetic with the Right Embeddings. CoRR abs/2405.17399 (2024) - [i80]Saswat Das, Marco Romanelli, Cuong Tran, Zarreen Reza, Bhavya Kailkhura, Ferdinando Fioretto:
Low-rank finetuning for LLMs: A fairness perspective. CoRR abs/2405.18572 (2024) - [i79]Haizhong Zheng, Elisa Tsai, Yifu Lu, Jiachen Sun, Brian R. Bartoldson, Bhavya Kailkhura, Atul Prakash:
ELFS: Enhancing Label-Free Coreset Selection via Clustering-based Pseudo-Labeling. CoRR abs/2406.04273 (2024) - [i78]Jacob K. Christopher, Brian R. Bartoldson, Bhavya Kailkhura, Ferdinando Fioretto:
Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion. CoRR abs/2408.05636 (2024) - [i77]Hongru Yang, Bhavya Kailkhura, Zhangyang Wang, Yingbin Liang:
Training Dynamics of Transformers to Recognize Word Co-occurrence via Gradient Flow Analysis. CoRR abs/2410.09605 (2024) - 2023
- [j28]Brian R. Bartoldson, Bhavya Kailkhura, Davis W. Blalock:
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities. J. Mach. Learn. Res. 24: 122:1-122:77 (2023) - [j27]Ziyi Chen, Bhavya Kailkhura, Yi Zhou:
An accelerated proximal algorithm for regularized nonconvex and nonsmooth bi-level optimization. Mach. Learn. 112(5): 1433-1463 (2023) - [c47]Yize Li, Pu Zhao, Xue Lin, Bhavya Kailkhura, Ryan A. Goldhahn:
Less is More: Data Pruning for Faster Adversarial Training. SafeAI@AAAI 2023 - [c46]Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura:
Neural Image Compression: Generalization, Robustness, and Spectral Biases. NeurIPS 2023 - [c45]Tejas Gokhale, Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Chitta Baral, Yezhou Yang:
Improving Diversity with Adversarially Learned Transformations for Domain Generalization. WACV 2023: 434-443 - [i76]Yize Li, Pu Zhao, Xue Lin, Bhavya Kailkhura, Ryan A. Goldhahn:
Less is More: Data Pruning for Faster Adversarial Training. CoRR abs/2302.12366 (2023) - [i75]Jinhao Duan, Hao Cheng, Shiqi Wang, Chenan Wang, Alex Zavalny, Renjing Xu, Bhavya Kailkhura, Kaidi Xu:
Shifting Attention to Relevance: Towards the Uncertainty Estimation of Large Language Models. CoRR abs/2307.01379 (2023) - [i74]Akshay Mehra, Yunbei Zhang, Bhavya Kailkhura, Jihun Hamm:
On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization. CoRR abs/2307.08551 (2023) - [i73]Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura:
Neural Image Compression: Generalization, Robustness, and Spectral Biases. CoRR abs/2307.08657 (2023) - [i72]Hao Cheng, Jiahang Cao, Erjia Xiao, Pu Zhao, Mengshu Sun, Jiaxu Wang, Jize Zhang, Xue Lin, Bhavya Kailkhura, Kaidi Xu, Renjing Xu:
Gaining the Sparse Rewards by Exploring Binary Lottery Tickets in Spiking Neural Network. CoRR abs/2309.13302 (2023) - [i71]Aochuan Chen, Yimeng Zhang, Jinghan Jia, James Diffenderfer, Jiancheng Liu, Konstantinos Parasyris, Yihua Zhang, Zheng Zhang, Bhavya Kailkhura, Sijia Liu:
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training. CoRR abs/2310.02025 (2023) - [i70]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:
NEFTune: Noisy Embeddings Improve Instruction Finetuning. CoRR abs/2310.05914 (2023) - [i69]Haizhong Zheng, Jiachen Sun, Shutong Wu, Bhavya Kailkhura, Zhuoqing Mao, Chaowei Xiao, Atul Prakash:
Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation. CoRR abs/2310.07506 (2023) - [i68]Zhimin Li, Shusen Liu, Bhavya Kailkhura, Timo Bremer, Valerio Pascucci:
Instance-wise Linearization of Neural Network for Model Interpretation. CoRR abs/2310.16295 (2023) - [i67]Hao Cheng, Jiahang Cao, Erjia Xiao, Mengshu Sun, Le Yang, Jize Zhang, Xue Lin, Bhavya Kailkhura, Kaidi Xu, Renjing Xu:
Pursing the Sparse Limitation of Spiking Deep Learning Structures. CoRR abs/2311.12060 (2023) - [i66]Gourav Datta, Zeyu Liu, James Diffenderfer, Bhavya Kailkhura, Peter A. Beerel:
When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks. CoRR abs/2312.06900 (2023) - [i65]Edoardo Debenedetti, Zishen Wan, Maksym Andriushchenko, Vikash Sehwag, Kshitij Bhardwaj, Bhavya Kailkhura:
Scaling Compute Is Not All You Need for Adversarial Robustness. CoRR abs/2312.13131 (2023) - 2022
- [j26]J. Luc Peterson, Benjamin Bay, Joe Koning, Peter B. Robinson, Jessica Semler, Jeremy White, Rushil Anirudh, Kevin Athey, Peer-Timo Bremer, Francesco Di Natale, David Fox, Jim A. Gaffney, Sam Ade Jacobs, Bhavya Kailkhura, Bogdan Kustowski, Steve H. Langer, Brian K. Spears, Jayaraman J. Thiagarajan, Brian Van Essen, Jae-Seung Yeom:
Enabling machine learning-ready HPC ensembles with Merlin. Future Gener. Comput. Syst. 131: 255-268 (2022) - [j25]Evan R. Antoniuk, Peggy Li, Bhavya Kailkhura, Anna M. Hiszpanski:
Representing Polymers as Periodic Graphs with Learned Descriptors for Accurate Polymer Property Predictions. J. Chem. Inf. Model. 62(22): 5435-5445 (2022) - [j24]Qunwei Li, Bhavya Kailkhura, Ryan A. Goldhahn, Priyadip Ray, Pramod K. Varshney:
Robust Decentralized Learning Using ADMM With Unreliable Agents. IEEE Trans. Signal Process. 70: 2743-2757 (2022) - [c44]Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura, Maya B. Gokhale:
Unsupervised Test-Time Adaptation of Deep Neural Networks at the Edge: A Case Study. DATE 2022: 412-417 - [c43]Siyue Wang, Geng Yuan, Xiaolong Ma, Yanyu Li, Xue Lin, Bhavya Kailkhura:
Fault-Tolerant Deep Neural Networks for Processing-In-Memory based Autonomous Edge Systems. DATE 2022: 424-429 - [c42]Jiachen Sun, Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Dan Hendrycks, Jihun Hamm, Z. Morley Mao:
A Spectral View of Randomized Smoothing Under Common Corruptions: Benchmarking and Improving Certified Robustness. ECCV (4) 2022: 654-671 - [c41]Fan Wu, Linyi Li, Huan Zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, Bo Li:
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks. ICLR 2022 - [c40]Zhuolin Yang, Linyi Li, Xiaojun Xu, Bhavya Kailkhura, Tao Xie, Bo Li:
On the Certified Robustness for Ensemble Models and Beyond. ICLR 2022 - [c39]Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura, Maya B. Gokhale:
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices. ISPASS 2022: 236-238 - [c38]Sara Fridovich-Keil, Brian R. Bartoldson, James Diffenderfer, Bhavya Kailkhura, Timo Bremer:
Models Out of Line: A Fourier Lens on Distribution Shift Robustness. NeurIPS 2022 - [c37]Hao Cheng, Kaidi Xu, Zhengang Li, Pu Zhao, Chenan Wang, Xue Lin, Bhavya Kailkhura, Ryan A. Goldhahn:
More or Less (MoL): Defending against Multiple Perturbation Attacks on Deep Neural Networks through Model Ensemble and Compression. WACV (Workshops) 2022: 645-655 - [i64]Jiachen Sun, Qingzhao Zhang, Bhavya Kailkhura, Zhiding Yu, Chaowei Xiao, Z. Morley Mao:
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions. CoRR abs/2201.12296 (2022) - [i63]Fan Wu, Linyi Li, Chejian Xu, Huan Zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, Bo Li:
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks. CoRR abs/2203.08398 (2022) - [i62]Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura, Maya B. Gokhale:
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices. CoRR abs/2203.11295 (2022) - [i61]Ziyi Chen, Bhavya Kailkhura, Yi Zhou:
A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-level Optimization. CoRR abs/2203.16615 (2022) - [i60]Evan R. Antoniuk, Peggy Li, Bhavya Kailkhura, Anna M. Hiszpanski:
Representing Polymers as Periodic Graphs with Learned Descriptors for Accurate Polymer Property Predictions. CoRR abs/2205.13757 (2022) - [i59]Ioannis C. Tsaknakis, Bhavya Kailkhura, Sijia Liu, Donald Loveland, James Diffenderfer, Anna Maria Hiszpanski, Mingyi Hong:
Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning. CoRR abs/2206.02785 (2022) - [i58]Tejas Gokhale, Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Chitta Baral, Yezhou Yang:
Improving Diversity with Adversarially Learned Transformations for Domain Generalization. CoRR abs/2206.07736 (2022) - [i57]Zhimin Li, Shusen Liu, Xin Yu, Bhavya Kailkhura, Jie Cao, James Daniel Diffenderfer, Peer-Timo Bremer, Valerio Pascucci:
"Understanding Robustness Lottery": A Comparative Visual Analysis of Neural Network Pruning Approaches. CoRR abs/2206.07918 (2022) - [i56]Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm:
On Certifying and Improving Generalization to Unseen Domains. CoRR abs/2206.12364 (2022) - [i55]Sara Fridovich-Keil, Brian R. Bartoldson, James Diffenderfer, Bhavya Kailkhura, Peer-Timo Bremer:
Models Out of Line: A Fourier Lens on Distribution Shift Robustness. CoRR abs/2207.04075 (2022) - [i54]Hao Cheng, Pu Zhao, Yize Li, Xue Lin, James Diffenderfer, Ryan A. Goldhahn, Bhavya Kailkhura:
Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed. CoRR abs/2209.12839 (2022) - [i53]Brian R. Bartoldson, Bhavya Kailkhura, Davis W. Blalock:
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities. CoRR abs/2210.06640 (2022) - 2021
- [j23]Bhavya Kailkhura, Pin-Yu Chen, Xue Lin, Bo Li:
Editorial: Safe and Trustworthy Machine Learning. Frontiers Big Data 4: 731605 (2021) - [j22]Hoseung Song, Jayaraman J. Thiagarajan, Bhavya Kailkhura:
Preventing Failures by Dataset Shift Detection in Safety-Critical Graph Applications. Frontiers Artif. Intell. 4: 589632 (2021) - [j21]Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Jize Zhang, Yi Zhou, Yingbin Liang, Thomas Yong-Jin Han, Pramod K. Varshney:
MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data. SIAM J. Math. Data Sci. 3(4): 1197-1222 (2021) - [j20]Gowtham Muniraju, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Cihan Tepedelenlioglu, Andreas Spanias:
Coverage-Based Designs Improve Sample Mining and Hyperparameter Optimization. IEEE Trans. Neural Networks Learn. Syst. 32(3): 1241-1253 (2021) - [c36]Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Chitta Baral, Yezhou Yang:
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations. AAAI 2021: 7574-7582 - [c35]Linyi Li, Maurice Weber, Xiaojun Xu, Luka Rimanic, Bhavya Kailkhura, Tao Xie, Ce Zhang, Bo Li:
TSS: Transformation-Specific Smoothing for Robustness Certification. CCS 2021: 535-557 - [c34]Ruoxi Jia, Fan Wu, Xuehui Sun, Jiacen Xu, David Dao, Bhavya Kailkhura, Ce Zhang, Bo Li, Dawn Song:
Scalability vs. Utility: Do We Have To Sacrifice One for the Other in Data Importance Quantification? CVPR 2021: 8239-8247 - [c33]Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm:
How Robust Are Randomized Smoothing Based Defenses to Data Poisoning? CVPR 2021: 13244-13253 - [c32]Mingjie Sun, Zichao Li, Chaowei Xiao, Haonan Qiu, Bhavya Kailkhura, Mingyan Liu, Bo Li:
Can Shape Structure Features Improve Model Robustness under Diverse Adversarial Settings? ICCV 2021: 7506-7515 - [c31]James Diffenderfer, Bhavya Kailkhura:
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network. ICLR 2021 - [c30]Cheng Chen, Bhavya Kailkhura, Ryan A. Goldhahn, Yi Zhou:
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing. MASS 2021: 173-179 - [c29]James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura:
A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness. NeurIPS 2021: 664-676 - [c28]Yunhui Long, Boxin Wang, Zhuolin Yang, Bhavya Kailkhura, Aston Zhang, Carl A. Gunter, Bo Li:
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators. NeurIPS 2021: 2965-2977 - [c27]Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm:
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning. NeurIPS 2021: 17347-17359 - [c26]Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han:
Deep kernels with probabilistic embeddings for small-data learning. UAI 2021: 918-928 - [i52]Xiaoyang Wang, Bo Li, Yibo Zhang, Bhavya Kailkhura, Klara Nahrstedt:
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning. CoRR abs/2101.05950 (2021) - [i51]James Diffenderfer, Bhavya Kailkhura:
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network. CoRR abs/2103.09377 (2021) - [i50]Cheng Chen, Bhavya Kailkhura, Ryan A. Goldhahn, Yi Zhou:
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing. CoRR abs/2103.16031 (2021) - [i49]Hao Cheng, Kaidi Xu, Chenan Wang, Xue Lin, Bhavya Kailkhura, Ryan A. Goldhahn:
Mixture of Robust Experts (MoRE): A Flexible Defense Against Multiple Perturbations. CoRR abs/2104.10586 (2021) - [i48]James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura:
A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness. CoRR abs/2106.09129 (2021) - [i47]Donald Loveland, Shusen Liu, Bhavya Kailkhura, Anna M. Hiszpanski, Yong Han:
Reliable Graph Neural Network Explanations Through Adversarial Training. CoRR abs/2106.13427 (2021) - [i46]Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm:
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning. CoRR abs/2107.03919 (2021) - [i45]Zhuolin Yang, Linyi Li, Xiaojun Xu, Bhavya Kailkhura, Tao Xie, Bo Li:
On the Certified Robustness for Ensemble Models and Beyond. CoRR abs/2107.10873 (2021) - [i44]Jiachen Sun, Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Dan Hendrycks, Jihun Hamm, Z. Morley Mao:
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines. CoRR abs/2112.00659 (2021) - 2020
- [j19]Saikiran Bulusu, Bhavya Kailkhura, Bo Li, Pramod K. Varshney, Dawn Song:
Anomalous Example Detection in Deep Learning: A Survey. IEEE Access 8: 132330-132347 (2020) - [j18]Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Peer-Timo Bremer:
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking. Int. J. Comput. Vis. 128(10): 2459-2477 (2020) - [j17]Anna M. Hiszpanski, Brian Gallagher, Karthik Chellappan, Peggy Li, Shusen Liu, Hyojin Kim, Jinkyu Han, Bhavya Kailkhura, David J. Buttler, Thomas Yong-Jin Han:
Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge. J. Chem. Inf. Model. 60(6): 2876-2887 (2020) - [j16]Donald Loveland, Bhavya Kailkhura, Piyush Karande, Anna M. Hiszpanski, Thomas Yong-Jin Han:
Automated Identification of Molecular Crystals' Packing Motifs. J. Chem. Inf. Model. 60(12): 6147-6154 (2020) - [j15]Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura, Gaoyuan Zhang, Alfred O. Hero III, Pramod K. Varshney:
A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning: Principals, Recent Advances, and Applications. IEEE Signal Process. Mag. 37(5): 43-54 (2020) - [c25]Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Bhavya Kailkhura:
Treeview and Disentangled Representations for Explaining Deep Neural Networks Decisions. ACSSC 2020: 284-288 - [c24]Cheng Chen, Ziyi Chen, Yi Zhou, Bhavya Kailkhura:
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling. IEEE BigData 2020: 5017-5026 - [c23]Kaidi Xu, Sijia Liu, Pin-Yu Chen, Mengshu Sun, Caiwen Ding, Bhavya Kailkhura, Xue Lin:
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks. ICASSP 2020: 8479-8483 - [c22]Boyuan Pan, Yazheng Yang, Kaizhao Liang, Bhavya Kailkhura, Zhongming Jin, Xian-Sheng Hua, Deng Cai, Bo Li:
Adversarial Mutual Information for Text Generation. ICML 2020: 7476-7486 - [c21]Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han:
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning. ICML 2020: 11117-11128 - [c20]Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Jize Zhang, Yi Zhou, Timo Bremer:
A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning. NeurIPS 2020 - [c19]Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh:
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond. NeurIPS 2020 - [i43]Kaidi Xu, Sijia Liu, Pin-Yu Chen, Mengshu Sun, Caiwen Ding, Bhavya Kailkhura, Xue Lin:
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks. CoRR abs/2002.10947 (2020) - [i42]Kaidi Xu, Zhouxing Shi, Huan Zhang, Minlie Huang, Kai-Wei Chang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh:
Automatic Perturbation Analysis on General Computational Graphs. CoRR abs/2002.12920 (2020) - [i41]Saikiran Bulusu, Bhavya Kailkhura, Bo Li, Pramod K. Varshney, Dawn Song:
Anomalous Instance Detection in Deep Learning: A Survey. CoRR abs/2003.06979 (2020) - [i40]Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han:
Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning. CoRR abs/2003.07329 (2020) - [i39]Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura, Gaoyuan Zhang, Alfred O. Hero III, Pramod K. Varshney:
A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning. CoRR abs/2006.06224 (2020) - [i38]Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Thomas Yong-Jin Han:
Actionable Attribution Maps for Scientific Machine Learning. CoRR abs/2006.16533 (2020) - [i37]Boyuan Pan, Yazheng Yang, Kaizhao Liang, Bhavya Kailkhura, Zhongming Jin, Xian-Sheng Hua, Deng Cai, Bo Li:
Adversarial Mutual Information for Text Generation. CoRR abs/2007.00067 (2020) - [i36]Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Thomas Yong-Jin Han:
Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design. CoRR abs/2007.08631 (2020) - [i35]Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han:
Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning. CoRR abs/2007.10800 (2020) - [i34]Cheng Chen, Ziyi Chen, Yi Zhou, Bhavya Kailkhura:
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling. CoRR abs/2009.10748 (2020) - [i33]Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm:
How Robust are Randomized Smoothing based Defenses to Data Poisoning? CoRR abs/2012.01274 (2020) - [i32]Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han:
Leveraging Uncertainty from Deep Learning for Trustworthy Materials Discovery Workflows. CoRR abs/2012.01478 (2020) - [i31]Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Chitta Baral, Yezhou Yang:
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations. CoRR abs/2012.01806 (2020)
2010 – 2019
- 2019
- [j14]Swatantra Kafle, Vipul Gupta, Bhavya Kailkhura, Thakshila Wimalajeewa, Pramod K. Varshney:
Joint Sparsity Pattern Recovery With 1-b Compressive Sensing in Distributed Sensor Networks. IEEE Trans. Signal Inf. Process. over Networks 5(1): 15-30 (2019) - [j13]Chengxi Li, Gang Li, Bhavya Kailkhura, Pramod K. Varshney:
Secure Distributed Detection of Sparse Signals via Falsification of Local Compressive Measurements. IEEE Trans. Signal Process. 67(18): 4696-4706 (2019) - [c18]Shusen Liu, Bhavya Kailkhura, Donald Loveland, Yong Han:
Generative Counterfactual Introspection for Explainable Deep Learning. GlobalSIP 2019: 1-5 - [c17]Pu Zhao, Sijia Liu, Pin-Yu Chen, Nghia Hoang, Kaidi Xu, Bhavya Kailkhura, Xue Lin:
On the Design of Black-Box Adversarial Examples by Leveraging Gradient-Free Optimization and Operator Splitting Method. ICCV 2019: 121-130 - [i30]Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Peer-Timo Bremer:
A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis. CoRR abs/1906.02732 (2019) - [i29]Shusen Liu, Bhavya Kailkhura, Donald Loveland, Yong Han:
Generative Counterfactual Introspection for Explainable Deep Learning. CoRR abs/1907.03077 (2019) - [i28]Pu Zhao, Sijia Liu, Pin-Yu Chen, Nghia Hoang, Kaidi Xu, Bhavya Kailkhura, Xue Lin:
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method. CoRR abs/1907.11684 (2019) - [i27]Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han:
Deep Probabilistic Kernels for Sample-Efficient Learning. CoRR abs/1910.05858 (2019) - [i26]J. Luc Peterson, Rushil Anirudh, Kevin Athey, Benjamin Bay, Peer-Timo Bremer, Vic Castillo, Francesco Di Natale, David Fox, Jim A. Gaffney, David Hysom, Sam Ade Jacobs, Bhavya Kailkhura, Joe Koning, Bogdan Kustowski, Steven H. Langer, Peter B. Robinson, Jessica Semler, Brian K. Spears, Jayaraman J. Thiagarajan, Brian Van Essen, Jae-Seung Yeom:
Merlin: Enabling Machine Learning-Ready HPC Ensembles. CoRR abs/1912.02892 (2019) - [i25]Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Timo Bremer:
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking. CoRR abs/1912.07748 (2019) - 2018
- [b1]Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney:
Secure Networked Inference with Unreliable Data Sources. Springer 2018, ISBN 978-981-13-2311-9, pp. 1-208 - [j12]Bhavya Kailkhura, Jayaraman J. Thiagarajan, Charvi Rastogi, Pramod K. Varshney, Peer-Timo Bremer:
A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms. J. Mach. Learn. Res. 19: 34:1-34:46 (2018) - [c16]Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney:
Human-Machine Inference Networks for Smart Decision Making: Opportunities and Challenges. ICASSP 2018: 6961-6965 - [c15]Jayaraman J. Thiagarajan, Rushil Anirudh, Bhavya Kailkhura, Nikhil Jain, Tanzima Z. Islam, Abhinav Bhatele, Jae-Seung Yeom, Todd Gamblin:
PADDLE: Performance Analysis Using a Data-Driven Learning Environment. IPDPS 2018: 784-793 - [c14]Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen, Pai-Shun Ting, Shiyu Chang, Lisa Amini:
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization. NeurIPS 2018: 3731-3741 - [i24]Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney:
Human-Machine Inference Networks For Smart Decision Making: Opportunities and Challenges. CoRR abs/1801.09626 (2018) - [i23]Bhavya Kailkhura, Priyadip Ray, Deepak Rajan, Anton Yen, Peter D. Barnes Jr., Ryan A. Goldhahn:
Byzantine-Resilient Locally Optimum Detection Using Collaborative Autonomous Networks. CoRR abs/1803.01221 (2018) - [i22]Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Timo Bremer:
An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks. CoRR abs/1805.07281 (2018) - [i21]Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen, Pai-Shun Ting, Shiyu Chang, Lisa Amini:
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization. CoRR abs/1805.10367 (2018) - [i20]Gowtham Muniraju, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer:
Controlled Random Search Improves Sample Mining and Hyper-Parameter Optimization. CoRR abs/1809.01712 (2018) - [i19]Thomas A. Hogan, Bhavya Kailkhura:
Universal Decision-Based Black-Box Perturbations: Breaking Security-Through-Obscurity Defenses. CoRR abs/1811.03733 (2018) - [i18]Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Timo Bremer:
MimicGAN: Corruption-Mimicking for Blind Image Recovery & Adversarial Defense. CoRR abs/1811.08484 (2018) - [i17]Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Yi Zhou, Yingbin Liang, Pramod K. Varshney:
MR-GAN: Manifold Regularized Generative Adversarial Networks. CoRR abs/1811.10427 (2018) - 2017
- [j11]Bhavya Kailkhura, Swastik Brahma, Pramod K. Varshney:
Data Falsification Attacks on Consensus-Based Detection Systems. IEEE Trans. Signal Inf. Process. over Networks 3(1): 145-158 (2017) - [j10]Bhavya Kailkhura, Thakshila Wimalajeewa, Pramod K. Varshney:
Collaborative Compressive Detection With Physical Layer Secrecy Constraints. IEEE Trans. Signal Process. 65(4): 1013-1025 (2017) - [j9]Bhavya Kailkhura, Lakshmi Narasimhan Theagarajan, Pramod K. Varshney:
Subspace-Aware Index Codes. IEEE Wirel. Commun. Lett. 6(3): 366-369 (2017) - [c13]Bhavya Kailkhura, Priyadip Ray, Deepak Rajan, Anton Yen, Peter D. Barnes Jr., Ryan A. Goldhahn:
Byzantine-Resilient locally optimum detection using collaborative autonomous networks. CAMSAP 2017: 1-5 - [c12]Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer:
Poisson Disk Sampling on the Grassmannnian: Applications in Subspace Optimization. CVPR Workshops 2017: 690-698 - [c11]Aniruddha Marathe, Rushil Anirudh, Nikhil Jain, Abhinav Bhatele, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Jae-Seung Yeom, Barry Rountree, Todd Gamblin:
Performance modeling under resource constraints using deep transfer learning. SC 2017: 31 - [i16]Bhavya Kailkhura, Lakshmi Narasimhan Theagarajan, Pramod K. Varshney:
Subspace-Aware Index Codes. CoRR abs/1702.03589 (2017) - [i15]Qunwei Li, Bhavya Kailkhura, Ryan A. Goldhahn, Priyadip Ray, Pramod K. Varshney:
Robust Federated Learning Using ADMM in the Presence of Data Falsifying Byzantines. CoRR abs/1710.05241 (2017) - [i14]Bhavya Kailkhura, Jayaraman J. Thiagarajan, Charvi Rastogi, Pramod K. Varshney, Peer-Timo Bremer:
A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms. CoRR abs/1712.06028 (2017) - 2016
- [j8]Prashant Khanduri, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Pramod K. Varshney:
Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach. IEEE Signal Process. Lett. 23(10): 1484-1488 (2016) - [j7]Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Pramod K. Varshney:
Stair blue noise sampling. ACM Trans. Graph. 35(6): 248:1-248:10 (2016) - [j6]Bhavya Kailkhura, Sijia Liu, Thakshila Wimalajeewa, Pramod K. Varshney:
Measurement Matrix Design for Compressed Detection With Secrecy Guarantees. IEEE Wirel. Commun. Lett. 5(4): 420-423 (2016) - [c10]Swatantra Kafle, Bhavya Kailkhura, Thakshila Wimalajeewa, Pramod K. Varshney:
Decentralized joint sparsity pattern recovery using 1-bit compressive sensing. GlobalSIP 2016: 1354-1358 - [c9]Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Pramod K. Varshney:
Theoretical guarantees for poisson disk sampling using pair correlation function. ICASSP 2016: 2589-2593 - [c8]Jayaraman J. Thiagarajan, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Bhavya Kailkhura:
Robust Local Scaling Using Conditional Quantiles of Graph Similarities. ICDM Workshops 2016: 762-769 - [c7]Qunwei Li, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Zhenliang Zhang, Pramod K. Varshney:
Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models. NIPS Time Series Workshop 2016: 27-37 - [i13]Prashant Khanduri, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Pramod K. Varshney:
Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach. CoRR abs/1601.06201 (2016) - [i12]Jayaraman J. Thiagarajan, Bhavya Kailkhura, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy:
TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning. CoRR abs/1611.07429 (2016) - [i11]Qunwei Li, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Zhenliang Zhang, Pramod K. Varshney:
Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models. CoRR abs/1611.10305 (2016) - 2015
- [j5]Bhavya Kailkhura, V. Sriram Siddhardh Nadendla, Pramod K. Varshney:
Distributed inference in the presence of eavesdroppers: a survey. IEEE Commun. Mag. 53(6): 40-46 (2015) - [j4]Bhavya Kailkhura, Yunghsiang Sam Han, Swastik Brahma, Pramod K. Varshney:
Asymptotic Analysis of Distributed Bayesian Detection with Byzantine Data. IEEE Signal Process. Lett. 22(5): 608-612 (2015) - [j3]Bhavya Kailkhura, Aditya Vempaty, Pramod K. Varshney:
Distributed Inference in Tree Networks Using Coding Theory. IEEE Trans. Signal Process. 63(14): 3715-3726 (2015) - [j2]Bhavya Kailkhura, Yunghsiang Sam Han, Swastik Brahma, Pramod K. Varshney:
Distributed Bayesian Detection in the Presence of Byzantine Data. IEEE Trans. Signal Process. 63(19): 5250-5263 (2015) - [c6]Vipul Gupta, Bhavya Kailkhura, Thakshila Wimalajeewa, Sijia Liu, Pramod K. Varshney:
Joint sparsity pattern recovery with 1-bit compressive sensing in sensor networks. ACSSC 2015: 1472-1476 - [i10]Bhavya Kailkhura, Thakshila Wimalajeewa, Pramod K. Varshney:
Collaborative Compressive Detection with Physical Layer Secrecy Constraints. CoRR abs/1502.05370 (2015) - [i9]Bhavya Kailkhura, V. Sriram Siddhardh Nadendla, Pramod K. Varshney:
Distributed Inference in the Presence of Eavesdroppers: A Survey. CoRR abs/1502.05448 (2015) - [i8]Bhavya Kailkhura, Swastik Brahma, Pramod K. Varshney:
Consensus based Detection in the Presence of Data Falsification Attacks. CoRR abs/1504.03413 (2015) - [i7]Bhavya Kailkhura, Sijia Liu, Thakshila Wimalajeewa, Pramod K. Varshney:
Measurement Matrix Design for Compressive Detection with Secrecy Guarantees. CoRR abs/1506.00238 (2015) - [i6]Vipul Gupta, Bhavya Kailkhura, Thakshila Wimalajeewa, Pramod K. Varshney:
Joint Sparsity Pattern Recovery with 1-bit Compressive Sensing in Sensor Networks. CoRR abs/1506.00540 (2015) - 2014
- [j1]Bhavya Kailkhura, Swastik Brahma, Yunghsiang S. Han, Pramod K. Varshney:
Distributed Detection in Tree Topologies With Byzantines. IEEE Trans. Signal Process. 62(12): 3208-3219 (2014) - [c5]Bhavya Kailkhura, Thakshila Wimalajeewa, Pramod K. Varshney:
On physical layer secrecy of collaborative compressive detection. ACSSC 2014: 51-55 - [c4]Bhavya Kailkhura, Swastik Brahma, Pramod K. Varshney:
On the performance analysis of data fusion schemes with Byzantines. ICASSP 2014: 7411-7415 - [c3]Bhavya Kailkhura, Thakshila Wimalajeewa, Lixin Shen, Pramod K. Varshney:
Distributed Compressive Detection with Perfect Secrecy. MASS 2014: 674-679 - [i5]Bhavya Kailkhura, Aditya Vempaty, Pramod K. Varshney:
Distributed Inference in Tree Networks using Coding Theory. CoRR abs/1407.7150 (2014) - [i4]Bhavya Kailkhura, Yunghsiang S. Han, Swastik Brahma, Pramod K. Varshney:
Asymptotic Analysis of Distributed Bayesian Detection with Byzantine Data. CoRR abs/1408.3434 (2014) - [i3]Bhavya Kailkhura, Swastik Brahma, Berkan Dulek, Yunghsiang S. Han, Pramod K. Varshney:
Distributed Detection in Tree Networks: Byzantines and Mitigation Techniques. CoRR abs/1410.5904 (2014) - 2013
- [c2]Bhavya Kailkhura, Swastik Brahma, Yunghsiang S. Han, Pramod K. Varshney:
Optimal distributed detection in the presence of Byzantines. ICASSP 2013: 2925-2929 - [c1]Bhavya Kailkhura, Swastik Brahma, Pramod K. Varshney:
Optimal Byzantine attacks on distributed detection in tree-based topologies. ICNC 2013: 227-231 - [i2]Bhavya Kailkhura, Yunghsiang S. Han, Swastik Brahma, Pramod K. Varshney:
Distributed Bayesian Detection with Byzantine Data. CoRR abs/1307.3544 (2013) - [i1]Bhavya Kailkhura, Swastik Brahma, Yunghsiang S. Han, Pramod K. Varshney:
Distributed Detection in Tree Topologies with Byzantines. CoRR abs/1309.4513 (2013)
Coauthor Index
Peer-Timo Bremer
aka: Timo Bremer
aka: Timo Bremer
James Diffenderfer
aka: James Daniel Diffenderfer
aka: James Daniel Diffenderfer
Anna M. Hiszpanski
aka: Anna Maria Hiszpanski
aka: Anna Maria Hiszpanski
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Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-05-24 01:02 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
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since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
