This paper compares different methods for forecasting the term structure of U.S. and European zero-coupon government bonds using both traditional econometric and Machine Learning (ML) approaches. We compare classical models (e.g., Dynamic Nelson-Siegel (DNS) and Principal Component Analysis (PCA)) with different Neural Network (NN) architectures, including those inspired by the classical models, on the U.S. Treasury market and bonds issued by the European Central Bank (ECB). To enhance predictive performance, macroeconomic variables are incorporated. The findings for both markets are separately analyzed and compared. To this end, we propose a robust model evaluation framework combining statistical accuracy metrics - such as RMSE, MAE, and directional accuracy - with the economic relevance of a quantitative bond trading strategy. Results show that NNs consistently outperform traditional models in both forecasting accuracy and portfolio performance. For the U.S., the most effective approach is a direct-forecasting NN that incorporates DNS factors to reduce the dimensionality of zero-rate data and an Autoencoder (AE) to extract macroeconomic features, while for Europe, the optimal model is a factor-based NN using PCA-derived zero-rate factors without the integration of macroeconomic variables. Overall, the paper demonstrates how combining traditional modeling approaches with modern ML techniques and evaluation can improve yield curve forecasts and support applications in fixed-income portfolio construction.

Bayesian filtering of partially and noisily observed dynamical systems seeks to infer the evolving conditional distribution of the state of a dynamical system, given observations, in an online fashion. This Bayesian filtering distribution is the natural object for uncertainty quantification, but it is rarely available as a supervised learning target. However, one can often use the forecast model to generate synthetic system trajectories, along with synthetic observations. We introduce the proper scoring ensemble filter (PSEF), an ensemble data assimilation method based on training an analysis map to approximate the filtering distribution using only synthetic state--observation trajectories. The analysis step is represented as a permutation-invariant, transformer-based map that takes as input a forecast ensemble and observations, producing an analysis ensemble. Training is based on strictly proper scoring rules -- with the energy score used in our implementation -- so that probabilistic accuracy is rewarded over the whole probability distribution. We prove that, under a realizability assumption, the population objective is minimized by the true Bayesian filtering distribution. We also derive the finite-ensemble empirical objective used in training and relate its single state--observation trajectory form to the population objective, using a mean-field consistency argument. Numerical experiments show that the learned filter accurately approximates challenging filtering distributions, including nonlinear, non-Gaussian, and multi-modal posteriors, and achieves stronger performance in data assimilation tasks than classical methods or learning-based methods with mean-squared-error objectives. For close-to-Gaussian problems, learning a correction to the EnKF is the best approach, while for highly non-Gaussian problems an end-to-end approach that discards this inductive bias is superior.

Differentially private (DP) training of neural networks is often hindered by the large amount of noise required by gradient-based methods such as DP-SGD, which repeatedly inject high-dimensional noise in parameter space throughout training. In this paper, we propose a new framework for DP learning that avoids iterative optimization in parameter space. Instead of updating the target model using privatized gradients, we employ a hypernetwork trained on public datasets to map a private dataset to the parameters of the target model. Specifically, each example is embedded into a low-dimensional representation, the embeddings are aggregated and perturbed to obtain a DP dataset embedding, and the hypernetwork generates the target model parameters from this noisy embedding. Because privacy noise is injected only once into a low-dimensional dataset representation, our approach can significantly reduce the adverse effect of noise. We theoretically show in a synthetic setting that, under a fixed privacy budget, models produced by our approach achieve higher utility than those trained with DP-SGD. Moreover, we apply our approach to LoRA fine-tuning of diffusion models and show that it achieves lower FID than LoRA models trained with DP-SGD and other public-data-guided methods.

We develop a general framework for analyzing representation costs of parametric data-fitting methods through their parameter-space regularizers. From this abstract perspective, we define representation costs for arbitrary parametric models and reveal their induced (native) function spaces. This unifies recent function-space views of data-fitting methods. We also prove that many natural results hold in this abstract setting, including representer theorems for parametric methods on their native spaces. The framework also rigorously connects parametric methods with their equivalent nonparametric descriptions under sufficient overparameterization. Classical methods and their native spaces, such as kernel methods / reproducing kernel Hilbert spaces, wavelets / Besov spaces, and shallow neural networks / variation spaces emerge as special cases of our abstract framework. A byproduct of "axiomatizing" the study of representation costs is that we also immediately obtain new results for deep neural networks: For depth-$L$ feedforward ReLU networks, their induced native spaces are $p$-normable quasi-Banach spaces with $p = 2/L$. This reveals that the inductive bias of deep neural networks (as given by the representation cost) cannot be captured by norms for depths $L > 2$.

Amazon-owned MGM Studios' decision to drop the OpenAI movie is just part of AI and film industries becoming increasingly intertwined. On, we take a look at where this is all headed. This week on Uncanny Valley, our hosts discuss Amazon's controversial decision to drop Luca Guadagnino's film about OpenAI's Sam Altman--which reportedly did not paint him in a favorable light. Alongside Google DeepMind's $75 million brand new partnership with indie film studio A24, how much of a dent is AI actually having in the films we see? They also dive into the recent upheaval of workers--from electricians to software engineers--against data centers. Plus: Meta's program to track employees' data gets paused after a massive leak, and Anthropic is now getting along with the government thanks to CEO Dario Amodei no longer being in the room. Write to us at [email protected] . You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link . Before we start, two quick things. If you've been enjoying listening to the show, we would appreciate it if you took a second to rate it in your podcast app of choice. It really helps us reach more people. And second, if you have any questions related to tech, privacy, or politics that you would like me, Zoë, and Leah to take on, now is the time to submit them to [email protected] . It doesn't matter how big or how small, we want to hear from you and get you answers. We're discussing Amazon's MGM Studios' sudden decision to drop the OpenAI biographical movie just as they were wrapping up production.

OpenAI will initially only release ChatGPT 5.6 to government-approved customers OpenAI will initially only release ChatGPT 5.6 to government-approved customers You may not be able to use the new ChatGPT 5.6 as soon as it's finished. According to a report in, OpenAI plans to stagger the release of its new AI model, and the first users will only be parties that are approved by the federal government. The publication's sources said that, according to a staff memo from CEO Sam Altman, federal leaders will be approving access customer by customer during this preview period, hopefully followed a couple of weeks later by a more general release of the 5.6 model. We've made clear to the US government that this is not our preferred long term model, and will work with them and others in industry to achieve a more sustainable approach for future releases, Altman reportedly told employees in the memo. Several agencies appear to be involved in directing the change in course from OpenAI.

When you purchase through links in our articles, we may earn a small commission. Stop asking ChatGPT to rewrite your drafts. Turn ChatGPT into a writing coach rather than a ghostwriter with this prompt. So you've drafted a cover letter for a job application, but it just doesn't feel right. Maybe your writing is a little flat (it's hard to write about yourself, after all) or the letter just comes across as unfocused.

Anthropic accuses Alibaba of'illicitly' accessing AI models Alibaba's American depositary receipts sank to a session low on the news, falling more than 3% to $99.10 at 3:38 p.m. in New York on Wednesday. Anthropic accused Chinese technology giant Alibaba Group Holding of waging a large-scale effort to "illicitly" access its Claude artificial intelligence model using thousands of fraudulent accounts that undermine the U.S. AI developer's decision to keep its products out of China. Anthropic claimed that a campaign by operators linked to Alibaba's Qwen AI lab targeted Claude's most prized capabilities, including software engineering and agentic reasoning, according to a letter that the AI startup sent to several U.S. senators and White House officials. The company said it was the biggest attempt so far by a Chinese company to piggyback on the work of top U.S. labs. In its letter, Anthropic claimed that the effort involved 28.8 million exchanges with Claude between April and June through almost 25,000 fraudulent accounts, according to people familiar with the document and a copy seen by Bloomberg News. The company said the Alibaba campaign resembled past efforts by other Chinese developers that Anthropic flagged in a blog post earlier this year.

GPT-5.5 Instant is now more capable at processing complex questions. OpenAI has updated GPT-5.5 Instant, the model you interact with the most when you use ChatGPT, to be better at understanding context and adapting to queries as you alter them to add more conditions or clarifications. The company updated ChatGPT's default model to GPT-5.5 Instant in May. Back then, it said that the model produced 52.5 percent fewer hallucinated statements during testing and 37.3 percent fewer factual errors. Now, the model has been upgraded to be more capable when it comes to identifying the underlying goal of a task or a question and carrying context over across multiple back-and-forths as you talk to it.

Modeling and sampling from the underlying distribution of asynchronous event sequences are crucial in various real-world applications, including social networks, medical diagnosis, and financial transactions. Existing autoregressive methods suffer from error accumulation during multi-step generation, while non-autoregressive diffusion methods are typically limited to fixed-length output sequences. In this paper, we propose Latent Block-Diffusion Temporal Point Processes (LBDTPP), a novel semi-autoregressive TPP framework that introduces a latent block diffusion mechanism for high-quality and variable-length event sequence generation. The core idea is to define an autoregressive probability distribution over event blocks in latent space and perform Gaussian diffusion within each block. By sequentially generating blocks while simultaneously sampling events in each block, LBDTPP preserves the length flexibility of autoregressive TPPs and inherits the parallel high-quality generation capability of diffusion models. Theoretically, we derive Wasserstein error bounds showing that, under suitable local approximation and prefix-stability assumptions, block-wise generation can reduce error accumulation compared with event-wise autoregressive generation. Extensive experiments on six real-world benchmark datasets demonstrate that LBDTPP outperforms state-of-the-art TPP baselines in both unconditional and conditional generation tasks. Further empirical analyses verify the benefits of latent-space diffusion and block-wise generation, and reveal the trade-off between generation quality and block size. Our code is available at https://github.com/Zh-Shuai/LBDTPP.