Bhavya Kailkhura

ORCID

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 146

Top Venues

CoRR

NeurIPS

IEEE Trans. Signal Process.

ICLR

Publications

Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies. CoRR (2024)
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 (2024)
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)
Gabriel 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)
Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, Yi Zhou
End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver. CoRR (2024)
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)
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)
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, Chao Zhang, 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 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 (2024)
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 (2024)
Akshay Mehra, Yunbei Zhang, Bhavya Kailkhura, Jihun Hamm
On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization. WACV (2024)
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 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 (2024)
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)
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)
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 (2024)
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 (2024)
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 (2024)
Saswat Das, Marco Romanelli, Cuong Tran, Zarreen Reza, Bhavya Kailkhura, Ferdinando Fioretto
Low-rank finetuning for LLMs: A fairness perspective. CoRR (2024)
Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura
Neural Image Compression: Generalization, Robustness, and Spectral Biases. CoRR (2023)
Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura
Neural Image Compression: Generalization, Robustness, and Spectral Biases. NeurIPS (2023)
Akshay Mehra, Yunbei Zhang, Bhavya Kailkhura, Jihun Hamm
On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization. CoRR (2023)
Haizhong Zheng, Jiachen Sun, Shutong Wu, Bhavya Kailkhura, Zhuoqing Mao, Chaowei Xiao, Atul Prakash
Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation. CoRR (2023)
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 (2023)
Tejas Gokhale, Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Chitta Baral, Yezhou Yang
Improving Diversity with Adversarially Learned Transformations for Domain Generalization. WACV (2023)
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 (2023)
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 (2023)
Yize Li, Pu Zhao, Xue Lin, Bhavya Kailkhura, Ryan Goldh
Less is More: Data Pruning for Faster Adversarial Training. CoRR (2023)
Zhimin Li, Shusen Liu, Bhavya Kailkhura, Timo Bremer, Valerio Pascucci
Instance-wise Linearization of Neural Network for Model Interpretation. CoRR (2023)
Edoardo Debenedetti, Zishen Wan, Maksym Andriushchenko, Vikash Sehwag, Kshitij Bhardwaj, Bhavya Kailkhura
Scaling Compute Is Not All You Need for Adversarial Robustness. CoRR (2023)
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 (2023)
Yize Li, Pu Zhao, Xue Lin, Bhavya Kailkhura, Ryan A. Goldhahn
Less is More: Data Pruning for Faster Adversarial Training. SafeAI@AAAI (2023)
Ziyi Chen, Bhavya Kailkhura, Yi Zhou
An accelerated proximal algorithm for regularized nonconvex and nonsmooth bi-level optimization. Mach. Learn. 112 (5) (2023)
Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities. J. Mach. Learn. Res. 24 (2023)
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 (2023)
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 (2023)
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)
Zhuolin Yang, Linyi Li, Xiaojun Xu, Bhavya Kailkhura, Tao Xie, Bo Li
On the Certified Robustness for Ensemble Models and Beyond. ICLR (2022)
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)
Evan R. Antoniuk, Peggy Li, Bhavya Kailkhura, Anna M. Hiszpanski
Representing Polymers as Periodic Graphs with Learned Descriptors for Accurate Polymer Property Predictions. CoRR (2022)
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)
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 (2022)
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)
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
On Certifying and Improving Generalization to Unseen Domains. CoRR (2022)
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)
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 (2022)
Ziyi Chen, Bhavya Kailkhura, Yi Zhou
A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-level Optimization. CoRR (2022)
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) (2022)
Jiachen Sun, Qingzhao Zhang, Bhavya Kailkhura, Zhiding Yu, Chaowei Xiao, Z. Morley Mao
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions. CoRR (2022)
Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura, Maya B. Gokhale
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices. ISPASS (2022)
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 (2022)
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 (2022)
Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities. CoRR (2022)
Tejas Gokhale, Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Chitta Baral, Yezhou Yang
Improving Diversity with Adversarially Learned Transformations for Domain Generalization. CoRR (2022)
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 (2022)
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)
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 (2022)
Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura, Maya B. Gokhale
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices. CoRR (2022)
Cheng Chen, Bhavya Kailkhura, Ryan A. Goldhahn, Yi Zhou
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing. CoRR (2021)
James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura
A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness. CoRR (2021)
Xiaoyang Wang, Bo Li, Yibo Zhang, Bhavya Kailkhura, Klara Nahrstedt
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning. CoRR (2021)
James Diffenderfer, Bhavya Kailkhura
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network. CoRR (2021)
Zhuolin Yang, Linyi Li, Xiaojun Xu, Bhavya Kailkhura, Tao Xie, Bo Li
On the Certified Robustness for Ensemble Models and Beyond. CoRR (2021)
Bhavya Kailkhura, Pin-Yu Chen, Xue Lin, Bo Li
Editorial: Safe and Trustworthy Machine Learning. Frontiers Big Data 4 (2021)
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 (2021)
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)
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) (2021)
Hoseung Song, Jayaraman J. Thiagarajan, Bhavya Kailkhura
Preventing Failures by Dataset Shift Detection in Safety-Critical Graph Applications. Frontiers Artif. Intell. 4 (2021)
Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Chitta Baral, Yezhou Yang
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations. AAAI (2021)
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) (2021)
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning. NeurIPS (2021)
Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han
Deep kernels with probabilistic embeddings for small-data learning. UAI (2021)
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)
James Diffenderfer, Bhavya Kailkhura
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network. ICLR (2021)
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
How Robust Are Randomized Smoothing Based Defenses to Data Poisoning? CVPR (2021)
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)
Cheng Chen, Bhavya Kailkhura, Ryan A. Goldhahn, Yi Zhou
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing. MASS (2021)
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning. CoRR (2021)
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 (2021)
Donald Loveland, Shusen Liu, Bhavya Kailkhura, Anna M. Hiszpanski, Yong Han
Reliable Graph Neural Network Explanations Through Adversarial Training. CoRR (2021)
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)
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)
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 (2020)
Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han
Leveraging Uncertainty from Deep Learning for Trustworthy Materials Discovery Workflows. CoRR (2020)
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) (2020)
Boyuan Pan, Yazheng Yang, Kaizhao Liang, Bhavya Kailkhura, Zhongming Jin, Xian-Sheng Hua, Deng Cai, Bo Li
Adversarial Mutual Information for Text Generation. CoRR (2020)
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)
Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning. ICML (2020)
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)
Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han
Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning. CoRR (2020)
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
How Robust are Randomized Smoothing based Defenses to Data Poisoning? CoRR (2020)
Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Chitta Baral, Yezhou Yang
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations. CoRR (2020)
Saikiran Bulusu, Bhavya Kailkhura, Bo Li, Pramod K. Varshney, Dawn Song
Anomalous Instance Detection in Deep Learning: A Survey. CoRR (2020)
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 (2020)
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) (2020)
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 (2020)
Cheng Chen, Ziyi Chen, Yi Zhou, Bhavya Kailkhura
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling. IEEE BigData (2020)
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)
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) (2020)
Cheng Chen, Ziyi Chen, Yi Zhou, Bhavya Kailkhura
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling. CoRR (2020)
Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, Thomas Yong-Jin Han
Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning. CoRR (2020)
Saikiran Bulusu, Bhavya Kailkhura, Bo Li, Pramod K. Varshney, Dawn Song
Anomalous Example Detection in Deep Learning: A Survey. IEEE Access 8 (2020)

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Bhavya Kailkhura