Neural Information Processing Systems http://nips.cc/

From Montenegro to northern California, the tech elite dream of building cities where they make the rules. Is this, finally, their moment? The shirtless man in the golden mask and cape has plans to lead his own country one day. There is no location yet, but it will be a crypto-and AI-powered paradise of medical experimentation, filled with people who want to "make death optional," he says. For now, though, he's leading a sparsely attended rave on the second floor of a San Francisco office building. A DJ is spinning at one end of an open room. A handful of people sway and jump on the space cleared out as a dance floor. At a nearby table, coffee is available with many alternative milks.

Summary: This paper proposes a modification of Mondorian Forest which is a variant of Random Forest, a majority vote of decision trees. The authors show that the modified algorithm has the consistency property while the original algorithm does not have one. In particular, when the conditional probability function is Lipschitz, the proposed algorithm achieves the minimax error rate, where the lower bound is previously known. Comments: The technical contribution is to refine the original version of the Mondorian Forest and prove its consistency. The theoretical results are nice and solid. The main idea comes from the original algorithm, thus the originality of the paper is a bit incremental.

Sampling-based model predictive control (MPC) optimization methods, such as Model Predictive Path Integral (MPPI), have recently shown promising results in various robotic tasks. However, it might produce an infeasible trajectory when the distributions of all sampled trajectories are concentrated within high-cost even infeasible regions. In this study, we propose a new method called log-MPPI equipped with a more effective trajectory sampling distribution policy which significantly improves the trajectory feasibility in terms of satisfying system constraints. The key point is to draw the trajectory samples from the normal log-normal (NLN) mixture distribution, rather than from Gaussian distribution. Furthermore, this work presents a method for collision-free navigation in unknown cluttered environments by incorporating the 2D occupancy grid map into the optimization problem of the sampling-based MPC algorithm. We first validate the efficiency and robustness of our proposed control strategy through extensive simulations of 2D autonomous navigation in different types of cluttered environments as well as the cartpole swing-up task. We further demonstrate, through real-world experiments, the applicability of log-MPPI for performing a 2D grid-based collision-free navigation in an unknown cluttered environment, showing its superiority to be utilized with the local costmap without adding additional complexity to the optimization problem. A video demonstrating the real-world and simulation results is available at https://youtu.be/_uGWQEFJSN0.

Climate change has been linked with a widespread decline in the ability of many of the world's forests to bounce back after events such as drought and logging. Forests around the world differ in their resilience to disturbances, but relatively little is know about how that resilience is changing over time. To tease out any shifts, Giovanni Forzieri at the University of Florence, Italy, and his colleagues ran a machine learning algorithm on satellite data of global vegetation from 2000 to 2020 to calculate a metric of resilience. Resilience was defined by a forest's ability to avoid shifting state, such as becoming savannah, and withstand perturbations, such as an influx of insect pests. The researchers found that more than half of forests in arid, tropical and temperate regions – where the majority of the world's trees are found – showed a significant decrease in resilience over the two decades.

AutoML addresses the skills gap between the demand for data science talent and the availability of this talent.

The problem with this type of method is that it does not really take advantage of Land use and land cover classification (LUCC) maps from high resolution images. We believe that pixel based spectral remote sensor data are of great interest since they allow to information is not enough to characterize land use and track issues like deforestation/reforestation, water sources land cover classes. For this reason, our goal is to design a reduction, urban growth, or to calculate indicators like a methodology that models classes as areas of correlated pixels.

In this paper we introduce Context-Sensitive Decision Forests - A new perspective to exploit contextual information in the popular decision forest framework for the object detection problem. They are tree-structured classifiers with the ability to access intermediate prediction (here: classification and regression) information during training and inference time. This intermediate prediction is available to each sample, which allows us to develop context-based decision criteria, used for refining the prediction process. In addition, we introduce a novel split criterion which in combination with a priority based way of constructing the trees, allows more accurate regression mode selection and hence improves the current context information. In our experiments, we demonstrate improved results for the task of pedestrian detection on the challenging TUD data set when compared to state-of-the-art methods.

We describe Information Forests, an approach to classification that generalizes Random Forests by replacing the splitting criterion of non-leaf nodes from a discriminative one -- based on the entropy of the label distribution -- to a generative one -- based on maximizing the information divergence between the class-conditional distributions in the resulting partitions. The basic idea consists of deferring classification until a measure of "classification confidence" is sufficiently high, and instead breaking down the data so as to maximize this measure. In an alternative interpretation, Information Forests attempt to partition the data into subsets that are "as informative as possible" for the purpose of the task, which is to classify the data. Classification confidence, or informative content of the subsets, is quantified by the Information Divergence. Our approach relates to active learning, semi-supervised learning, mixed generative/discriminative learning.

Random Forests have been shown to perform very well in propositional learning.  FORF is an upgrade of Random Forests for relational data. In this paper we investigate shortcomings of FORF and propose an alternative algorithm, RF, for generating Random Forests over relational data. RF employs randomly generated relational rules as fully self-contained Boolean tests inside each node in a tree and thus can be viewed as an instance of dynamic propositionalization.  The implementation of RF allows for the simultaneous or parallel growth of all the branches of all the trees in the ensemble in an efficient shared, but still single-threaded way.  Experiments favorably compare RF to both FORF and the combination of static propositionalization together with standard Random Forests. Various strategies for tree initialization and splitting of nodes, as well as resulting ensemble size, diversity, and computational complexity of RF are also investigated.