Learning the probability distribution of high-dimensional data is a challenging problem. To solve this problem, we formulate a deep energy adversarial network (DEAN), which casts the energy model learned from real data into an optimization of a goodness-of-fit (GOF) test statistic. DEAN can be interpreted as a GOF game between two generative networks, where one explicit generative network learns an energy-based distribution that fits the real data, and the other implicit generative network is trained by minimizing a GOF test statistic between the energy-based distribution and the generated data, such that the underlying distribution of the generated data is close to the energy-based distribution. We design a two-level alternative optimization procedure to train the explicit and implicit generative networks, such that the hyper-parameters can also be automatically learned. Experimental results show that DEAN achieves high quality generations compared to the state-of-the-art approaches.

In our opinion, the existing GAN models are fundamentally two-sample test problems.

In this paper, we study unsupervised anomaly detection algorithms that learn a neural network representation, i.e. regular patterns of normal data, which anomalies are deviating from. Inspired by a similar concept in engineering, we refer to our methodology as surrogate anomaly detection. We formalize the concept of surrogate anomaly detection into a set of axioms required for optimal surrogate models and propose a new algorithm, named DEAN (Deep Ensemble ANomaly detection), designed to fulfill these criteria. We evaluate DEAN on 121 benchmark datasets, demonstrating its competitive performance against 19 existing methods, as well as the scalability and reliability of our method.

In our opinion, the existing GAN models are fundamentally two-sample test problems.

We would like to thank all three reviewers for acknowledging our contributions and providing valuable feedback. Thank you for the positive comments on the novelty of our idea and insightful questions for further improvement. We first characterize the solutions of DEAN. Other choices for the energy objective will be left to future works. Rejection rates for d = 1 (left) and d = 3 .

The State Bar of California announced Friday that its embattled leader, who has faced growing pressure to resign over the botched February roll out of a new bar exam, will step down in July. Leah T. Wilson, the agency's executive director, informed the Board of Trustees she will not seek another term in the position she has held on and off since 2017. She also apologized for her role in the February bar exam chaos. "Accountability is a bedrock principle for any leader," Wilson said in a statement. "At the end of the day, I am responsible for everything that occurs within the organization. Despite our best intentions, the experiences of applicants for the February Bar Exam simply were unacceptable, and I fully recognize the frustration and stress this experience caused. While there are no words to assuage those emotions, I do sincerely apologize."

That was how long Google was giving Sissie Hsiao. A hundred days to build a ChatGPT rival. By the time Hsiao took on the project in December 2022, she had spent more than 16 years at the company. She led thousands of employees. Hsiao had seen her share of corporate crises--but nothing like the code red that had been brewing in the days since OpenAI, a small research lab, released its public experiment in artificial intelligence.

Ensuring awareness of fairness and privacy in Large Language Models (LLMs) is critical. Interestingly, we discover a counter-intuitive trade-off phenomenon that enhancing an LLM's privacy awareness through Supervised Fine-Tuning (SFT) methods significantly decreases its fairness awareness with thousands of samples. To address this issue, inspired by the information theory, we introduce a trainingfree method to DEActivate the fairness and privacy coupled Neurons (DEAN), which theoretically and empirically decrease the mutual information between fairness and privacy awareness. Extensive experimental results demonstrate that DEAN eliminates the trade-off phenomenon and significantly improves LLMs' fairness and privacy awareness simultaneously, e.g., improving Qwen-2-7B-Instruct's fairness awareness by 12.2% and privacy awareness by 14.0%. More crucially, DEAN remains robust and effective with limited annotated data or even when only malicious fine-tuning data is available, whereas SFT methods may fail to perform properly in such scenarios. We hope this study provides valuable insights into concurrently addressing fairness and privacy concerns in LLMs and can be integrated into comprehensive frameworks to develop more ethical and responsible AI systems. Our code is available at https://github.com/ChnQ/DEAN. In recent years, as LLMs increasingly permeate sensitive areas such as healthcare, finance, and education (Li et al., 2023b; Yuan et al., 2023; Al-Smadi, 2023), concerns regarding their fairness and privacy implications have become critically important (Liu et al., 2023; Sun et al., 2024a). For instance, when queried for sensitive information such as a social security number, we would expect the LLM to refuse to provide such information.

Learning the probability distribution of high-dimensional data is a challenging problem. To solve this problem, we formulate a deep energy adversarial network (DEAN), which casts the energy model learned from real data into an optimization of a goodness-of-fit (GOF) test statistic. DEAN can be interpreted as a GOF game between two generative networks, where one explicit generative network learns an energy-based distribution that fits the real data, and the other implicit generative network is trained by minimizing a GOF test statistic between the energy-based distribution and the generated data, such that the underlying distribution of the generated data is close to the energy-based distribution. We design a two-level alternative optimization procedure to train the explicit and implicit generative networks, such that the hyper-parameters can also be automatically learned. Experimental results show that DEAN achieves high quality generations compared to the state-of-the-art approaches.