5 accepted papers
A key challenge in calibrating Multi-Label Classification(MLC) problems is to consider the interdependencies among labels. To address this, in this research we propose an unbiased, differentiable, trainable calibration error estimator for MLC problems by using Copula. Unlike other methods for calibr…
Most counterfactual inference frameworks traditionally assume acyclic structural causal models (SCMs), i.e. directed acyclic graphs (DAGs). However, many real-world systems (e.g. biological systems) contain feedback loops or cyclic dependencies that violate acyclicity. In this work, we study counter…
A key challenge in multi-label classification is to model the dependencies between the labels while ensuring proper calibration, as the assumption of label independence often results in inferior classification performance and poor calibration. However, most of the earlier works that modeled label de…
Crosswords are a form of word puzzle that require a solver to demonstrate a high degree of proficiency in natural language understanding, wordplay, reasoning, and world knowledge, along with adherence to character and length constraints. In this paper we tackle the challenge of solving crosswords wi…
User authorization-based access privileges are a key feature in many safety-critical systems, but have not been extensively studied in the large language model (LLM) realm. In this work, drawing inspiration from such access control systems, we introduce sudoLLM, a novel framework that results in mul