Bhavya Kailkhura
44 papers · 2018–2026 · 13 conferences · across top CS/AI conferences
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Conferences
NIPS (11)
ICLR (6)
ICML (5)
NAACL (4)
ACL (3)
ICCV (3)
AAAI (2)
CVPR (2)
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Keywords
large language model
(5)
domain generalization
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zeroth-order optimization
(2)
spectral analysis
(2)
adversarial robustness
(2)
domain shift
(2)
model unlearning
(2)
model robustness
(2)
out-of-distribution generalization
(2)
certified robustness
(2)
adversarial training
(2)
image classification
(2)
foundation model
(2)
data poisoning
(2)
strategic reasoning
(2)
adversarial learning
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representation learning
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data augmentation
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neural network optimization
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board game
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Papers
STAR-1: Safer Alignment of Reasoning LLMs with 1K Data
AAAI 2026
Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense
NAACL 2025
GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models
ACL 2025
Extracting and Understanding the Superficial Knowledge in Alignment
NAACL 2025
ELFS: Label-Free Coreset Selection with Proxy Training Dynamics
ICLR 2025
TruthPrInt: Mitigating Large Vision-Language Models Object Hallucination Via Latent Truthful-Guided Pre-Intervention
ICCV 2025
Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion
NAACL 2025
DeepZero: Scaling Up Zeroth-Order Optimization for Deep Model Training
ICLR 2024
GTBench: Uncovering the Strategic Reasoning Capabilities of LLMs via Game-Theoretic Evaluations
NIPS 2024
Training Dynamics of Transformers to Recognize Word Co-occurrence via Gradient Flow Analysis
NIPS 2024
Transformers Can Do Arithmetic with the Right Embeddings
NIPS 2024
Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models
ACL 2024
RankMean: Module-Level Importance Score for Merging Fine-tuned LLM Models
ACL 2024
Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation
ECCV 2024
SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning
EMNLP 2024
NEFTune: Noisy Embeddings Improve Instruction Finetuning
ICLR 2024
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies
ICML 2024
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression
ICML 2024
Position: TrustLLM: Trustworthiness in Large Language Models
ICML 2024
ReTA: Recursively Thinking Ahead to Improve the Strategic Reasoning of Large Language Models
NAACL 2024
On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization
WACV 2024
Improving Diversity With Adversarially Learned Transformations for Domain Generalization
WACV 2023
Neural Image Compression: Generalization, Robustness, and Spectral Biases
NIPS 2023
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
JMLR 2023
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks
ICLR 2022
On the Certified Robustness for Ensemble Models and Beyond
ICLR 2022
Models Out of Line: A Fourier Lens on Distribution Shift Robustness
NIPS 2022
A Spectral View of Randomized Smoothing under Common Corruptions: Benchmarking and Improving Certified Robustness
ECCV 2022
Can Shape Structure Features Improve Model Robustness Under Diverse Adversarial Settings?
ICCV 2021
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network
ICLR 2021
A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness
NIPS 2021
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning
NIPS 2021
Deep kernels with probabilistic embeddings for small-data learning
UAI 2021
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations
AAAI 2021
How Robust Are Randomized Smoothing Based Defenses to Data Poisoning?
CVPR 2021
Scalability vs. Utility: Do We Have To Sacrifice One for the Other in Data Importance Quantification?
CVPR 2021
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators
NIPS 2021
A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning
NIPS 2020
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond
NIPS 2020
Adversarial Mutual Information for Text Generation
ICML 2020
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
ICML 2020
On the Design of Black-Box Adversarial Examples by Leveraging Gradient-Free Optimization and Operator Splitting Method
ICCV 2019
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization
NIPS 2018
A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms
JMLR 2018