Justice Department expands FACE Act enforcement to synagogue protests, with Assistant Attorney General Harmeet Dhillon citing cases against accused protesters.

Robby Starbuck speaks in an interview in New York in March. Robby Starbuck speaks in an interview in New York in March. Critics condemn Robby Starbuck, appointed in lawsuit settlement, for'peddling lies and pushing extremism' A prominent anti-DEI campaigner appointed by Meta in August as an adviser on AI bias has spent the weeks since his appointment spreading disinformation about shootings, transgender people, vaccines, crime, and protests. Robby Starbuck, 36, of Nashville, was appointed in August as an adviser by Meta - owner of Facebook, Instagram, WhatsApp, and other tech platforms - in an August lawsuit settlement. Since his appointment, Starbuck has baselessly claimed that individual shooters in the US were motivated by leftist ideology, described faith-based protest groups as communists, and without evidence tied Democratic lawmakers to murders.

If California is the political fundraising powerhouse of the nation, Silicon Valley has grown into one of the increasingly dominant forces of campaign cash. And while Northern California tech entrepreneurs overwhelmingly support Democratic candidates, a small but powerful group of defectors has moved rightward in recent years. A gathering of tech's conservative cohort enjoyed a visit from former President Trump on Thursday evening at a tony fundraiser held at venture capitalist David Sacks' San Francisco home. The estate, nestled on Billionaires' Row in Pacific Heights, welcomed about 80 elites to the sold-out event. Cost of admission: up to 300,000 per person and 500,000 per couple, according to an invitation obtained by The Times.

The goal of co-clustering is to simultaneously identify a clustering of rows as well as columns of a two dimensional data matrix. A number of co-clustering techniques have been proposed including information-theoretic co-clustering and the minimum sum-squared residue co-clustering method. However, most existing co-clustering algorithms are designed to find pairwise disjoint and exhaustive co-clusters while many real-world datasets contain not only a large overlap between co-clusters but also outliers which should not belong to any co-cluster. In this paper, we formulate the problem of Non-Exhaustive, Overlapping Co-Clustering where both of the row and column clusters are allowed to overlap with each other and outliers for each dimension of the data matrix are not assigned to any cluster. To solve this problem, we propose intuitive objective functions, and develop an an efficient iterative algorithm which we call the NEO-CC algorithm. We theoretically show that the NEO-CC algorithm monotonically decreases the proposed objective functions. Experimental results show that the NEO-CC algorithm is able to effectively capture the underlying co-clustering structure of real-world data, and thus outperforms state-of-the-art clustering and co-clustering methods. This manuscript includes an extended analysis of [21].

AI is just one of several technologies that banks and other financial institutions are using to improve internal processes and bring new experiences to their customers. This is borne out of necessity: if traditional industries don't embrace advanced technologies in the right use cases there is a real chance of disruption. Why would HSBC, for example, let a challenger like Starling Bank out-innovate them? Both the large and emerging players in the finance industry are opening their arms to AI. AI-based chatbots, for example, are increasingly be used as the first point of contact for customers. This point was reiterated by HSBC's AI programme manager, Sebastian Wilson, during a recent roundtable hosted by Information Age -- big banks are not standing still, because they realise the incredible level of service and personalisation that can be achieved when technology is used in the right way.

We consider the problem of matrix completion with side information on an $n\times m$ matrix. We formulate the problem exactly as a sparse regression problem of selecting features and show that it can be reformulated as a binary convex optimization problem. We design OptComplete, based on a novel concept of stochastic cutting planes to enable efficient scaling of the algorithm up to matrices of sizes $n = 10^6$ and $m = 10^5$. We report experiments on both synthetic and real-world datasets that show that OptComplete outperforms current state-of-the-art methods both in terms of accuracy and scalability, while providing insight on the factors that affect the ratings.

Matrix completion method has been used in a wide range of applications such as collaborative filtering for recommendation (Koren et al., 2009), multi-label learning (Cabral et al., 2011) and clustering (Hsieh et al., 2012). In these applications, every entry is modeled as the inner product between factors corresponding to the row and column variables. For example, in movie recommendation, each row factor represents the latent representation of a user and each column factor represents the latent representation of a movie. In many applications of significant interest, besides the partially observed matrix, side information, in the form of features, is also available. These might correspond to demographic information (genders, occupation) for users or product information (genre, director) in a movie recommender system for example. With such features at hand, one can model an observation as a specific linear interaction between features to reduce the model complexity.

Driving long distances (or using New York City's subway system) used to be a much more complicated affair, generally requiring maps, a sense of direction, some luck and the occasional stop to ask questions of strangers. Turn-by-turn navigation apps have changed all that: You may still take a wrong turn along the way, but the apps usually get you back on track with little fuss. Self-service integration specialist SnapLogic is turning to artificial intelligence (AI) to help its customers achieve that sort of turn-by-turn navigation when it comes to enterprise integration. Citing GPS navigation and digital home assistants like Amazon's Alexa, SnapLogic Founder and CEO Gaurav Dhillon says the company's new technology, Iris, will eliminate the integration backlog that stifles so many technology initiatives through the use of AI to automate highly repetitive, low-level development tasks. "Companies can't innovate and transform their businesses if they're bogged down in rote, repetitive tasks that don't do much for the organization," Doug Henschen, vice president and principal analyst at Constellation Research, said in a statement last week.

Driving long distances (or using New York City's subway system) used to be a much more complicated affair, generally requiring maps, a sense of direction, some luck and the occasional stop to ask questions of strangers. Turn-by-turn navigation apps have changed all that: You may still take a wrong turn along the way, but the apps usually get you back on track with little fuss. Self-service integration specialist SnapLogic is turning to artificial intelligence (AI) to help its customers achieve that sort of turn-by-turn navigation when it comes to enterprise integration. Citing GPS navigation and digital home assistants like Amazon's Alexa, SnapLogic Founder and CEO Gaurav Dhillon says the company's new technology, Iris, will eliminate the integration backlog that stifles so many technology initiatives through the use of AI to automate highly repetitive, low-level development tasks. "Companies can't innovate and transform their businesses if they're bogged down in rote, repetitive tasks that don't do much for the organization," Doug Henschen, vice president and principal analyst at Constellation Research, said in a statement last week.

We consider the problem of recommending relevant labels (items) for a given data point (user). In particular, we are interested in the practically important setting where the evaluation is with respect to non-decomposable (over labels) performance metrics like the $F_1$ measure, \emph{and} training data has missing labels. To this end, we propose a generic framework that given a performance metric $\Psi$, can devise a regularized objective function and a threshold such that all the values in the predicted score vector above and only above the threshold are selected to be positive. We show that the regret or generalization error in the given metric $\Psi$ is bounded ultimately by estimation error of certain underlying parameters. In particular, we derive regret bounds under three popular settings: a) collaborative filtering, b) multilabel classification, and c) PU (positive-unlabeled) learning. For each of the above problems, we can obtain precise non-asymptotic regret bound which is small even when a large fraction of labels is missing. Our empirical results on synthetic and benchmark datasets demonstrate that by explicitly modeling for missing labels and optimizing the desired performance metric, our algorithm indeed achieves significantly better performance (like $F_1$ score) when compared to methods that do not model missing label information carefully.