Bhavya Kailkhura

214 papers A* 30A 8B 3Misc 9Journal 143Unranked 20
YearRankTypeTitle / Venue / Authors
2026 J jnl
ProtAlign: Contrastive learning paradigm for Sequence and structure alignment.
CoRR
Aditya Ranganath, Hasin Us Sami, Kowshik Thopalli, Bhavya Kailkhura, Wesam A. Sakla
2026 A* conf
STAR-1: Safer Alignment of Reasoning LLMs with 1K Data.
AAAI
Zijun Wang, Haoqin Tu, Yuhan Wang, Juncheng Wu, Yanqing Liu, Jieru Mei, Brian R. Bartoldson, Bhavya Kailkhura, Cihang Xie
2025 J jnl
"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches.
IEEE Trans. Vis. Comput. Graph.
Zhimin Li, Shusen Liu, Xin Yu, Bhavya Kailkhura, Jie Cao, James Daniel Diffenderfer, Peer-Timo Bremer, Valerio Pascucci
2025 J jnl
A Comedy of Estimators: On KL Regularization in RL Training of LLMs.
CoRR
Vedant Shah, Johan S. Obando-Ceron, Vineet Jain, Brian R. Bartoldson, Bhavya Kailkhura, Sarthak Mittal, Glen Berseth, Pablo Samuel Castro, Yoshua Bengio, Nikolay Malkin, Moksh Jain, Siddarth Venkatraman, Aaron C. Courville
2025 J jnl
A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment.
CoRR
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
2025 J jnl
Act Only When It Pays: Efficient Reinforcement Learning for LLM Reasoning via Selective Rollouts.
CoRR
Haizhong Zheng, Yang Zhou, Brian R. Bartoldson, Bhavya Kailkhura, Fan Lai, Jiawei Zhao, Beidi Chen
2025 J jnl
Active Learning Enables Extrapolation in Molecular Generative Models.
CoRR
Evan R. Antoniuk, Peggy Li, Nathan Keilbart, Stephen Weitzner, Bhavya Kailkhura, Anna M. Hiszpanski
2025 J jnl
AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security.
CoRR
Zikui Cai, Shayan Shabihi, Bang An, Zora Che, Brian R. Bartoldson, Bhavya Kailkhura, Tom Goldstein, Furong Huang
2025 J jnl
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models.
CoRR
Evan R. Antoniuk, Shehtab Zaman, Tal Ben-Nun, Peggy Li, James Diffenderfer, Busra Demirci, Obadiah Smolenski, Tim Hsu, Anna M. Hiszpanski, Kenneth Chiu, Bhavya Kailkhura, Brian Van Essen
2025 J jnl
Constrained Language Generation with Discrete Diffusion Models.
CoRR
Michael Cardei, Jacob K. Christopher, Thomas Hartvigsen, Brian R. Bartoldson, Bhavya Kailkhura, Ferdinando Fioretto
2025 J jnl
Deep learning of PDE correction and mesh adaption without automatic differentiation.
Mach. Learn.
Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, Yi Zhou
2025 J jnl
Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness.
CoRR
Zeyu Wang, Cihang Xie, Brian R. Bartoldson, Bhavya Kailkhura
2025 J jnl
EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants.
CoRR
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
2025 A* conf
ELFS: Label-Free Coreset Selection with Proxy Training Dynamics.
ICLR
Haizhong Zheng, Elisa Tsai, Yifu Lu, Jiachen Sun, Brian R. Bartoldson, Bhavya Kailkhura, Atul Prakash
2025 — conf
Extracting and Understanding the Superficial Knowledge in Alignment.
NAACL (Long Papers)
Runjin Chen, Gabriel J. Perin, Xuxi Chen, Xilun Chen, Yan Han, Nina S. T. Hirata, Junyuan Hong, Bhavya Kailkhura
2025 J jnl
Extracting and Understanding the Superficial Knowledge in Alignment.
CoRR
Runjin Chen, Gabriel J. Perin, Xuxi Chen, Xilun Chen, Yan Han, Nina S. T. Hirata, Junyuan Hong, Bhavya Kailkhura
2025 J jnl
Forecasting Fails: Unveiling Evasion Attacks in Weather Prediction Models.
CoRR
Huzaifa Arif, Pin-Yu Chen, Alex Gittens, James Diffenderfer, Bhavya Kailkhura
2025 — conf
GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models.
ACL (Findings)
Mufan Qiu, Xinyu Hu, Fengwei Zhan, Sukwon Yun, Jie Peng, Ruichen Zhang, Bhavya Kailkhura, Jiekun Yang, Tianlong Chen
2025 J jnl
GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models.
CoRR
Mufan Qiu, Xinyu Hu, Fengwei Zhan, Sukwon Yun, Jie Peng, Ruichen Zhang, Bhavya Kailkhura, Jiekun Yang, Tianlong Chen
2025 J jnl
Get RICH or Die Scaling: Profitably Trading Inference Compute for Robustness.
CoRR
Tavish McDonald, Bo Lei, Stanislav Fort, Bhavya Kailkhura, Brian R. Bartoldson
2025 J jnl
LLM Unlearning Reveals a Stronger-Than-Expected Coreset Effect in Current Benchmarks.
CoRR
Soumyadeep Pal, Changsheng Wang, James Diffenderfer, Bhavya Kailkhura, Sijia Liu
2025 — conf
Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense.
NAACL (Long Papers)
Yang Ouyang, Hengrui Gu, Shuhang Lin, Wenyue Hua, Jie Peng, Bhavya Kailkhura, Meijun Gao, Tianlong Chen, Kaixiong Zhou
2025 J jnl
Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense.
CoRR
Yang Ouyang, Hengrui Gu, Shuhang Lin, Wenyue Hua, Jie Peng, Bhavya Kailkhura, Tianlong Chen, Kaixiong Zhou
2025 J jnl
Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models.
CoRR
Siddarth Venkatraman, Vineet Jain, Sarthak Mittal, Vedant Shah, Johan S. Obando-Ceron, Yoshua Bengio, Brian R. Bartoldson, Bhavya Kailkhura, Guillaume Lajoie, Glen Berseth, Nikolay Malkin, Moksh Jain
2025 J jnl
STAR-1: Safer Alignment of Reasoning LLMs with 1K Data.
CoRR
Zijun Wang, Haoqin Tu, Yuhan Wang, Juncheng Wu, Jieru Mei, Brian R. Bartoldson, Bhavya Kailkhura, Cihang Xie
2025 J jnl
Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach.
CoRR
Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Tom Goldstein
2025 A conf
Socially Responsible and Trustworthy Generative Foundation Models: Principles, Challenges, and Practices.
CIKM
Yue Huang, Canyu Chen, Lu Cheng, Bhavya Kailkhura, Nitesh V. Chawla, Xiangliang Zhang
2025 — conf
Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion.
NAACL (Long Papers)
Jacob K. Christopher, Brian R. Bartoldson, Tal Ben-Nun, Michael Cardei, Bhavya Kailkhura, Ferdinando Fioretto
2025 J jnl
Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence.
CoRR
Sean McLeish, Ang Li, John Kirchenbauer, Dayal Singh Kalra, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Jonas Geiping, Tom Goldstein, Micah Goldblum
2025 J jnl
The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text.
CoRR
Nikhil Kandpal, Brian Lester, Colin Raffel, Sebastian Majstorovic, Stella Biderman, Baber Abbasi, Luca Soldaini, Enrico Shippole, A. Feder Cooper, Aviya Skowron, John Kirchenbauer, Shayne Longpre, Lintang Sutawika, Alon Albalak, Zhenlin Xu, Guilherme Penedo, Loubna Ben Allal, Elie Bakouch, John David Pressman, Honglu Fan, Dashiell Stander, Guangyu Song, Aaron Gokaslan, Tom Goldstein, Brian R. Bartoldson, Bhavya Kailkhura, Tyler Murray
2025 J jnl
Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training.
CoRR
Brian R. Bartoldson, Siddarth Venkatraman, James Diffenderfer, Moksh Jain, Tal Ben-Nun, Seanie Lee, Minsu Kim, Johan S. Obando-Ceron, Yoshua Bengio, Bhavya Kailkhura
2025 J jnl
TruthPrInt: Mitigating LVLM Object Hallucination Via Latent Truthful-Guided Pre-Intervention.
CoRR
Jinhao Duan, Fei Kong, Hao Cheng, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Xiaofeng Zhu, Xiaoshuang Shi, Kaidi Xu
2025 J jnl
UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making.
CoRR
Jinhao Duan, James Diffenderfer, Sandeep Madireddy, Tianlong Chen, Bhavya Kailkhura, Kaidi Xu
2025 J jnl
When SNN meets ANN: Error-Free ANN-to-SNN Conversion for Extreme Edge Efficiency.
Trans. Mach. Learn. Res.
Gourav Datta, Zeyu Liu, James Diffenderfer, Bhavya Kailkhura, Peter Anthony Beerel
2024 A* conf
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies.
ICML
Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura
2024 J jnl
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies.
CoRR
Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura
2024 — conf
Cross-Quality Few-Shot Transfer for Alloy Yield Strength Prediction: A New Materials Science Benchmark and A Sparsity-Oriented Optimization Framework.
CPAL
Xuxi Chen, Tianlong Chen, Everardo Yeriel Olivares, Kate Elder, Scott McCall, Aurelien Perron, Joseph McKeown, Bhavya Kailkhura, Zhangyang Wang, Brian Gallagher
2024 A* conf
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression.
ICML
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 J jnl
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression.
CoRR
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
2024 A* conf
DeepZero: Scaling Up Zeroth-Order Optimization for Deep Model Training.
ICLR
Aochuan Chen, Yimeng Zhang, Jinghan Jia, James Diffenderfer, Konstantinos Parasyris, Jiancheng Liu, Yihua Zhang, Zheng Zhang, Bhavya Kailkhura, Sijia Liu
2024 J jnl
ELFS: Enhancing Label-Free Coreset Selection via Clustering-based Pseudo-Labeling.
CoRR
Haizhong Zheng, Elisa Tsai, Yifu Lu, Jiachen Sun, Brian R. Bartoldson, Bhavya Kailkhura, Atul Prakash
2024 J jnl
End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver.
CoRR
Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, Yi Zhou
2024 A* conf
GTBench: Uncovering the Strategic Reasoning Capabilities of LLMs via Game-Theoretic Evaluations.
NeurIPS
Jinhao Duan, Renming Zhang, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Elias Stengel-Eskin, Mohit Bansal, Tianlong Chen, Kaidi Xu
2024 J jnl
GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations.
CoRR
Jinhao Duan, Renming Zhang, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Elias Stengel-Eskin, Mohit Bansal, Tianlong Chen, Kaidi Xu
2024 J jnl
Introducing v0.5 of the AI Safety Benchmark from MLCommons.
CoRR
Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, Victor Akinwande, Namir Al-Nuaimi, Najla Alfaraj, Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Borhane Blili-Hamelin, Kurt 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
2024 — conf
Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation.
ECCV (24)
Haizhong Zheng, Jiachen Sun, Shutong Wu, Bhavya Kailkhura, Z. Morley Mao, Chaowei Xiao, Atul Prakash
2024 J jnl
Low-rank finetuning for LLMs: A fairness perspective.
CoRR
Saswat Das, Marco Romanelli, Cuong Tran, Zarreen Reza, Bhavya Kailkhura, Ferdinando Fioretto
2024 A* conf
NEFTune: Noisy Embeddings Improve Instruction Finetuning.
ICLR
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 A conf
On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization.
WACV
Akshay Mehra, Yunbei Zhang, Bhavya Kailkhura, Jihun Hamm
2024 A* conf
Position: TrustLLM: Trustworthiness in Large Language Models.
ICML
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 — conf
Q-Hitter: A Better Token Oracle for Efficient LLM Inference via Sparse-Quantized KV Cache.
MLSys
Zhenyu Zhang, Shiwei Liu, Runjin Chen, Bhavya Kailkhura, Beidi Chen, Atlas Wang
2024 — conf
RankMean: Module-Level Importance Score for Merging Fine-tuned LLM Models.
ACL (Findings)
Gabriel J. Perin, Xuxi Chen, Shusen Liu, Bhavya Kailkhura, Zhangyang Wang, Brian Gallagher
2024 — conf
ReTA: Recursively Thinking Ahead to Improve the Strategic Reasoning of Large Language Models.
NAACL-HLT
Jinhao Duan, Shiqi Wang, James Diffenderfer, Lichao Sun, Tianlong Chen, Bhavya Kailkhura, Kaidi Xu
2024 A* conf
SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning.
EMNLP
Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, Sijia Liu
2024 J jnl
SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning.
CoRR
Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, Sijia Liu
2024 — conf
Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models.
ACL (1)
Jinhao Duan, Hao Cheng, Shiqi Wang, Alex Zavalny, Chenan Wang, Renjing Xu, Bhavya Kailkhura, Kaidi Xu
2024 J jnl
Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion.
CoRR
Jacob K. Christopher, Brian R. Bartoldson, Bhavya Kailkhura, Ferdinando Fioretto
2024 A* conf
Training Dynamics of Transformers to Recognize Word Co-occurrence via Gradient Flow Analysis.
NeurIPS
Hongru Yang, Bhavya Kailkhura, Zhangyang Wang, Yingbin Liang
2024 J jnl
Training Dynamics of Transformers to Recognize Word Co-occurrence via Gradient Flow Analysis.
CoRR
Hongru Yang, Bhavya Kailkhura, Zhangyang Wang, Yingbin Liang
2024 A* conf
Transformers Can Do Arithmetic with the Right Embeddings.
NeurIPS
Sean McLeish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein
2024 J jnl
Transformers Can Do Arithmetic with the Right Embeddings.
CoRR
Sean McLeish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein
2024 J jnl
TrustLLM: Trustworthiness in Large Language Models.
CoRR
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
2023 J jnl
An accelerated proximal algorithm for regularized nonconvex and nonsmooth bi-level optimization.
Mach. Learn.
Ziyi Chen, Bhavya Kailkhura, Yi Zhou
2023 J jnl
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities.
J. Mach. Learn. Res.
Brian R. Bartoldson, Bhavya Kailkhura, Davis W. Blalock
2023 J jnl
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training.
CoRR
Aochuan Chen, Yimeng Zhang, Jinghan Jia, James Diffenderfer, Jiancheng Liu, Konstantinos Parasyris, Yihua Zhang, Zheng Zhang, Bhavya Kailkhura, Sijia Liu
2023 J jnl
Gaining the Sparse Rewards by Exploring Binary Lottery Tickets in Spiking Neural Network.
CoRR
Hao Cheng, Jiahang Cao, Erjia Xiao, Pu Zhao, Mengshu Sun, Jiaxu Wang, Jize Zhang, Xue Lin, Bhavya Kailkhura, Kaidi Xu, Renjing Xu
2023 A conf
Improving Diversity with Adversarially Learned Transformations for Domain Generalization.
WACV
Tejas Gokhale, Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Chitta Baral, Yezhou Yang
2023 J jnl
Instance-wise Linearization of Neural Network for Model Interpretation.
CoRR
Zhimin Li, Shusen Liu, Bhavya Kailkhura, Timo Bremer, Valerio Pascucci
2023 — conf
Less is More: Data Pruning for Faster Adversarial Training.
SafeAI@AAAI
Yize Li, Pu Zhao, Xue Lin, Bhavya Kailkhura, Ryan A. Goldhahn
2023 J jnl
Less is More: Data Pruning for Faster Adversarial Training.
CoRR
Yize Li, Pu Zhao, Xue Lin, Bhavya Kailkhura, Ryan A. Goldhahn
2023 J jnl
Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation.
CoRR
Haizhong Zheng, Jiachen Sun, Shutong Wu, Bhavya Kailkhura, Zhuoqing Mao, Chaowei Xiao, Atul Prakash
2023 J jnl
NEFTune: Noisy Embeddings Improve Instruction Finetuning.
CoRR
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
2023 A* conf
Neural Image Compression: Generalization, Robustness, and Spectral Biases.
NeurIPS
Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura
2023 J jnl
Neural Image Compression: Generalization, Robustness, and Spectral Biases.
CoRR
Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura
2023 J jnl
On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization.
CoRR
Akshay Mehra, Yunbei Zhang, Bhavya Kailkhura, Jihun Hamm
2023 J jnl
Pursing the Sparse Limitation of Spiking Deep Learning Structures.
CoRR
Hao Cheng, Jiahang Cao, Erjia Xiao, Mengshu Sun, Le Yang, Jize Zhang, Xue Lin, Bhavya Kailkhura, Kaidi Xu, Renjing Xu
2023 J jnl
Scaling Compute Is Not All You Need for Adversarial Robustness.
CoRR
Edoardo Debenedetti, Zishen Wan, Maksym Andriushchenko, Vikash Sehwag, Kshitij Bhardwaj, Bhavya Kailkhura
2023 J jnl
Shifting Attention to Relevance: Towards the Uncertainty Estimation of Large Language Models.
CoRR
Jinhao Duan, Hao Cheng, Shiqi Wang, Chenan Wang, Alex Zavalny, Renjing Xu, Bhavya Kailkhura, Kaidi Xu
2023 J jnl
When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks.
CoRR
Gourav Datta, Zeyu Liu, James Diffenderfer, Bhavya Kailkhura, Peter A. Beerel
2022 J jnl
"Understanding Robustness Lottery": A Comparative Visual Analysis of Neural Network Pruning Approaches.
CoRR
Zhimin Li, Shusen Liu, Xin Yu, Bhavya Kailkhura, Jie Cao, James Daniel Diffenderfer, Peer-Timo Bremer, Valerio Pascucci
2022 J jnl
A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-level Optimization.
CoRR
Ziyi Chen, Bhavya Kailkhura, Yi Zhou
2022 — conf
A Spectral View of Randomized Smoothing Under Common Corruptions: Benchmarking and Improving Certified Robustness.
ECCV (4)
Jiachen Sun, Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Dan Hendrycks, Jihun Hamm, Z. Morley Mao
2022 J jnl
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions.
CoRR
Jiachen Sun, Qingzhao Zhang, Bhavya Kailkhura, Zhiding Yu, Chaowei Xiao, Z. Morley Mao
2022 B conf
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices.
ISPASS
Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura, Maya B. Gokhale
2022 J jnl
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices.
CoRR
Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura, Maya B. Gokhale
2022 A* conf
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks.
ICLR
Fan Wu, Linyi Li, Huan Zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, Bo Li
2022 J jnl
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks.
CoRR
Fan Wu, Linyi Li, Chejian Xu, Huan Zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, Bo Li
2022 J jnl
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities.
CoRR
Brian R. Bartoldson, Bhavya Kailkhura, Davis W. Blalock
2022 J jnl
Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed.
CoRR
Hao Cheng, Pu Zhao, Yize Li, Xue Lin, James Diffenderfer, Ryan A. Goldhahn, Bhavya Kailkhura
2022 J jnl
Enabling machine learning-ready HPC ensembles with Merlin.
Future Gener. Comput. Syst.
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
2022 A conf
Fault-Tolerant Deep Neural Networks for Processing-In-Memory based Autonomous Edge Systems.
DATE
Siyue Wang, Geng Yuan, Xiaolong Ma, Yanyu Li, Xue Lin, Bhavya Kailkhura
2022 J jnl
Improving Diversity with Adversarially Learned Transformations for Domain Generalization.
CoRR
Tejas Gokhale, Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Chitta Baral, Yezhou Yang
2022 A* conf
Models Out of Line: A Fourier Lens on Distribution Shift Robustness.
NeurIPS
Sara Fridovich-Keil, Brian R. Bartoldson, James Diffenderfer, Bhavya Kailkhura, Timo Bremer
2022 J jnl
Models Out of Line: A Fourier Lens on Distribution Shift Robustness.
CoRR
Sara Fridovich-Keil, Brian R. Bartoldson, James Diffenderfer, Bhavya Kailkhura, Peer-Timo Bremer
2022 — conf
More or Less (MoL): Defending against Multiple Perturbation Attacks on Deep Neural Networks through Model Ensemble and Compression.
WACV (Workshops)
Hao Cheng, Kaidi Xu, Zhengang Li, Pu Zhao, Chenan Wang, Xue Lin, Bhavya Kailkhura, Ryan A. Goldhahn
2022 J jnl
On Certifying and Improving Generalization to Unseen Domains.
CoRR
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
2022 A* conf
On the Certified Robustness for Ensemble Models and Beyond.
ICLR
Zhuolin Yang, Linyi Li, Xiaojun Xu, Bhavya Kailkhura, Tao Xie, Bo Li
2022 J jnl
Representing Polymers as Periodic Graphs with Learned Descriptors for Accurate Polymer Property Predictions.
CoRR
Evan R. Antoniuk, Peggy Li, Bhavya Kailkhura, Anna M. Hiszpanski
2022 J jnl
Representing Polymers as Periodic Graphs with Learned Descriptors for Accurate Polymer Property Predictions.
J. Chem. Inf. Model.
Evan R. Antoniuk, Peggy Li, Bhavya Kailkhura, Anna M. Hiszpanski
2022 J jnl
Robust Decentralized Learning Using ADMM With Unreliable Agents.
IEEE Trans. Signal Process.
Qunwei Li, Bhavya Kailkhura, Ryan A. Goldhahn, Priyadip Ray, Pramod K. Varshney
2022 A conf
Unsupervised Test-Time Adaptation of Deep Neural Networks at the Edge: A Case Study.
DATE
Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura, Maya B. Gokhale
2022 J jnl
Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning.
CoRR
Ioannis C. Tsaknakis, Bhavya Kailkhura, Sijia Liu, Donald Loveland, James Diffenderfer, Anna Maria Hiszpanski, Mingyi Hong
2021 J jnl
A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness.
CoRR
James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura
2021 A* conf
A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness.
NeurIPS
James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura
2021 A* conf
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations.
AAAI
Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Chitta Baral, Yezhou Yang
2021 A* conf
Can Shape Structure Features Improve Model Robustness under Diverse Adversarial Settings?
ICCV
Mingjie Sun, Zichao Li, Chaowei Xiao, Haonan Qiu, Bhavya Kailkhura, Mingyan Liu, Bo Li
2021 B conf
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing.
MASS
Cheng Chen, Bhavya Kailkhura, Ryan A. Goldhahn, Yi Zhou
2021 J jnl
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing.
CoRR
Cheng Chen, Bhavya Kailkhura, Ryan A. Goldhahn, Yi Zhou
2021 J jnl
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines.
CoRR
Jiachen Sun, Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Dan Hendrycks, Jihun Hamm, Z. Morley Mao
2021 J jnl
Coverage-Based Designs Improve Sample Mining and Hyperparameter Optimization.
IEEE Trans. Neural Networks Learn. Syst.
Gowtham Muniraju, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Cihan Tepedelenlioglu, Andreas Spanias
2021 A conf
Deep kernels with probabilistic embeddings for small-data learning.
UAI
Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han
2021 J jnl
Editorial: Safe and Trustworthy Machine Learning.
Frontiers Big Data
Bhavya Kailkhura, Pin-Yu Chen, Xue Lin, Bo Li
2021 A* conf
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators.
NeurIPS
Yunhui Long, Boxin Wang, Zhuolin Yang, Bhavya Kailkhura, Aston Zhang, Carl A. Gunter, Bo Li
2021 A* conf
How Robust Are Randomized Smoothing Based Defenses to Data Poisoning?
CVPR
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
2021 J jnl
MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data.
SIAM J. Math. Data Sci.
Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Jize Zhang, Yi Zhou, Yingbin Liang, Thomas Yong-Jin Han, Pramod K. Varshney
2021 J jnl
Mixture of Robust Experts (MoRE): A Flexible Defense Against Multiple Perturbations.
CoRR
Hao Cheng, Kaidi Xu, Chenan Wang, Xue Lin, Bhavya Kailkhura, Ryan A. Goldhahn
2021 A* conf
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network.
ICLR
James Diffenderfer, Bhavya Kailkhura
2021 J jnl
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network.
CoRR
James Diffenderfer, Bhavya Kailkhura
2021 J jnl
On the Certified Robustness for Ensemble Models and Beyond.
CoRR
Zhuolin Yang, Linyi Li, Xiaojun Xu, Bhavya Kailkhura, Tao Xie, Bo Li
2021 J jnl
Preventing Failures by Dataset Shift Detection in Safety-Critical Graph Applications.
Frontiers Artif. Intell.
Hoseung Song, Jayaraman J. Thiagarajan, Bhavya Kailkhura
2021 J jnl
Reliable Graph Neural Network Explanations Through Adversarial Training.
CoRR
Donald Loveland, Shusen Liu, Bhavya Kailkhura, Anna M. Hiszpanski, Yong Han
2021 J jnl
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning.
CoRR
Xiaoyang Wang, Bo Li, Yibo Zhang, Bhavya Kailkhura, Klara Nahrstedt
2021 A* conf
Scalability vs. Utility: Do We Have To Sacrifice One for the Other in Data Importance Quantification?
CVPR
Ruoxi Jia, Fan Wu, Xuehui Sun, Jiacen Xu, David Dao, Bhavya Kailkhura, Ce Zhang, Bo Li, Dawn Song
2021 A* conf
TSS: Transformation-Specific Smoothing for Robustness Certification.
CCS
Linyi Li, Maurice Weber, Xiaojun Xu, Luka Rimanic, Bhavya Kailkhura, Tao Xie, Ce Zhang, Bo Li
2021 A* conf
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning.
NeurIPS
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
2021 J jnl
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning.
CoRR
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
2020 J jnl
A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning.
CoRR
Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura, Gaoyuan Zhang, Alfred O. Hero III, Pramod K. Varshney
2020 J jnl
A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning: Principals, Recent Advances, and Applications.
IEEE Signal Process. Mag.
Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura, Gaoyuan Zhang, Alfred O. Hero III, Pramod K. Varshney
2020 A* conf
A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning.
NeurIPS
Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Jize Zhang, Yi Zhou, Timo Bremer
2020 J jnl
Actionable Attribution Maps for Scientific Machine Learning.
CoRR
Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Thomas Yong-Jin Han
2020 A* conf
Adversarial Mutual Information for Text Generation.
ICML
Boyuan Pan, Yazheng Yang, Kaizhao Liang, Bhavya Kailkhura, Zhongming Jin, Xian-Sheng Hua, Deng Cai, Bo Li
2020 J jnl
Adversarial Mutual Information for Text Generation.
CoRR
Boyuan Pan, Yazheng Yang, Kaizhao Liang, Bhavya Kailkhura, Zhongming Jin, Xian-Sheng Hua, Deng Cai, Bo Li
2020 J jnl
Anomalous Example Detection in Deep Learning: A Survey.
IEEE Access
Saikiran Bulusu, Bhavya Kailkhura, Bo Li, Pramod K. Varshney, Dawn Song
2020 J jnl
Anomalous Instance Detection in Deep Learning: A Survey.
CoRR
Saikiran Bulusu, Bhavya Kailkhura, Bo Li, Pramod K. Varshney, Dawn Song
2020 J jnl
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations.
CoRR
Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Chitta Baral, Yezhou Yang
2020 J jnl
Automated Identification of Molecular Crystals' Packing Motifs.
J. Chem. Inf. Model.
Donald Loveland, Bhavya Kailkhura, Piyush Karande, Anna M. Hiszpanski, Thomas Yong-Jin Han
2020 A* conf
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond.
NeurIPS
Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh
2020 J jnl
Automatic Perturbation Analysis on General Computational Graphs.
CoRR
Kaidi Xu, Zhouxing Shi, Huan Zhang, Minlie Huang, Kai-Wei Chang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh
2020 J jnl
Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design.
CoRR
Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Thomas Yong-Jin Han
2020 — conf
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling.
IEEE BigData
Cheng Chen, Ziyi Chen, Yi Zhou, Bhavya Kailkhura
2020 J jnl
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling.
CoRR
Cheng Chen, Ziyi Chen, Yi Zhou, Bhavya Kailkhura
2020 J jnl
How Robust are Randomized Smoothing based Defenses to Data Poisoning?
CoRR
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
2020 J jnl
Leveraging Uncertainty from Deep Learning for Trustworthy Materials Discovery Workflows.
CoRR
Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han
2020 J jnl
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking.
Int. J. Comput. Vis.
Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Peer-Timo Bremer
2020 A* conf
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning.
ICML
Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han
2020 J jnl
Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning.
CoRR
Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han
2020 J jnl
Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge.
J. Chem. Inf. Model.
Anna M. Hiszpanski, Brian Gallagher, Karthik Chellappan, Peggy Li, Shusen Liu, Hyojin Kim, Jinkyu Han, Bhavya Kailkhura, David J. Buttler, Thomas Yong-Jin Han
2020 J jnl
Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning.
CoRR
Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han
2020 Misc conf
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks.
ICASSP
Kaidi Xu, Sijia Liu, Pin-Yu Chen, Mengshu Sun, Caiwen Ding, Bhavya Kailkhura, Xue Lin
2020 J jnl
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks.
CoRR
Kaidi Xu, Sijia Liu, Pin-Yu Chen, Mengshu Sun, Caiwen Ding, Bhavya Kailkhura, Xue Lin
2020 Misc conf
Treeview and Disentangled Representations for Explaining Deep Neural Networks Decisions.
ACSSC
Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Bhavya Kailkhura
2019 J jnl
A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis.
CoRR
Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Peer-Timo Bremer
2019 J jnl
Deep Probabilistic Kernels for Sample-Efficient Learning.
CoRR
Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han
2019 — conf
Generative Counterfactual Introspection for Explainable Deep Learning.
GlobalSIP
Shusen Liu, Bhavya Kailkhura, Donald Loveland, Yong Han
2019 J jnl
Generative Counterfactual Introspection for Explainable Deep Learning.
CoRR
Shusen Liu, Bhavya Kailkhura, Donald Loveland, Yong Han
2019 J jnl
Joint Sparsity Pattern Recovery With 1-b Compressive Sensing in Distributed Sensor Networks.
IEEE Trans. Signal Inf. Process. over Networks
Swatantra Kafle, Vipul Gupta, Bhavya Kailkhura, Thakshila Wimalajeewa, Pramod K. Varshney
2019 J jnl
Merlin: Enabling Machine Learning-Ready HPC Ensembles.
CoRR
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
2019 J jnl
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking.
CoRR
Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Timo Bremer
2019 A* conf
On the Design of Black-Box Adversarial Examples by Leveraging Gradient-Free Optimization and Operator Splitting Method.
ICCV
Pu Zhao, Sijia Liu, Pin-Yu Chen, Nghia Hoang, Kaidi Xu, Bhavya Kailkhura, Xue Lin
2019 J jnl
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method.
CoRR
Pu Zhao, Sijia Liu, Pin-Yu Chen, Nghia Hoang, Kaidi Xu, Bhavya Kailkhura, Xue Lin
2019 J jnl
Secure Distributed Detection of Sparse Signals via Falsification of Local Compressive Measurements.
IEEE Trans. Signal Process.
Chengxi Li, Gang Li, Bhavya Kailkhura, Pramod K. Varshney
2018 J jnl
A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms.
J. Mach. Learn. Res.
Bhavya Kailkhura, Jayaraman J. Thiagarajan, Charvi Rastogi, Pramod K. Varshney, Peer-Timo Bremer
2018 J jnl
An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks.
CoRR
Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Timo Bremer
2018 J jnl
Byzantine-Resilient Locally Optimum Detection Using Collaborative Autonomous Networks.
CoRR
Bhavya Kailkhura, Priyadip Ray, Deepak Rajan, Anton Yen, Peter D. Barnes Jr., Ryan A. Goldhahn
2018 J jnl
Controlled Random Search Improves Sample Mining and Hyper-Parameter Optimization.
CoRR
Gowtham Muniraju, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer
2018 J jnl
Human-Machine Inference Networks For Smart Decision Making: Opportunities and Challenges.
CoRR
Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney
2018 Misc conf
Human-Machine Inference Networks for Smart Decision Making: Opportunities and Challenges.
ICASSP
Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney
2018 J jnl
MR-GAN: Manifold Regularized Generative Adversarial Networks.
CoRR
Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Yi Zhou, Yingbin Liang, Pramod K. Varshney
2018 J jnl
MimicGAN: Corruption-Mimicking for Blind Image Recovery & Adversarial Defense.
CoRR
Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Timo Bremer
2018 A conf
PADDLE: Performance Analysis Using a Data-Driven Learning Environment.
IPDPS
Jayaraman J. Thiagarajan, Rushil Anirudh, Bhavya Kailkhura, Nikhil Jain, Tanzima Z. Islam, Abhinav Bhatele, Jae-Seung Yeom, Todd Gamblin
2018 — book
Secure Networked Inference with Unreliable Data Sources
Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney
2018 J jnl
Universal Decision-Based Black-Box Perturbations: Breaking Security-Through-Obscurity Defenses.
CoRR
Thomas A. Hogan, Bhavya Kailkhura
2018 A* conf
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization.
NeurIPS
Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen, Pai-Shun Ting, Shiyu Chang, Lisa Amini
2018 J jnl
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization.
CoRR
Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen, Pai-Shun Ting, Shiyu Chang, Lisa Amini
2017 J jnl
A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms.
CoRR
Bhavya Kailkhura, Jayaraman J. Thiagarajan, Charvi Rastogi, Pramod K. Varshney, Peer-Timo Bremer
2017 — conf
Byzantine-Resilient locally optimum detection using collaborative autonomous networks.
CAMSAP
Bhavya Kailkhura, Priyadip Ray, Deepak Rajan, Anton Yen, Peter D. Barnes Jr., Ryan A. Goldhahn
2017 J jnl
Collaborative Compressive Detection With Physical Layer Secrecy Constraints.
IEEE Trans. Signal Process.
Bhavya Kailkhura, Thakshila Wimalajeewa, Pramod K. Varshney
2017 J jnl
Data Falsification Attacks on Consensus-Based Detection Systems.
IEEE Trans. Signal Inf. Process. over Networks
Bhavya Kailkhura, Swastik Brahma, Pramod K. Varshney
2017 A conf
Performance modeling under resource constraints using deep transfer learning.
SC
Aniruddha Marathe, Rushil Anirudh, Nikhil Jain, Abhinav Bhatele, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Jae-Seung Yeom, Barry Rountree, Todd Gamblin
2017 — conf
Poisson Disk Sampling on the Grassmannnian: Applications in Subspace Optimization.
CVPR Workshops
Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer
2017 J jnl
Robust Federated Learning Using ADMM in the Presence of Data Falsifying Byzantines.
CoRR
Qunwei Li, Bhavya Kailkhura, Ryan A. Goldhahn, Priyadip Ray, Pramod K. Varshney
2017 J jnl
Subspace-Aware Index Codes.
CoRR
Bhavya Kailkhura, Lakshmi Narasimhan Theagarajan, Pramod K. Varshney
2017 J jnl
Subspace-Aware Index Codes.
IEEE Wirel. Commun. Lett.
Bhavya Kailkhura, Lakshmi Narasimhan Theagarajan, Pramod K. Varshney
2016 — conf
Decentralized joint sparsity pattern recovery using 1-bit compressive sensing.
GlobalSIP
Swatantra Kafle, Bhavya Kailkhura, Thakshila Wimalajeewa, Pramod K. Varshney
2016 — conf
Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models.
NIPS Time Series Workshop
Qunwei Li, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Zhenliang Zhang, Pramod K. Varshney
2016 J jnl
Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models.
CoRR
Qunwei Li, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Zhenliang Zhang, Pramod K. Varshney
2016 J jnl
Measurement Matrix Design for Compressed Detection With Secrecy Guarantees.
IEEE Wirel. Commun. Lett.
Bhavya Kailkhura, Sijia Liu, Thakshila Wimalajeewa, Pramod K. Varshney
2016 — conf
Robust Local Scaling Using Conditional Quantiles of Graph Similarities.
ICDM Workshops
Jayaraman J. Thiagarajan, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Bhavya Kailkhura
2016 J jnl
Stair blue noise sampling.
ACM Trans. Graph.
Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Pramod K. Varshney
2016 Misc conf
Theoretical guarantees for poisson disk sampling using pair correlation function.
ICASSP
Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Pramod K. Varshney
2016 J jnl
TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning.
CoRR
Jayaraman J. Thiagarajan, Bhavya Kailkhura, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy
2016 J jnl
Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach.
CoRR
Prashant Khanduri, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Pramod K. Varshney
2016 J jnl
Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach.
IEEE Signal Process. Lett.
Prashant Khanduri, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Pramod K. Varshney
2015 J jnl
Asymptotic Analysis of Distributed Bayesian Detection with Byzantine Data.
IEEE Signal Process. Lett.
Bhavya Kailkhura, Yunghsiang Sam Han, Swastik Brahma, Pramod K. Varshney
2015 J jnl
Collaborative Compressive Detection with Physical Layer Secrecy Constraints.
CoRR
Bhavya Kailkhura, Thakshila Wimalajeewa, Pramod K. Varshney
2015 J jnl
Consensus based Detection in the Presence of Data Falsification Attacks.
CoRR
Bhavya Kailkhura, Swastik Brahma, Pramod K. Varshney
2015 J jnl
Distributed Bayesian Detection in the Presence of Byzantine Data.
IEEE Trans. Signal Process.
Bhavya Kailkhura, Yunghsiang Sam Han, Swastik Brahma, Pramod K. Varshney
2015 J jnl
Distributed Inference in Tree Networks Using Coding Theory.
IEEE Trans. Signal Process.
Bhavya Kailkhura, Aditya Vempaty, Pramod K. Varshney
2015 J jnl
Distributed Inference in the Presence of Eavesdroppers: A Survey.
CoRR
Bhavya Kailkhura, V. Sriram Siddhardh Nadendla, Pramod K. Varshney
2015 J jnl
Distributed inference in the presence of eavesdroppers: a survey.
IEEE Commun. Mag.
Bhavya Kailkhura, V. Sriram Siddhardh Nadendla, Pramod K. Varshney
2015 J jnl
Joint Sparsity Pattern Recovery with 1-bit Compressive Sensing in Sensor Networks.
CoRR
Vipul Gupta, Bhavya Kailkhura, Thakshila Wimalajeewa, Pramod K. Varshney
2015 Misc conf
Joint sparsity pattern recovery with 1-bit compressive sensing in sensor networks.
ACSSC
Vipul Gupta, Bhavya Kailkhura, Thakshila Wimalajeewa, Sijia Liu, Pramod K. Varshney
2015 J jnl
Measurement Matrix Design for Compressive Detection with Secrecy Guarantees.
CoRR
Bhavya Kailkhura, Sijia Liu, Thakshila Wimalajeewa, Pramod K. Varshney
2014 J jnl
Asymptotic Analysis of Distributed Bayesian Detection with Byzantine Data.
CoRR
Bhavya Kailkhura, Yunghsiang S. Han, Swastik Brahma, Pramod K. Varshney
2014 B conf
Distributed Compressive Detection with Perfect Secrecy.
MASS
Bhavya Kailkhura, Thakshila Wimalajeewa, Lixin Shen, Pramod K. Varshney
2014 J jnl
Distributed Detection in Tree Networks: Byzantines and Mitigation Techniques.
CoRR
Bhavya Kailkhura, Swastik Brahma, Berkan Dulek, Yunghsiang S. Han, Pramod K. Varshney
2014 J jnl
Distributed Detection in Tree Topologies With Byzantines.
IEEE Trans. Signal Process.
Bhavya Kailkhura, Swastik Brahma, Yunghsiang S. Han, Pramod K. Varshney
2014 J jnl
Distributed Inference in Tree Networks using Coding Theory.
CoRR
Bhavya Kailkhura, Aditya Vempaty, Pramod K. Varshney
2014 Misc conf
On physical layer secrecy of collaborative compressive detection.
ACSSC
Bhavya Kailkhura, Thakshila Wimalajeewa, Pramod K. Varshney
2014 Misc conf
On the performance analysis of data fusion schemes with Byzantines.
ICASSP
Bhavya Kailkhura, Swastik Brahma, Pramod K. Varshney
2013 J jnl
Distributed Bayesian Detection with Byzantine Data.
CoRR
Bhavya Kailkhura, Yunghsiang S. Han, Swastik Brahma, Pramod K. Varshney
2013 J jnl
Distributed Detection in Tree Topologies with Byzantines.
CoRR
Bhavya Kailkhura, Swastik Brahma, Yunghsiang S. Han, Pramod K. Varshney
2013 Misc conf
Optimal Byzantine attacks on distributed detection in tree-based topologies.
ICNC
Bhavya Kailkhura, Swastik Brahma, Pramod K. Varshney
2013 Misc conf
Optimal distributed detection in the presence of Byzantines.
ICASSP
Bhavya Kailkhura, Swastik Brahma, Yunghsiang S. Han, Pramod K. Varshney