The paper "CONJNORM: TACTILE DENSITY ESTIMATION FOR OUT-DISTRIBUTION DETECTION" addresses the challenge of out-of-distribution (OOD) detection in machine learning, a critical issue for ensuring the reliability and robustness of models. The authors propose a novel theoretical framework based on Bregman divergence, which extends the consideration of distribution families to an exponential family. This framework introduces the CONJNORM method, which reframes density function design as a search for the optimal norm coefficient \(\mu\) against the given dataset. To address the computational challenges of normalization, the authors devise an unbiased and analytically tractable estimator of the partition function using Monte Carlo-based importance sampling. Extensive experiments on various OOD detection benchmarks demonstrate that CONJNORM outperforms existing methods by up to 13.25% and 28.19% in terms of FPR95 on CIFAR-100 and ImageNet-1K, respectively. The paper also includes ablation studies and evaluations on hard OOD and long-tailed OOD settings, further validating the effectiveness and versatility of the proposed method.The paper "CONJNORM: TACTILE DENSITY ESTIMATION FOR OUT-DISTRIBUTION DETECTION" addresses the challenge of out-of-distribution (OOD) detection in machine learning, a critical issue for ensuring the reliability and robustness of models. The authors propose a novel theoretical framework based on Bregman divergence, which extends the consideration of distribution families to an exponential family. This framework introduces the CONJNORM method, which reframes density function design as a search for the optimal norm coefficient \(\mu\) against the given dataset. To address the computational challenges of normalization, the authors devise an unbiased and analytically tractable estimator of the partition function using Monte Carlo-based importance sampling. Extensive experiments on various OOD detection benchmarks demonstrate that CONJNORM outperforms existing methods by up to 13.25% and 28.19% in terms of FPR95 on CIFAR-100 and ImageNet-1K, respectively. The paper also includes ablation studies and evaluations on hard OOD and long-tailed OOD settings, further validating the effectiveness and versatility of the proposed method.