6 accepted papers
The alignment of Large Language Models (LLMs) with human preferences is critically undermined by noisy labels in training datasets. Existing robust methods often prove insufficient, as they rely on single, narrow heuristics such as perplexity or loss, failing to address the diverse nature of real-wo…
The efficiency of hyperparameter optimization (HPO) is critical for deep learning, yet state-of-the-art methods share a fundamental flaw: they are difficulty-agnostic, treating all hyperparameter configurations homogeneously. This approach leads to inefficient resource allocation, wasting budget in
Learning Curve Extrapolation (LCE) is a critical technique for accelerating automated machine learning by terminating unpromising training runs early. Recent state-of-the-art methods have improved predictive accuracy by incorporating contextual information, such as neural network architecture. Howev
Direct Preference Optimization (DPO) has become the dominant approach for aligning large language models with human preferences. However, standard DPO treats all preference pairs uniformly, overlooking the heterogeneous nature of the learning problem: some samples demand sophisticated semantic under…
Direct Preference Optimization (DPO) has become a predominant approach for aligning large language models with human preferences. Recent work has used perplexity differentials to identify unreliable preference labels, but these methods apply uniform calibration strategies across all samples. We obse…
Features, logits, and labels are the three primary data when a sample passes through a deep neural network. Feature perturbation and label perturbation receive increasing attention in recent years. They have been proven to be useful in various deep learning approaches. For example, (adversarial) fea…