Colombian court rules Meta was wrong to bar porn star's Instagram account Colombia's highest court has ruled that Meta violated a porn star's right to freedom of expression when it deleted her Instagram account. The South American nation's Constitutional Court said on Friday that the tech firm had removed Esperanza Gómez's account without a clear and transparent justification and without offering similar treatment to other, similar accounts. The 45-year-old, whose account had more than five million followers, is one of Colombia's best known adult content actresses. Meta argued in the case that she had violated its rules on nudity. The company, which also owns Facebook and WhatsApp, did not immediately react to the ruling.

We consider the problem of learning classification trees that are robust to distribution shifts between training and testing/deployment data. This problem arises frequently in high stakes settings such as public health and social work where data is often collected using self-reported surveys which are highly sensitive to e.g., the framing of the questions, the time when and place where the survey is conducted, and the level of comfort the interviewee has in sharing information with the interviewer. We propose a method for learning optimal robust classification trees based on mixed-integer robust optimization technology. In particular, we demonstrate that the problem of learning an optimal robust tree can be cast as a single-stage mixed-integer robust optimization problem with a highly nonlinear and discontinuous objective. We reformulate this problem equivalently as a two-stage linear robust optimization problem for which we devise a tailored solution procedure based on constraint generation. We evaluate the performance of our approach on numerous publicly available datasets, and compare the performance to a regularized, non-robust optimal tree. We show an increase of up to 12.48% in worst-case accuracy and of up to 4.85% in average-case accuracy across several datasets and distribution shifts from using our robust solution in comparison to the non-robust one.

Decision trees are among the most popular machine learning models and are used routinely in applications ranging from revenue management and medicine to bioinformatics. In this paper, we consider the problem of learning optimal binary classification trees with univariate splits. Literature on the topic has burgeoned in recent years, motivated both by the empirical suboptimality of heuristic approaches and the tremendous improvements in mixed-integer optimization (MIO) technology. Yet, existing MIO-based approaches from the literature do not leverage the power of MIO to its full extent: they rely on weak formulations, resulting in slow convergence and large optimality gaps. To fill this gap in the literature, we propose an intuitive flow-based MIO formulation for learning optimal binary classification trees. Our formulation can accommodate side constraints to enable the design of interpretable and fair decision trees. Moreover, we show that our formulation has a stronger linear optimization relaxation than existing methods in the case of binary data. We exploit the decomposable structure of our formulation and max-flow/min-cut duality to derive a Benders' decomposition method to speed-up computation. We propose a tailored procedure for solving each decomposed subproblem that provably generates facets of the feasible set of the MIO as constraints to add to the main problem. We conduct extensive computational experiments on standard benchmark datasets on which we show that our proposed approaches are 29 times faster than state-of-the-art MIO-based techniques and improve out-of-sample performance by up to 8%.

Ridge regularized sparse regression involves selecting a subset of features that explains the relationship between a design matrix and an output vector in an interpretable manner. To select the sparsity and robustness of linear regressors, techniques like leave-one-out cross-validation are commonly used for hyperparameter tuning. However, cross-validation typically increases the cost of sparse regression by several orders of magnitude. Additionally, validation metrics are noisy estimators of the test-set error, with different hyperparameter combinations giving models with different amounts of noise. Therefore, optimizing over these metrics is vulnerable to out-of-sample disappointment, especially in underdetermined settings. To address this, we make two contributions. First, we leverage the generalization theory literature to propose confidence-adjusted variants of leave-one-out that display less propensity to out-of-sample disappointment. Second, we leverage ideas from the mixed-integer literature to obtain computationally tractable relaxations of confidence-adjusted leave-one-out, thereby minimizing it without solving as many MIOs. Our relaxations give rise to an efficient coordinate descent scheme which allows us to obtain significantly lower leave-one-out errors than via other methods in the literature. We validate our theory by demonstrating we obtain significantly sparser and comparably accurate solutions than via popular methods like GLMNet and suffer from less out-of-sample disappointment. On synthetic datasets, our confidence adjustment procedure generates significantly fewer false discoveries, and improves out-of-sample performance by 2-5% compared to cross-validating without confidence adjustment. Across a suite of 13 real datasets, a calibrated version of our procedure improves the test set error by an average of 4% compared to cross-validating without confidence adjustment.

Multi-Agent Pathfinding (MAPF) over grids is the problem of finding n non-conflicting paths that lead n agents from a given initial cell to a given goal cell. Cost-optimal MAPF in addition minimizes the total number of actions performed by each agent before stopping at the goal. Being a combinatorial problem in nature, a number of compilations from MAPF to Answer Set Programming (ASP) exist. In this paper we propose a new one, which unlike existing ASP approaches (1) produces cost-optimal solutions, (2) exploits information that can be pre-computed quickly using Dijkstra's algorithm, and (3) when grounded, produces a number of clauses that grows linearly with the number of agents. In our empirical evaluation, in which we use the clasp solver, we show that our approach is superior to heuristic-search-based algorithms in various settings.

This paper presents a novel method for controlling teams of unmanned aerial vehicles using Stochastic Optimal Control (SOC) theory. The approach consists of a centralized high-level planner that computes optimal state trajectories as velocity sequences, and a platform-specific low-level controller which ensures that these velocity sequences are met. The planning task is expressed as a centralized path-integral control problem, for which optimal control computation corresponds to a probabilistic inference problem that can be solved by efficient sampling methods. Through simulation we show that our SOC approach (a) has significant benefits compared to deterministic control and other SOC methods in multimodal problems with noise-dependent optimal solutions, (b) is capable of controlling a large number of platforms in real-time, and (c) yields collective emergent behaviour in the form of flight formations. Finally, we show that our approach works for real platforms, by controlling a team of three quadrotors in outdoor conditions.

Former science teacher Berna Gómez played a pivotal role in new research on restoring some sight to blind people. She is named as a co-author of the study that was published this week. Former science teacher Berna Gómez played a pivotal role in new research on restoring some sight to blind people. She is named as a co-author of the study that was published this week. A former science teacher who's been blind for 16 years became able to see letters, discern objects' edges -- and even play a Maggie Simpson video game -- thanks to a visual prosthesis that includes a camera and a brain implant, according to American and Spanish researchers who collaborated on the project.

Using comment information available from Digg we define a co-participation network between users. We focus on the analysis of this implicit network, and study the behavioral characteristics of users. We use the comment data and social network derived features to predict the popularity of online content linked at Digg using a classification and regression framework. We also compare network properties of our co-participation network to a previously defined reply-answer network on news forums.