Kailkhura, Bhavya

57 publications

NeurIPS 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, Jiawei Zhao, Beidi Chen

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

NeurIPS 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 Sahin, Obadiah Hersh Smolenski, Everett Grethel, Tim Hsu, Anna Hiszpanski, Kenneth Chiu, Bhavya Kailkhura, Brian Van Essen

NeurIPS 2025 Constrained Discrete Diffusion Michael Cardei, Jacob K Christopher, Bhavya Kailkhura, Thomas Hartvigsen, Ferdinando Fioretto

MLJ 2025 Deep Learning of PDE Correction and Mesh Adaption Without Automatic Differentiation Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, Yi Zhou

ICLR 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

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

NeurIPS 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, 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, John Kirchenbauer, Tom Goldstein, Brian R. Bartoldson, Bhavya Kailkhura, Tyler Murray

NeurIPS 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, Johan Obando-Ceron, Yoshua Bengio, Bhavya Kailkhura

ICCV 2025 TruthPrInt: Mitigating Large Vision-Language Models Object Hallucination via Latent Truthful-Guided Pre-Intervention Jinhao Duan, Fei Kong, Hao Cheng, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Xiaofeng Zhu, Xiaoshuang Shi, Kaidi Xu

TMLR 2025 When SNN Meets ANN: Error-Free ANN-to-SNN Conversion for Extreme Edge Efficiency Gourav Datta, Zeyu Liu, James Diffenderfer, Bhavya Kailkhura, Peter Anthony Beerel

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

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

CPAL 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, Everardo Yeriel Olivares, Kate Elder, Scott McCall, Aurelien Perron, Joseph McKeown, Bhavya Kailkhura, Zhangyang Wang, Brian Gallagher

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, Dawn Song, Zhangyang Wang, Bo Li

ICLRW 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, Dawn Song, Zhangyang Wang, Bo Li

ICLR 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, Bhavya Kailkhura, Sijia Liu

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

ECCV 2024 Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation Haizhong Zheng, Jiachen Sun, Shutong Wu, Bhavya Kailkhura, Zhuoqing Morley Mao, Chaowei Xiao, Atul Prakash

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, Tom Goldstein

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

ICML 2024 Position: TrustLLM: Trustworthiness in Large Language Models 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 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 Yang Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yong Chen, Yue Zhao

ICLRW 2024 Scaling Compute Is Not All You Need for Adversarial Robustness Edoardo Debenedetti, Zishen Wan, Maksym Andriushchenko, Vikash Sehwag, Kshitij Bhardwaj, Bhavya Kailkhura

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 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

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

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

MLJ 2023 An Accelerated Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-Level Optimization Ziyi Chen, Bhavya Kailkhura, Yi Zhou

JMLR 2023 Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock

ICLRW 2023 Cross-Quality Few-Shot Transfer for Alloy Yield Strength Prediction: A New Material Science Benchmark and an Integrated Optimization Framework Xuxi Chen, Tianlong Chen, Everardo Yeriel Olivares, Kate Elder, Scott McCall, Aurelien Perron, Joseph McKeown, Bhavya Kailkhura, Zhangyang Wang, Brian Gallagher

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

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

ICMLW 2023 Neural Image Compression: Generalization, Robustness, and Spectral Biases Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura

ICMLW 2023 Risk-Averse Predictions on Unseen Domains via Neural Style Smoothing Akshay Mehra, Yunbei Zhang, Bhavya Kailkhura, Jihun Hamm

ECCV 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

ICLR 2022 COPA: Certifying Robust Policies for Offline Reinforcement Learning Against Poisoning Attacks Fan Wu, Linyi Li, Huan Zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, Bo Li

NeurIPSW 2022 Do Domain Generalization Methods Generalize Well? Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm

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

ICLR 2022 On the Certified Robustness for Ensemble Models and Beyond Zhuolin Yang, Linyi Li, Xiaojun Xu, Bhavya Kailkhura, Tao Xie, Bo Li

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

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

ICCV 2021 Can Shape Structure Features Improve Model Robustness Under Diverse Adversarial Settings? Mingjie Sun, Zichao Li, Chaowei Xiao, Haonan Qiu, Bhavya Kailkhura, Mingyan Liu, Bo Li

UAI 2021 Deep Kernels with Probabilistic Embeddings for Small-Data Learning Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, T. Yong-Jin Han

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 Gunter, Bo Li

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

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

ICMLW 2021 On the Effectiveness of Poisoning Against Unsupervised Domain Adaptation Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm

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

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

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

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

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

ICML 2020 Mix-N-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning Jize Zhang, Bhavya Kailkhura, T. Yong-Jin Han

JMLR 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

NeurIPS 2018 Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen, Paishun Ting, Shiyu Chang, Lisa Amini

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