arXiv:2601.09757v1 Announce Type: new Abstract: This essay examines how judicial review should adapt to address challenges posed by artificial intelligence decision-making, particularly regarding minority rights and interests. As I argue in this essay, the rise of three trends-privatization, prediction, and automation in AI-have combined to pose similar risks to minorities. Here, I outline what a theory of judicial review would look like in an era of artificial intelligence, analyzing both the limitations and the possibilities of judicial review […]

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arXiv:2601.10626v1 Announce Type: cross Abstract: Differential Privacy (DP) provides a rigorous framework for releasing statistics while protecting individual information present in a dataset. Although substantial progress has been made on differentially private linear regression, existing methods almost exclusively address the item-level DP setting, where each user contributes a single observation. Many scientific and economic applications instead involve longitudinal or panel data, in which each user contributes multiple dependent observations. In these settings, item-level DP offers inadequate protection, and […]

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arXiv:2602.22217v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has established itself as the standard paradigm for grounding Large Language Models (LLMs) in domain-specific, up-to-date data. However, the prevailing architecture for RAG has evolved into a complex, distributed stack requiring cloud-hosted vector databases, heavy deep learning frameworks (e.g., PyTorch, CUDA), and high-latency embedding inference servers. This “infrastructure bloat” creates a significant barrier to entry for edge computing, air-gapped environments, and privacy-constrained applications where data sovereignty is paramount. This paper […]

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arXiv:2601.16994v1 Announce Type: new Abstract: This data paper describes and publicly releases this dataset (v1.0.0), published on Zenodo under DOI 10.5281/zenodo.18189192. Motivated by the need to increase the temporal granularity of originally monthly data to enable more effective training of AI models for epidemiological forecasting, the dataset harmonizes municipal-level dengue hospitalization time series across Brazil and disaggregates them to weekly resolution (epidemiological weeks) through an interpolation protocol with a correction step that preserves monthly totals. The statistical and […]

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arXiv:2601.22539v1 Announce Type: cross Abstract: Humans learn efficiently from their environment by engaging multiple interacting neural systems that support distinct yet complementary forms of control, including model-based (goal-directed) planning, model-free (habitual) responding, and episodic memory-based learning. Model-based mechanisms compute prospective action values using an internal model of the environment, supporting flexible but computationally costly planning; model-free mechanisms cache value estimates and build heuristics that enable fast, efficient habitual responding; and memory-based mechanisms allow rapid adaptation from individual experience. […]

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Deep learning models for radiology interpretation increasingly rely on multi-institutional data, yet privacy regulations and distribution shift across hospitals limit central data pooling. Federated learning (FL) allows hospitals to collaboratively train models without sharing raw images, but current FL algorithms typically assume a static data distribution. In practice, hospitals experience continual evolution in case mix, annotation protocols, and imaging devices, which leads to catastrophic forgetting when models are updated sequentially. Federated continual learning (FCL) aims to reconcile these […]

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Happy Holidays people! If you live in the Bay Area then the next week is probably your time off, so I hope you have fun and enjoy the holiday season! As for Robotics, I just finished Week 2 of Perception, and will probably kick off Week 3 in 2018. I am excited for the last ‘real’ course (Estimation & Learning), and then building my own robot as part of the ‘Capstone’ project after that :-D. This week’s articles: […]

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Email marketing only works if messages reach the inbox. Deliverability testing identifies spam triggers, broken links, and authentication issues. Tools like MailGenius help marketers optimize campaigns, improve engagement, protect sender reputation, and maximize ROI by ensuring emails are seen, not lost to spam or promotions tabs.

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So far, I’ve written about two types of generative models, GAN and VAE. Neither of them explicitly learns the probability density function of real data, $p(mathbf{x})$ (where $mathbf{x} in mathcal{D}$) — because it is really hard! Taking the generative model with latent variables as an example, $p(mathbf{x}) = int p(mathbf{x}vertmathbf{z})p(mathbf{z})dmathbf{z}$ can hardly be calculated as it is intractable to go through all possible values of the latent code $mathbf{z}$.

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In reinforcement learning, we typically aim to optimize the expected value of the sum of rewards an agent collects over a trajectory. However, if the process generating these rewards is non-ergodic, the expected value, i.e., the average over infinitely many trajectories with a given policy, is uninformative for the average over a single, but infinitely long trajectory. Thus, if we care about how the individual agent performs during deployment, the expected value is not a good optimization objective. […]

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