Every story across every category, newest first. Each card links to the original publisher; daily-brief posts open as editorial pages.
arXiv:2310.15976v4 Announce Type: replace-cross Abstract: signSGD is attractive in nonconvex optimization because it communicates sign-valued rather than full-precision gradients. Several standard analyses assume independent stochastic-gradient samples,…
arXiv:2608.10845v1 Announce Type: new Abstract: Spectral clustering methods for network data are commonly based on a few matrix representations, such as the adjacency matrix and the…
arXiv:2608.10896v1 Announce Type: new Abstract: Constant-stepsize temporal-difference (TD) learning is attractive for policy evaluation, but inference from a single Markov trajectory must account for serial dependence…
arXiv:2608.10155v1 Announce Type: cross Abstract: Powerful black-box predictive models have motivated many proposals for combining observed data with predictions to perform valid statistical inference. Despite this…
arXiv:2606.15237v1 Announce Type: cross Abstract: Ensemble classifiers are predictive models that combine the results of simpler base models, often by majority vote. A classic example is…
arXiv:2608.10305v1 Announce Type: cross Abstract: Observational datasets frequently contain many baseline variables, yet investigators estimating causal effects may not know which variables to include in the…
arXiv:2608.10184v1 Announce Type: cross Abstract: Let $x_1,ldots,x_n$ be independent standard Gaussian vectors in $mathbb{R}^d$. An emph{ellipsoid fit} is a matrix $S succeq 0$ such that $x_i^top…
arXiv:2602.20403v2 Announce Type: replace-cross Abstract: We study distributionally robust online learning, where a risk-averse learner updates decisions sequentially to guard against worst-case distributions drawn from a…
arXiv:2601.18128v2 Announce Type: replace Abstract: High-dimensional data often exhibit variation that can be captured by lower-dimensional factors. For high-dimensional data from multiple studies, one goal is…
arXiv:2606.00643v2 Announce Type: replace Abstract: Physics-Informed Neural Networks (PINNs) often train slowly or fail to converge on challenging partial differential equations (PDEs), a behavior recently linked…
arXiv:2603.10485v3 Announce Type: replace Abstract: In this work, we study the convergence properties of the Dual Space Preconditioned Gradient Descent, encompassing optimizers such as Normalized Gradient…
arXiv:2608.10245v1 Announce Type: new Abstract: Inventory and distribution planning in Physical Internet networks requires coordinating factory-hub assignments, factory supply, lateral transshipment among collaborative hubs, retailer deliveries,…
arXiv:2608.10425v1 Announce Type: new Abstract: Evolutionary multitasking is a recent approach that solves multiple related optimization problems within a single evolutionary run, rather than addressing each…
arXiv:2608.10323v1 Announce Type: cross Abstract: Competitive artificial-life systems can rank trained controllers differently under training and ecological evaluation. We present Neuroevolution Arena, a GPU-accelerated spatial ecology…
arXiv:2608.10450v1 Announce Type: cross Abstract: Complex software systems develop over timescales that exceed the lifespan of any individual coding agent. Most agentic software systems preserve continuity…
arXiv:2608.10694v1 Announce Type: cross Abstract: Evolutionary optimization of LLM prompts and agentic programs (e.g., GEPA) is dominated by fitness evaluation: scoring each candidate runs an answering…
arXiv:2405.03728v3 Announce Type: replace Abstract: Zero-shot optimization involves optimizing a target task that was not seen during training, aiming to provide the optimal solution without or…
arXiv:2608.10795v1 Announce Type: cross Abstract: Successful mutation strategies in evolutionary code search may contain reusable knowledge that is useful beyond a single run, and in some…
arXiv:2509.03140v2 Announce Type: replace Abstract: We demonstrate that local sensing is sufficient for effective global reconfiguration of homogeneous pivoting cube modular robots in two dimensions. While…
arXiv:2608.10144v1 Announce Type: new Abstract: We consider federated parameter efficient fine-tuning of large neural networks with low-rank adaptation (LoRA,~Hu et al. 2022). Combining LoRA with federated…
arXiv:2605.21610v2 Announce Type: replace Abstract: Antibody design methods condition on antigen structure to generate complementarity-determining regions (CDR), yet a systematic evaluation of baseline methods reveals that…
arXiv:2608.10126v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) aggregates heterogeneous preferences into a single reward model, assuming preference homogeneity. When preferences are heterogeneous,…
arXiv:2608.10120v1 Announce Type: new Abstract: Modern sequence models, from Transformers to State Space Models, have enabled powerful generative modeling across diverse domains, yet they are typically…
arXiv:2505.22839v2 Announce Type: replace Abstract: Recent studies suggest that diffusion models significantly improve the empirical adversarial robustness of deep neural network models. While intuitive explanations have…