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2020 – today
- 2023
[j28]Brian R. Bartoldson, Bhavya Kailkhura, Davis 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) - [c46]Yize Li, Pu Zhao, Xue Lin, Bhavya Kailkhura, Ryan A. Goldhahn:
Less is More: Data Pruning for Faster Adversarial Training. SafeAI@AAAI 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 - [i68]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) - [i67]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) - [i66]Akshay Mehra, Yunbei Zhang, Bhavya Kailkhura, Jihun Hamm:
On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization. CoRR abs/2307.08551 (2023) - [i65]Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura:
Neural Image Compression: Generalization, Robustness, and Spectral Biases. CoRR abs/2307.08657 (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 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]