Things to Do in L.A. Hollywood director Rob Reiner engineered Proposition 10, a 1998 tobacco tax that created First 5 California, generating more than $11 billion for early childhood programs statewide. This is read by an automated voice. Please report any issues or inconsistencies here . After his tragic death Sunday, the world remembers Rob Reiner as a cinematic force -- and he was one, as an unforgettable presence on the ambitious 1970s sitcom "All in the Family" and later as the director of beloved films. I came to know him differently: as a restless thinker who transformed his own life story into bold public policy, reshaping how California understands and invests in its youngest children.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Rob Reiner used his fame to advocate for progressive causes. This is read by an automated voice. Please report any issues or inconsistencies here . Rob Reiner was a Hollywood legend and also a political force, a frequent voice in progressive causes and a Democratic Party activist.

Things to Do in L.A. Tap to enable a layout that focuses on the article. This is read by an automated voice. Please report any issues or inconsistencies here . Two people were found dead Sunday afternoon at the Brentwood home of director and actor Rob Reiner, multiple law enforcement sources confirmed. Margaret Stewart, a Los Angeles Fire Department spokesman, said the department was called to the home around 3:30 p.m. for medical aid.

The higher-order correlation clustering problem for a graph $G$ and costs associated with cliques of $G$ consists in finding a clustering of $G$ so as to minimize the sum of the costs of those cliques whose nodes all belong to the same cluster. To tackle this NP-hard problem in practice, local search heuristics have been proposed and studied in the context of applications. Here, we establish partial optimality conditions for cubic correlation clustering, i.e., for the special case of at most 3-cliques. We define and implement algorithms for deciding these conditions and examine their effectiveness numerically, on two data sets.

Conditional risk minimization arises in high-stakes decisions where risk must be assessed in light of side information, such as stressed economic conditions, specific customer profiles, or other contextual covariates. Constructing reliable conditional distributions from limited data is notoriously difficult, motivating a series of optimal-transport-based proposals that address this uncertainty in a distributionally robust manner. Yet these approaches remain fragmented, each constrained by its own limitations: some rely on point estimates or restrictive structural assumptions, others apply only to narrow classes of risk measures, and their structural connections are unclear. We introduce a universal framework for distributionally robust conditional risk minimization, built on a novel union-ball formulation in optimal transport. This framework offers three key advantages: interpretability, by subsuming existing methods as special cases and revealing their deep structural links; tractability, by yielding convex reformulations for virtually all major risk functionals studied in the literature; and scalability, by supporting cutting-plane algorithms for large-scale conditional risk problems. Applications to portfolio optimization with rank-dependent expected utility highlight the practical effectiveness of the framework, with conditional models converging to optimal solutions where unconditional ones clearly do not.