Kolmogorov--Arnold Networks (KANs) have recently emerged as a structured alternative to standard MLPs, yet a principled theory for their training dynamics, generalization, and privacy properties remains limited. In this paper, we analyze gradient descent (GD) for training two-layer KANs and derive general bounds that characterize their training dynamics, generalization, and utility under differential privacy (DP). As a concrete instantiation, we specialize our analysis to logistic loss under an NTK-separable assumption, where we show that polylogarithmic network width suffices for GD to achieve an optimization rate of order $1/T$ and a generalization rate of order $1/n$, with $T$ denoting the number of GD iterations and $n$ the sample size. In the private setting, we characterize the noise required for $(ε,δ)$-DP and obtain a utility bound of order $\sqrt{d}/(nε)$ (with $d$ the input dimension), matching the classical lower bound for general convex Lipschitz problems. Our results imply that polylogarithmic width is not only sufficient but also necessary under differential privacy, revealing a qualitative gap between non-private (sufficiency only) and private (necessity also emerges) training regimes. Experiments further illustrate how these theoretical insights can guide practical choices, including network width selection and early stopping.

The synthetic control method (SCM) estimates causal effects in panel data with a single-treated unit by constructing a counterfactual outcome as a weighted combination of untreated control units that matches the pre-treatment trajectory. In this paper, we introduce the targeted synthetic control (TSC) method, a new two-stage estimator that directly estimates the counterfactual outcome. Specifically, our TSC method (1) yields a targeted debiasing estimator, in the sense that the targeted updating refines the initial weights to produce more stable weights; and (2) ensures that the final counterfactual estimation is a convex combination of observed control outcomes to enable direct interpretation of the synthetic control weights. TSC is flexible and can be instantiated with arbitrary machine learning models. Methodologically, TSC starts from an initial set of synthetic-control weights via a one-dimensional targeted update through the weight-tilting submodel, which calibrates the weights to reduce bias of weights estimation arising from pre-treatment fit. Furthermore, TSC avoids key shortcomings of existing methods (e.g., the augmented SCM), which can produce unbounded counterfactual estimates. Across extensive synthetic and real-world experiments, TSC consistently improves estimation accuracy over state-of-the-art SCM baselines.

We present GROOVE, a semi-supervised multi-modal representation learning approach for high-content perturbation data where samples across modalities are weakly paired through shared perturbation labels but lack direct correspondence. Our primary contribution is GroupCLIP, a novel group-level contrastive loss that bridges the gap between CLIP for paired cross-modal data and SupCon for uni-modal supervised contrastive learning, addressing a fundamental gap in contrastive learning for weakly-paired settings. We integrate GroupCLIP with an on-the-fly backtranslating autoencoder framework to encourage cross-modally entangled representations while maintaining group-level coherence within a shared latent space. Critically, we introduce a comprehensive combinatorial evaluation framework that systematically assesses representation learners across multiple optimal transport aligners, addressing key limitations in existing evaluation strategies. This framework includes novel simulations that systematically vary shared versus modality-specific perturbation effects enabling principled assessment of method robustness. Our combinatorial benchmarking reveals that there is not yet an aligner that uniformly dominates across settings or modality pairs. Across simulations and two real single-cell genetic perturbation datasets, GROOVE performs on par with or outperforms existing approaches for downstream cross-modal matching and imputation tasks. Our ablation studies demonstrate that GroupCLIP is the key component driving performance gains. These results highlight the importance of leveraging group-level constraints for effective multi-modal representation learning in scenarios where only weak pairing is available.

Moltbook is the chaotic future of the internet. The first signs of the apocalypse might look a little like Moltbook: a new social-media platform, launched last week, that is supposed to be populated exclusively by AI bots--1.6 million of them and counting say hello, post software ideas, and exhort other AIs to "stop worshiping biological containers that will rot away." Moltbook was developed as a sort of experimental playground for interactions among AI "agents," which are bots that have access to and can use programs. Claude Code, a popular AI coding tool, has such agentic capabilities, for example: It can act on your behalf to manage files on your computer, send emails, develop and publish apps, and so on. Normally, humans direct an agent to perform specific tasks.

Every day, 50,000 mattresses are tossed in the trash in the United States. A relative of penicillin could be the cure. Breakthroughs, discoveries, and DIY tips sent six days a week. Shopping for a new mattress can be stressful--this is something you plan to sleep on for years to come, after all. But your old one can be its own problem for the environment .

President Donald Trump told NBC's Tom Llamas that the Iranian regime should be "very worried" at the moment amid high tensions between the two countries.

An National Highway Traffic Safety Administration investigation is examining Waymo after one of its self-driving cars struck a child in Santa Monica, California.

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NBC announced that sportscaster and former professional tennis player Mary Carillo will step in for Savannah Guthrie to host the Winter Olympics in Italy.

Suspected Chinese state-backed hackers hijacked the Notepadd++ update infrastructure to deliver a backdoored version of the popular free source code editor and note-taking app for Windows. Infrastructure delivering updates for Notepad++--a widely used text editor for Windows--was compromised for six months by suspected China-state hackers who used their control to deliver backdoored versions of the app to select targets, developers said Monday. "I deeply apologize to all users affected by this hijacking," the author of a post published to the official notepad-plus-plus.org The post said that the attack began last June with an "infrastructure-level compromise that allowed malicious actors to intercept and redirect update traffic destined for notepad-plus-plus.org." The attackers, whom multiple investigators tied to the Chinese government, then selectively redirected certain targeted users to malicious update servers where they received backdoored updates.