Optimal distributed detection in the presence of Byzantines. - Bhavya Kailkhura; Swastik Brahma; Yunghsiang S. Han; Pramod K. Varshney (ICASSP, 2013)

Optimal Byzantine attacks on distributed detection in tree-based topologies. - Bhavya Kailkhura; Swastik Brahma; Pramod K. Varshney (ICNC, 2013)

Distributed Bayesian Detection with Byzantine Data. - Bhavya Kailkhura; Yunghsiang S. Han; Swastik Brahma; Pramod K. Varshney (CoRR, 2013)

On physical layer secrecy of collaborative compressive detection. - Bhavya Kailkhura; Thakshila Wimalajeewa; Pramod K. Varshney (ACSSC, 2014)

On the performance analysis of data fusion schemes with Byzantines. - Bhavya Kailkhura; Swastik Brahma; Pramod K. Varshney (ICASSP, 2014)

Distributed Compressive Detection with Perfect Secrecy. - Bhavya Kailkhura; Thakshila Wimalajeewa; Lixin Shen; Pramod K. Varshney (MASS, 2014)

Distributed Detection in Tree Networks: Byzantines and Mitigation Techniques. - Bhavya Kailkhura; Swastik Brahma; Berkan Dulek; Yunghsiang S. Han; Pramod K. Varshney (CoRR, 2014)

Distributed Detection in Tree Topologies With Byzantines. - Bhavya Kailkhura; Swastik Brahma; Yunghsiang S. Han; Pramod K. Varshney (IEEE Trans. Signal Process., 2014)

Joint sparsity pattern recovery with 1-bit compressive sensing in sensor networks. - Vipul Gupta; Bhavya Kailkhura; Thakshila Wimalajeewa; Sijia Liu 001; Pramod K. Varshney (ACSSC, 2015)

Distributed inference in the presence of eavesdroppers: a survey. - Bhavya Kailkhura; V. Sriram Siddhardh Nadendla; Pramod K. Varshney (IEEE Commun. Mag., 2015)

Joint Sparsity Pattern Recovery with 1-bit Compressive Sensing in Sensor Networks. - Vipul Gupta; Bhavya Kailkhura; Thakshila Wimalajeewa; Pramod K. Varshney (CoRR, 2015)

Consensus based Detection in the Presence of Data Falsification Attacks. - Bhavya Kailkhura; Swastik Brahma; Pramod K. Varshney (CoRR, 2015)

Measurement Matrix Design for Compressive Detection with Secrecy Guarantees. - Bhavya Kailkhura; Sijia Liu 001; Thakshila Wimalajeewa; Pramod K. Varshney (CoRR, 2015)

Asymptotic Analysis of Distributed Bayesian Detection with Byzantine Data. - Bhavya Kailkhura; Yunghsiang Sam Han; Swastik Brahma; Pramod K. Varshney (IEEE Signal Process. Lett., 2015)

Distributed Bayesian Detection in the Presence of Byzantine Data. - Bhavya Kailkhura; Yunghsiang Sam Han; Swastik Brahma; Pramod K. Varshney (IEEE Trans. Signal Process., 2015)

Distributed Inference in Tree Networks Using Coding Theory. - Bhavya Kailkhura; Aditya Vempaty; Pramod K. Varshney (IEEE Trans. Signal Process., 2015)

Decentralized joint sparsity pattern recovery using 1-bit compressive sensing. - Swatantra Kafle; Bhavya Kailkhura; Thakshila Wimalajeewa; Pramod K. Varshney (GlobalSIP, 2016)

Theoretical guarantees for poisson disk sampling using pair correlation function. - Bhavya Kailkhura; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Pramod K. Varshney (ICASSP, 2016)

Robust Local Scaling Using Conditional Quantiles of Graph Similarities. - Jayaraman J. Thiagarajan; Prasanna Sattigeri; Karthikeyan Natesan Ramamurthy; Bhavya Kailkhura (ICDM Workshops, 2016)

Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models. - Qunwei Li; Bhavya Kailkhura; Jayaraman J. Thiagarajan; Zhenliang Zhang; Pramod K. Varshney (NIPS Time Series Workshop, 2016)

TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning. - Jayaraman J. Thiagarajan; Bhavya Kailkhura; Prasanna Sattigeri; Karthikeyan Natesan Ramamurthy (CoRR, 2016)

Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach. - Prashant Khanduri; Bhavya Kailkhura; Jayaraman J. Thiagarajan; Pramod K. Varshney (IEEE Signal Process. Lett., 2016)

Stair blue noise sampling. - Bhavya Kailkhura; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Pramod K. Varshney (ACM Trans. Graph., 2016)

Measurement Matrix Design for Compressed Detection With Secrecy Guarantees. - Bhavya Kailkhura; Sijia Liu 001; Thakshila Wimalajeewa; Pramod K. Varshney (IEEE Wirel. Commun. Lett., 2016)

Byzantine-Resilient locally optimum detection using collaborative autonomous networks. - Bhavya Kailkhura; Priyadip Ray; Deepak Rajan; Anton Yen; Peter D. Barnes Jr.; Ryan A. Goldhahn (CAMSAP, 2017)

Poisson Disk Sampling on the Grassmannnian: Applications in Subspace Optimization. - Rushil Anirudh; Bhavya Kailkhura; Jayaraman J. Thiagarajan; Peer-Timo Bremer (CVPR Workshops, 2017)

Performance modeling under resource constraints using deep transfer learning. - Aniruddha Marathe; Rushil Anirudh; Nikhil Jain; Abhinav Bhatele; Jayaraman J. Thiagarajan; Bhavya Kailkhura; Jae-Seung Yeom; Barry Rountree; Todd Gamblin (SC, 2017)

Robust Federated Learning Using ADMM in the Presence of Data Falsifying Byzantines. - Qunwei Li; Bhavya Kailkhura; Ryan A. Goldhahn; Priyadip Ray; Pramod K. Varshney (CoRR, 2017)

Data Falsification Attacks on Consensus-Based Detection Systems. - Bhavya Kailkhura; Swastik Brahma; Pramod K. Varshney (IEEE Trans. Signal Inf. Process. over Networks, 2017)

Collaborative Compressive Detection With Physical Layer Secrecy Constraints. - Bhavya Kailkhura; Thakshila Wimalajeewa; Pramod K. Varshney (IEEE Trans. Signal Process., 2017)

Subspace-Aware Index Codes. - Bhavya Kailkhura; Lakshmi Narasimhan Theagarajan; Pramod K. Varshney (IEEE Wirel. Commun. Lett., 2017)

Secure Networked Inference with Unreliable Data Sources - Aditya Vempaty; Bhavya Kailkhura; Pramod K. Varshney (Springer, 2018)

Human-Machine Inference Networks for Smart Decision Making: Opportunities and Challenges. - Aditya Vempaty; Bhavya Kailkhura; Pramod K. Varshney (ICASSP, 2018)

PADDLE: Performance Analysis Using a Data-Driven Learning Environment. - Jayaraman J. Thiagarajan; Rushil Anirudh; Bhavya Kailkhura; Nikhil Jain; Tanzima Z. Islam; Abhinav Bhatele; Jae-Seung Yeom; Todd Gamblin (IPDPS, 2018)

Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization. - Sijia Liu 001; Bhavya Kailkhura; Pin-Yu Chen; Pai-Shun Ting; Shiyu Chang; Lisa Amini (NeurIPS, 2018)

An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks. - Rushil Anirudh; Jayaraman J. Thiagarajan; Bhavya Kailkhura; Timo Bremer (CoRR, 2018)

Controlled Random Search Improves Sample Mining and Hyper-Parameter Optimization. - Gowtham Muniraju; Bhavya Kailkhura; Jayaraman J. Thiagarajan; Peer-Timo Bremer (CoRR, 2018)

Universal Decision-Based Black-Box Perturbations: Breaking Security-Through-Obscurity Defenses. - Thomas A. Hogan; Bhavya Kailkhura (CoRR, 2018)

MimicGAN: Corruption-Mimicking for Blind Image Recovery & Adversarial Defense. - Rushil Anirudh; Jayaraman J. Thiagarajan; Bhavya Kailkhura; Timo Bremer (CoRR, 2018)

MR-GAN: Manifold Regularized Generative Adversarial Networks. - Qunwei Li; Bhavya Kailkhura; Rushil Anirudh; Yi Zhou 017; Yingbin Liang; Pramod K. Varshney (CoRR, 2018)

A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms. - Bhavya Kailkhura; Jayaraman J. Thiagarajan; Charvi Rastogi; Pramod K. Varshney; Peer-Timo Bremer (J. Mach. Learn. Res., 2018)

Generative Counterfactual Introspection for Explainable Deep Learning. - Shusen Liu 001; Bhavya Kailkhura; Donald Loveland; Yong Han (GlobalSIP, 2019)

On the Design of Black-Box Adversarial Examples by Leveraging Gradient-Free Optimization and Operator Splitting Method. - Pu Zhao 001; Sijia Liu 001; Pin-Yu Chen; Nghia Hoang; Kaidi Xu; Bhavya Kailkhura; Xue Lin 001 (ICCV, 2019)

A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis. - Bhavya Kailkhura; Jayaraman J. Thiagarajan; Qunwei Li; Peer-Timo Bremer (CoRR, 2019)

Deep Probabilistic Kernels for Sample-Efficient Learning. - Ankur Mallick; Chaitanya Dwivedi; Bhavya Kailkhura; Gauri Joshi; Thomas Yong-Jin Han (CoRR, 2019)

Merlin: Enabling Machine Learning-Ready HPC Ensembles. - 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; et al. (CoRR, 2019)

Joint Sparsity Pattern Recovery With 1-b Compressive Sensing in Distributed Sensor Networks. - Swatantra Kafle; Vipul Gupta; Bhavya Kailkhura; Thakshila Wimalajeewa; Pramod K. Varshney (IEEE Trans. Signal Inf. Process. over Networks, 2019)

Secure Distributed Detection of Sparse Signals via Falsification of Local Compressive Measurements. - Chengxi Li 001; Gang Li 008; Bhavya Kailkhura; Pramod K. Varshney (IEEE Trans. Signal Process., 2019)

Treeview and Disentangled Representations for Explaining Deep Neural Networks Decisions. - Prasanna Sattigeri; Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Bhavya Kailkhura (ACSSC, 2020)

FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling. - Cheng Chen; Ziyi Chen 002; Yi Zhou 017; Bhavya Kailkhura (IEEE BigData, 2020)

Towards an Efficient and General Framework of Robust Training for Graph Neural Networks. - Kaidi Xu; Sijia Liu 001; Pin-Yu Chen; Mengshu Sun; Caiwen Ding; Bhavya Kailkhura; Xue Lin 001 (ICASSP, 2020)

Adversarial Mutual Information for Text Generation. - Boyuan Pan; Yazheng Yang; Kaizhao Liang; Bhavya Kailkhura; Zhongming Jin 001; Xian-Sheng Hua 001; Deng Cai 001; Bo Li 026 (ICML, 2020)

Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning. - Jize Zhang; Bhavya Kailkhura; Thomas Yong-Jin Han (ICML, 2020)

A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning. - Bhavya Kailkhura; Jayaraman J. Thiagarajan; Qunwei Li; Jize Zhang; Yi Zhou; Timo Bremer (NeurIPS, 2020)

Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond. - Kaidi Xu; Zhouxing Shi; Huan Zhang 001; Yihan Wang; Kai-Wei Chang; Minlie Huang; Bhavya Kailkhura; Xue Lin 001; Cho-Jui Hsieh (NeurIPS, 2020)

Anomalous Example Detection in Deep Learning: A Survey. - Saikiran Bulusu; Bhavya Kailkhura; Bo Li 026; Pramod K. Varshney; Dawn Song (IEEE Access, 2020)

Automatic Perturbation Analysis on General Computational Graphs. - Kaidi Xu; Zhouxing Shi; Huan Zhang 001; Minlie Huang; Kai-Wei Chang; Bhavya Kailkhura; Xue Lin 001; Cho-Jui Hsieh (CoRR, 2020)

Anomalous Instance Detection in Deep Learning: A Survey. - Saikiran Bulusu; Bhavya Kailkhura; Bo Li 026; Pramod K. Varshney; Dawn Song (CoRR, 2020)

A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning. - Sijia Liu 001; Pin-Yu Chen; Bhavya Kailkhura; Gaoyuan Zhang; Alfred O. Hero III; Pramod K. Varshney (CoRR, 2020)

Actionable Attribution Maps for Scientific Machine Learning. - Shusen Liu 001; Bhavya Kailkhura; Jize Zhang; Anna M. Hiszpanski; Emily Robertson; Donald Loveland; Thomas Yong-Jin Han (CoRR, 2020)

Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design. - Shusen Liu 001; Bhavya Kailkhura; Jize Zhang; Anna M. Hiszpanski; Emily Robertson; Donald Loveland; Thomas Yong-Jin Han (CoRR, 2020)

Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning. - Ankur Mallick; Chaitanya Dwivedi; Bhavya Kailkhura; Gauri Joshi; Thomas Yong-Jin Han (CoRR, 2020)

Leveraging Uncertainty from Deep Learning for Trustworthy Materials Discovery Workflows. - Jize Zhang; Bhavya Kailkhura; Thomas Yong-Jin Han (CoRR, 2020)

MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking. - Rushil Anirudh; Jayaraman J. Thiagarajan; Bhavya Kailkhura; Peer-Timo Bremer (Int. J. Comput. Vis., 2020)

Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge. - Anna M. Hiszpanski; Brian Gallagher; Karthik Chellappan; Peggy Li; Shusen Liu 001; Hyojin Kim 001; Jinkyu Han; Bhavya Kailkhura; David J. Buttler; Thomas Yong-Jin Han (J. Chem. Inf. Model., 2020)

Automated Identification of Molecular Crystals' Packing Motifs. - Donald Loveland; Bhavya Kailkhura; Piyush Karande; Anna M. Hiszpanski; Thomas Yong-Jin Han (J. Chem. Inf. Model., 2020)

A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning: Principals, Recent Advances, and Applications. - Sijia Liu 001; Pin-Yu Chen; Bhavya Kailkhura; Gaoyuan Zhang; Alfred O. Hero III; Pramod K. Varshney (IEEE Signal Process. Mag., 2020)

Attribute-Guided Adversarial Training for Robustness to Natural Perturbations. - Tejas Gokhale; Rushil Anirudh; Bhavya Kailkhura; Jayaraman J. Thiagarajan; Chitta Baral; Yezhou Yang (AAAI, 2021)

TSS: Transformation-Specific Smoothing for Robustness Certification. - Linyi Li 001; Maurice Weber; Xiaojun Xu; Luka Rimanic; Bhavya Kailkhura; Tao Xie 001; Ce Zhang 001; Bo Li 026 (CCS, 2021)

Scalability vs. Utility: Do We Have To Sacrifice One for the Other in Data Importance Quantification? - Ruoxi Jia 001; Fan Wu 011; Xuehui Sun; Jiacen Xu; David Dao; Bhavya Kailkhura; Ce Zhang 001; Bo Li 026; Dawn Song (CVPR, 2021)

How Robust Are Randomized Smoothing Based Defenses to Data Poisoning? - Akshay Mehra; Bhavya Kailkhura; Pin-Yu Chen; Jihun Hamm (CVPR, 2021)

Can Shape Structure Features Improve Model Robustness under Diverse Adversarial Settings? - Mingjie Sun; Zichao Li 009; Chaowei Xiao; Haonan Qiu; Bhavya Kailkhura; Mingyan Liu; Bo Li 026 (ICCV, 2021)

Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network. - James Diffenderfer; Bhavya Kailkhura (ICLR, 2021)

Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing. - Cheng Chen; Bhavya Kailkhura; Ryan A. Goldhahn; Yi Zhou 017 (MASS, 2021)

A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness. - James Diffenderfer; Brian R. Bartoldson; Shreya Chaganti; Jize Zhang; Bhavya Kailkhura (NeurIPS, 2021)

G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators. - Yunhui Long; Boxin Wang; Zhuolin Yang; Bhavya Kailkhura; Aston Zhang; Carl A. Gunter; Bo Li 026 (NeurIPS, 2021)

Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning. - Akshay Mehra; Bhavya Kailkhura; Pin-Yu Chen; Jihun Hamm (NeurIPS, 2021)

Deep kernels with probabilistic embeddings for small-data learning. - Ankur Mallick; Chaitanya Dwivedi; Bhavya Kailkhura; Gauri Joshi; Thomas Yong-Jin Han (UAI, 2021)

Robusta: Robust AutoML for Feature Selection via Reinforcement Learning. - Xiaoyang Wang; Bo Li 026; Yibo Zhang; Bhavya Kailkhura; Klara Nahrstedt (CoRR, 2021)

Mixture of Robust Experts (MoRE): A Flexible Defense Against Multiple Perturbations. - Hao Cheng 015; Kaidi Xu; Chenan Wang; Xue Lin 001; Bhavya Kailkhura; Ryan A. Goldhahn (CoRR, 2021)

Reliable Graph Neural Network Explanations Through Adversarial Training. - Donald Loveland; Shusen Liu 001; Bhavya Kailkhura; Anna M. Hiszpanski; Yong Han (CoRR, 2021)

Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines. - Jiachen Sun; Akshay Mehra; Bhavya Kailkhura; Pin-Yu Chen; Dan Hendrycks; Jihun Hamm; Z. Morley Mao (CoRR, 2021)

Editorial: Safe and Trustworthy Machine Learning. - Bhavya Kailkhura; Pin-Yu Chen; Xue Lin 001; Bo Li 026 (Frontiers Big Data, 2021)

Preventing Failures by Dataset Shift Detection in Safety-Critical Graph Applications. - Hoseung Song; Jayaraman J. Thiagarajan; Bhavya Kailkhura (Frontiers Artif. Intell., 2021)

MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data. - Qunwei Li; Bhavya Kailkhura; Rushil Anirudh; Jize Zhang; Yi Zhou 017; Yingbin Liang; Thomas Yong-Jin Han; Pramod K. Varshney (SIAM J. Math. Data Sci., 2021)

Coverage-Based Designs Improve Sample Mining and Hyperparameter Optimization. - Gowtham Muniraju; Bhavya Kailkhura; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Cihan Tepedelenlioglu; Andreas Spanias (IEEE Trans. Neural Networks Learn. Syst., 2021)

Unsupervised Test-Time Adaptation of Deep Neural Networks at the Edge: A Case Study. - Kshitij Bhardwaj; James Diffenderfer; Bhavya Kailkhura; Maya B. Gokhale (DATE, 2022)

Fault-Tolerant Deep Neural Networks for Processing-In-Memory based Autonomous Edge Systems. - Siyue Wang; Geng Yuan; Xiaolong Ma; Yanyu Li; Xue Lin 001; Bhavya Kailkhura (DATE, 2022)

A Spectral View of Randomized Smoothing Under Common Corruptions: Benchmarking and Improving Certified Robustness. - Jiachen Sun; Akshay Mehra; Bhavya Kailkhura; Pin-Yu Chen; Dan Hendrycks; Jihun Hamm; Z. Morley Mao (ECCV (4), 2022)

COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks. - Fan Wu 011; Linyi Li 001; Huan Zhang 001; Bhavya Kailkhura; Krishnaram Kenthapadi; Ding Zhao; Bo Li 026 (ICLR, 2022)

On the Certified Robustness for Ensemble Models and Beyond. - Zhuolin Yang; Linyi Li 001; Xiaojun Xu; Bhavya Kailkhura; Tao Xie 001; Bo Li 026 (ICLR, 2022)

Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices. - Kshitij Bhardwaj; James Diffenderfer; Bhavya Kailkhura; Maya B. Gokhale (ISPASS, 2022)

Models Out of Line: A Fourier Lens on Distribution Shift Robustness. - Sara Fridovich-Keil; Brian R. Bartoldson; James Diffenderfer; Bhavya Kailkhura; Timo Bremer (NeurIPS, 2022)

More or Less (MoL): Defending against Multiple Perturbation Attacks on Deep Neural Networks through Model Ensemble and Compression. - Hao Cheng 015; Kaidi Xu; Zhengang Li; Pu Zhao 001; Chenan Wang; Xue Lin 001; Bhavya Kailkhura; Ryan A. Goldhahn (WACV (Workshops), 2022)

Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions. - Jiachen Sun; Qingzhao Zhang; Bhavya Kailkhura; Zhiding Yu; Chaowei Xiao; Z. Morley Mao (CoRR, 2022)

COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks. - Fan Wu 011; Linyi Li 001; Chejian Xu; Huan Zhang 001; Bhavya Kailkhura; Krishnaram Kenthapadi; Ding Zhao; Bo Li 026 (CoRR, 2022)

A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-level Optimization. - Ziyi Chen 002; Bhavya Kailkhura; Yi Zhou 017 (CoRR, 2022)

Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning. - Ioannis C. Tsaknakis; Bhavya Kailkhura; Sijia Liu 001; Donald Loveland; James Diffenderfer; Anna Maria Hiszpanski; Mingyi Hong 001 (CoRR, 2022)

"Understanding Robustness Lottery": A Comparative Visual Analysis of Neural Network Pruning Approaches. - Zhimin Li; Shusen Liu 001; Xin Yu 002; Bhavya Kailkhura; Jie Cao 010; James Daniel Diffenderfer; Peer-Timo Bremer; Valerio Pascucci (CoRR, 2022)

On Certifying and Improving Generalization to Unseen Domains. - Akshay Mehra; Bhavya Kailkhura; Pin-Yu Chen; Jihun Hamm (CoRR, 2022)

Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed. - Hao Cheng 015; Pu Zhao 001; Yize Li; Xue Lin 001; James Diffenderfer; Ryan A. Goldhahn; Bhavya Kailkhura (CoRR, 2022)

Enabling machine learning-ready HPC ensembles with Merlin. - 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; et al. (Future Gener. Comput. Syst., 2022)

Representing Polymers as Periodic Graphs with Learned Descriptors for Accurate Polymer Property Predictions. - Evan R. Antoniuk; Peggy Li; Bhavya Kailkhura; Anna M. Hiszpanski (J. Chem. Inf. Model., 2022)

Robust Decentralized Learning Using ADMM With Unreliable Agents. - Qunwei Li; Bhavya Kailkhura; Ryan A. Goldhahn; Priyadip Ray; Pramod K. Varshney (IEEE Trans. Signal Process., 2022)

Less is More: Data Pruning for Faster Adversarial Training. - Yize Li; Pu Zhao 001; Xue Lin 001; Bhavya Kailkhura; Ryan A. Goldhahn (SafeAI@AAAI, 2023)

Neural Image Compression: Generalization, Robustness, and Spectral Biases. - Kelsey Lieberman; James Diffenderfer; Charles Godfrey; Bhavya Kailkhura (NeurIPS, 2023)

Improving Diversity with Adversarially Learned Transformations for Domain Generalization. - Tejas Gokhale; Rushil Anirudh; Jayaraman J. Thiagarajan; Bhavya Kailkhura; Chitta Baral; Yezhou Yang (WACV, 2023)

Shifting Attention to Relevance: Towards the Uncertainty Estimation of Large Language Models. - Jinhao Duan; Hao Cheng 015; Shiqi Wang 002; Chenan Wang; Alex Zavalny; Renjing Xu; Bhavya Kailkhura; Kaidi Xu (CoRR, 2023)

Gaining the Sparse Rewards by Exploring Binary Lottery Tickets in Spiking Neural Network. - Hao Cheng 015; Jiahang Cao; Erjia Xiao; Pu Zhao 001; Mengshu Sun; Jiaxu Wang; Jize Zhang; Xue Lin 001; Bhavya Kailkhura; Kaidi Xu; Renjing Xu (CoRR, 2023)

Instance-wise Linearization of Neural Network for Model Interpretation. - Zhimin Li; Shusen Liu 001; Bhavya Kailkhura; Timo Bremer; Valerio Pascucci (CoRR, 2023)

Pursing the Sparse Limitation of Spiking Deep Learning Structures. - Hao Cheng 015; Jiahang Cao; Erjia Xiao; Mengshu Sun; Le Yang; Jize Zhang; Xue Lin 001; Bhavya Kailkhura; Kaidi Xu; Renjing Xu (CoRR, 2023)

When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks. - Gourav Datta; Zeyu Liu 003; James Diffenderfer; Bhavya Kailkhura; Peter A. Beerel (CoRR, 2023)

Scaling Compute Is Not All You Need for Adversarial Robustness. - Edoardo Debenedetti; Zishen Wan; Maksym Andriushchenko; Vikash Sehwag; Kshitij Bhardwaj; Bhavya Kailkhura (CoRR, 2023)

Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities. - Brian R. Bartoldson; Bhavya Kailkhura; Davis W. Blalock (J. Mach. Learn. Res., 2023)

An accelerated proximal algorithm for regularized nonconvex and nonsmooth bi-level optimization. - Ziyi Chen; Bhavya Kailkhura; Yi Zhou (Mach. Learn., 2023)

Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models. - Jinhao Duan; Hao Cheng 015; Shiqi Wang 002; Alex Zavalny; Chenan Wang; Renjing Xu; Bhavya Kailkhura; Kaidi Xu (ACL (1), 2024)

RankMean: Module-Level Importance Score for Merging Fine-tuned LLM Models. - Gabriel J. Perin; Xuxi Chen; Shusen Liu 001; Bhavya Kailkhura; Zhangyang Wang; Brian Gallagher (ACL (Findings), 2024)

Cross-Quality Few-Shot Transfer for Alloy Yield Strength Prediction: A New Materials Science Benchmark and A Sparsity-Oriented Optimization Framework. - Xuxi Chen; Tianlong Chen 001; Everardo Yeriel Olivares; Kate Elder; Scott McCall; Aurelien Perron; Joseph McKeown; Bhavya Kailkhura; Zhangyang Wang; Brian Gallagher (CPAL, 2024)

Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation. - Haizhong Zheng; Jiachen Sun; Shutong Wu; Bhavya Kailkhura; Z. Morley Mao; Chaowei Xiao; Atul Prakash 001 (ECCV (24), 2024)

SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning. - Jinghan Jia; Yihua Zhang; Yimeng Zhang; Jiancheng Liu; Bharat Runwal; James Diffenderfer; Bhavya Kailkhura; Sijia Liu 001 (EMNLP, 2024)

DeepZero: Scaling Up Zeroth-Order Optimization for Deep Model Training. - Aochuan Chen; Yimeng Zhang; Jinghan Jia; James Diffenderfer; Konstantinos Parasyris; Jiancheng Liu; Yihua Zhang; Zheng Zhang 005; Bhavya Kailkhura; Sijia Liu 001 (ICLR, 2024)

NEFTune: Noisy Embeddings Improve Instruction Finetuning. - Neel Jain; Ping-yeh Chiang; Yuxin Wen; John Kirchenbauer; Hong-Min Chu; Gowthami Somepalli; Brian R. Bartoldson; Bhavya Kailkhura; Avi Schwarzschild; Aniruddha Saha; Micah Goldblum; Jonas Geiping; et al. (ICLR, 2024)

Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies. - Brian R. Bartoldson; James Diffenderfer; Konstantinos Parasyris; Bhavya Kailkhura (ICML, 2024)

Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression. - Junyuan Hong; Jinhao Duan; Chenhui Zhang; Zhangheng Li; Chulin Xie; Kelsey Lieberman; James Diffenderfer; Brian R. Bartoldson; Ajay Kumar Jaiswal; Kaidi Xu; Bhavya Kailkhura; Dan Hendrycks; et al. (ICML, 2024)

Position: TrustLLM: Trustworthiness in Large Language Models. - Yue Huang; Lichao Sun 001; Haoran Wang 005; Siyuan Wu 001; Qihui Zhang; Yuan Li; Chujie Gao; Yixin Huang; Wenhan Lyu; Yixuan Zhang; Xiner Li; Hanchi Sun; et al. (ICML, 2024)

Q-Hitter: A Better Token Oracle for Efficient LLM Inference via Sparse-Quantized KV Cache. - Zhenyu Zhang 015; Shiwei Liu 003; Runjin Chen; Bhavya Kailkhura; Beidi Chen; Atlas Wang (MLSys, 2024)

ReTA: Recursively Thinking Ahead to Improve the Strategic Reasoning of Large Language Models. - Jinhao Duan; Shiqi Wang 002; James Diffenderfer; Lichao Sun 001; Tianlong Chen 001; Bhavya Kailkhura; Kaidi Xu (NAACL-HLT, 2024)

GTBench: Uncovering the Strategic Reasoning Capabilities of LLMs via Game-Theoretic Evaluations. - Jinhao Duan; Renming Zhang; James Diffenderfer; Bhavya Kailkhura; Lichao Sun 001; Elias Stengel-Eskin; Mohit Bansal; Tianlong Chen 001; Kaidi Xu (NeurIPS, 2024)

Transformers Can Do Arithmetic with the Right Embeddings. - Sean McLeish; Arpit Bansal; Alex Stein; Neel Jain; John Kirchenbauer; Brian R. Bartoldson; Bhavya Kailkhura; Abhinav Bhatele; Jonas Geiping; Avi Schwarzschild; Tom Goldstein (NeurIPS, 2024)

Training Dynamics of Transformers to Recognize Word Co-occurrence via Gradient Flow Analysis. - Hongru Yang; Bhavya Kailkhura; Zhangyang Wang; Yingbin Liang (NeurIPS, 2024)

On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization. - Akshay Mehra; Yunbei Zhang; Bhavya Kailkhura; Jihun Hamm (WACV, 2024)

TrustLLM: Trustworthiness in Large Language Models. - Lichao Sun 001; Yue Huang 001; Haoran Wang 005; Siyuan Wu 001; Qihui Zhang; Chujie Gao; Yixin Huang; Wenhan Lyu; Yixuan Zhang 001; Xiner Li; Zhengliang Liu; Yixin Liu 002; et al. (CoRR, 2024)

GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations. - Jinhao Duan; Renming Zhang; James Diffenderfer; Bhavya Kailkhura; Lichao Sun 001; Elias Stengel-Eskin; Mohit Bansal; Tianlong Chen 001; Kaidi Xu (CoRR, 2024)

End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver. - Shaocong Ma; James Diffenderfer; Bhavya Kailkhura; Yi Zhou 017 (CoRR, 2024)

Introducing v0.5 of the AI Safety Benchmark from MLCommons. - Bertie Vidgen; Adarsh Agrawal; Ahmed M. Ahmed 004; Victor Akinwande; Namir Al-Nuaimi; Najla Alfaraj; Elie Alhajjar; Lora Aroyo; Trupti Bavalatti; Borhane Blili-Hamelin; Kurt D. Bollacker; Rishi Bomassani; et al. (CoRR, 2024)

Low-rank finetuning for LLMs: A fairness perspective. - Saswat Das; Marco Romanelli 002; Cuong Tran 007; Zarreen Reza; Bhavya Kailkhura; Ferdinando Fioretto (CoRR, 2024)

ELFS: Enhancing Label-Free Coreset Selection via Clustering-based Pseudo-Labeling. - Haizhong Zheng; Elisa Tsai; Yifu Lu; Jiachen Sun; Brian R. Bartoldson; Bhavya Kailkhura; Atul Prakash 001 (CoRR, 2024)

Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion. - Jacob K. Christopher; Brian R. Bartoldson; Bhavya Kailkhura; Ferdinando Fioretto (CoRR, 2024)

GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models. - Mufan Qiu; Xinyu Hu; Fengwei Zhan; Sukwon Yun; Jie Peng 002; Ruichen Zhang; Bhavya Kailkhura; Jiekun Yang; Tianlong Chen 001 (ACL (Findings), 2025)

ELFS: Label-Free Coreset Selection with Proxy Training Dynamics. - Haizhong Zheng; Elisa Tsai; Yifu Lu; Jiachen Sun; Brian R. Bartoldson; Bhavya Kailkhura; Atul Prakash 001 (ICLR, 2025)

Extracting and Understanding the Superficial Knowledge in Alignment. - Runjin Chen; Gabriel J. Perin; Xuxi Chen; Xilun Chen; Yan Han; Nina S. T. Hirata; Junyuan Hong; Bhavya Kailkhura (NAACL (Long Papers), 2025)

Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion. - Jacob K. Christopher; Brian R. Bartoldson; Tal Ben-Nun; Michael Cardei; Bhavya Kailkhura; Ferdinando Fioretto (NAACL (Long Papers), 2025)

Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense. - Yang Ouyang; Hengrui Gu 002; Shuhang Lin; Wenyue Hua; Jie Peng 002; Bhavya Kailkhura; Meijun Gao; Tianlong Chen 001; Kaixiong Zhou (NAACL (Long Papers), 2025)

When SNN meets ANN: Error-Free ANN-to-SNN Conversion for Extreme Edge Efficiency. - Gourav Datta; Zeyu Liu 003; James Diffenderfer; Bhavya Kailkhura; Peter Anthony Beerel (Trans. Mach. Learn. Res., 2025)

Active Learning Enables Extrapolation in Molecular Generative Models. - Evan R. Antoniuk; Peggy Li; Nathan Keilbart; Stephen Weitzner; Bhavya Kailkhura; Anna M. Hiszpanski (CoRR, 2025)

Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense. - Yang Ouyang; Hengrui Gu 002; Shuhang Lin; Wenyue Hua; Jie Peng 002; Bhavya Kailkhura; Tianlong Chen 001; Kaixiong Zhou (CoRR, 2025)

Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness. - Zeyu Wang 008; Cihang Xie; Brian R. Bartoldson; Bhavya Kailkhura (CoRR, 2025)

Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach. - Jonas Geiping; Sean McLeish; Neel Jain; John Kirchenbauer; Siddharth Singh; Brian R. Bartoldson; Bhavya Kailkhura; Abhinav Bhatele; Tom Goldstein (CoRR, 2025)

EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants. - 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; et al. (CoRR, 2025)

Constrained Language Generation with Discrete Diffusion Models. - Michael Cardei; Jacob K. Christopher; Thomas Hartvigsen; Brian R. Bartoldson; Bhavya Kailkhura; Ferdinando Fioretto (CoRR, 2025)

TruthPrInt: Mitigating LVLM Object Hallucination Via Latent Truthful-Guided Pre-Intervention. - Jinhao Duan; Fei Kong; Hao Cheng 015; James Diffenderfer; Bhavya Kailkhura; Lichao Sun 001; Xiaofeng Zhu 001; Xiaoshuang Shi; Kaidi Xu (CoRR, 2025)

Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training. - Brian R. Bartoldson; Siddarth Venkatraman; James Diffenderfer; Moksh Jain; Tal Ben-Nun; Seanie Lee; Minsu Kim 004; Johan S. Obando-Ceron; Yoshua Bengio; Bhavya Kailkhura (CoRR, 2025)

Deep learning of PDE correction and mesh adaption without automatic differentiation. - Shaocong Ma; James Diffenderfer; Bhavya Kailkhura; Yi Zhou 017 (Mach. Learn., 2025)

STAR-1: Safer Alignment of Reasoning LLMs with 1K Data. - Zijun Wang; Haoqin Tu; Yuhan Wang; Juncheng Wu; Jieru Mei; Brian R. Bartoldson; Bhavya Kailkhura; Cihang Xie (CoRR, 2025)

LLM Unlearning Reveals a Stronger-Than-Expected Coreset Effect in Current Benchmarks. - Soumyadeep Pal; Changsheng Wang; James Diffenderfer; Bhavya Kailkhura; Sijia Liu 001 (CoRR, 2025)

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment. - Kun Wang 056; Guibin Zhang; Zhenhong Zhou; Jiahao Wu; Miao Yu; Shiqian Zhao; Chenlong Yin; Jinhu Fu; Yibo Yan; Hanjun Luo; Liang Lin; Zhihao Xu; et al. (CoRR, 2025)

AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security. - Zikui Cai; Shayan Shabihi; Bang An 001; Zora Che; Brian R. Bartoldson; Bhavya Kailkhura; Tom Goldstein; Furong Huang (CoRR, 2025)

BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models. - 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 (CoRR, 2025)

Act Only When It Pays: Efficient Reinforcement Learning for LLM Reasoning via Selective Rollouts. - Haizhong Zheng; Yang Zhou; Brian R. Bartoldson; Bhavya Kailkhura; Fan Lai 001; Jiawei Zhao; Beidi Chen (CoRR, 2025)

The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text. - 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; et al. (CoRR, 2025)

UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making. - Jinhao Duan; James Diffenderfer; Sandeep Madireddy; Tianlong Chen 001; Bhavya Kailkhura; Kaidi Xu (CoRR, 2025)

"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches. - Zhimin Li; Shusen Liu 001; Xin Yu 002; Bhavya Kailkhura; Jie Cao 010; James Daniel Diffenderfer; Peer-Timo Bremer; Valerio Pascucci (IEEE Trans. Vis. Comput. Graph., 2025)