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Nikhil Abhyankar

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TMLR Journal 2026 Journal Article

LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers

  • Nikhil Abhyankar
  • Parshin Shojaee
  • Chandan K. Reddy

Automated feature engineering plays a critical role in improving predictive model performance for tabular learning tasks. Traditional automated feature engineering methods are limited by their reliance on pre-defined transformations within fixed, manually designed search spaces, often neglecting domain knowledge. Recent advances using Large Language Models (LLMs) have enabled the integration of domain knowledge into the feature engineering process. However, existing LLM-based approaches use direct prompting or rely solely on validation scores for feature selection, failing to leverage insights from prior feature discovery experiments or establish meaningful reasoning between feature generation and data-driven performance. To address these challenges, we propose LLM-FE, a novel framework that combines evolutionary search with the domain knowledge and reasoning capabilities of LLMs to automatically discover effective features for tabular learning tasks. LLM-FE formulates feature engineering as a program search problem, where LLMs propose new feature transformation programs iteratively, and data-driven feedback guides the search process. Our results demonstrate that LLM-FE consistently outperforms state-of-the-art baselines, showcasing generalizability across diverse models, tasks, and datasets.

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TMLR Journal 2024 Journal Article

Data-Centric Defense: Shaping Loss Landscape with Augmentations to Counter Model Inversion

  • Si Chen
  • Feiyang Kang
  • Nikhil Abhyankar
  • Ming Jin
  • Ruoxi Jia

Machine Learning models have shown susceptibility to various privacy attacks, with model inversion (MI) attacks posing a significant threat. Current defense techniques are mostly \emph{model-centric}, involving modifying model training or inference. However, these approaches require model trainers' cooperation, are computationally expensive, and often result in a significant privacy-utility tradeoff. To address these limitations, we propose a novel \emph{data-centric} approach to mitigate MI attacks. Compared to traditional model-centric techniques, our approach offers the unique advantage of enabling each individual user to control their data's privacy risk, aligning with findings from a Cisco survey that only a minority actively seek privacy protection. Specifically, we introduce several privacy-focused data augmentations that modify the private data uploaded to the model trainer. These augmentations shape the resulting model's loss landscape, making it challenging for attackers to generate private target samples. Additionally, we provide theoretical analysis to explain why such augmentations can reduce the risk of model inversion. We evaluate our approach against state-of-the-art MI attacks and demonstrate its effectiveness and robustness across various model architectures and datasets. Specifically, in standard face recognition benchmarks, we reduce face reconstruction success rates to $\leq5\%$, while maintaining high utility with only a 2\% classification accuracy drop, significantly surpassing state-of-the-art model-centric defenses. This is the first study to propose a data-centric approach for mitigating model inversion attacks, showing promising potential for decentralized privacy protection.

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