Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech. Meanwhile, in an important case of heterogenous tabular data, the advantage of DNNs over shallow counterparts remains questionable. In particular, there is no sufficient evidence that deep learning machinery allows constructing methods that outperform gradient boosting decision trees (GBDT), which are often the top choice for tabular problems. In this paper, we introduce Neural Oblivious Decision Ensembles (NODE), a new deep learning architecture, designed to work with any tabular data. In a nutshell, the proposed NODE architecture generalizes ensembles of oblivious decision trees, but benefits from both end-to-end gradient-based optimization and the power of multi-layer hierarchical representation learning. With an extensive experimental comparison to the leading GBDT packages on a large number of tabular datasets, we demonstrate the advantage of the proposed NODE architecture, which outperforms the competitors on most of the tasks. We open-source the PyTorch implementation of NODE and believe that it will become a universal framework for machine learning on tabular data. The recent rise of deep neural networks (DNN) resulted in a substantial breakthrough for a large number of machine learning tasks in computer vision, natural language processing, speech recognition, reinforcement learning (Goodfellow et al., 2016). Both gradient-based optimization via back-propagation (Rumelhart et al., 1985) and hierarchical representation learning appear to be crucial in increasing the performance of machine learning for these problems by a large margin. While the superiority of deep architectures in these domains is undoubtful, machine learning for tabular data still did not fully benefit from the DNN power.

We consider the problem of estimating the parameters of a $d$-dimensional rectified Gaussian distribution from i.i.d. samples. A rectified Gaussian distribution is defined by passing a standard Gaussian distribution through a one-layer ReLU neural network. We give a simple algorithm to estimate the parameters (i.e., the weight matrix and bias vector of the ReLU neural network) up to an error $\epsilon||W||_F$ using $\tilde{O}(1/\epsilon^2)$ samples and $\tilde{O}(d^2/\epsilon^2)$ time (log factors are ignored for simplicity). This implies that we can estimate the distribution up to $\epsilon$ in total variation distance using $\tilde{O}(\kappa^2d^2/\epsilon^2)$ samples, where $\kappa$ is the condition number of the covariance matrix. Our only assumption is that the bias vector is non-negative. Without this non-negativity assumption, we show that estimating the bias vector within any error requires the number of samples at least exponential in the infinity norm of the bias vector. Our algorithm is based on the key observation that vector norms and pairwise angles can be estimated separately. We use a recent result on learning from truncated samples. We also prove two sample complexity lower bounds: $\Omega(1/\epsilon^2)$ samples are required to estimate the parameters up to error $\epsilon$, while $\Omega(d/\epsilon^2)$ samples are necessary to estimate the distribution up to $\epsilon$ in total variation distance. The first lower bound implies that our algorithm is optimal for parameter estimation. Finally, we show an interesting connection between learning a two-layer generative model and non-negative matrix factorization. Experimental results are provided to support our analysis.

Whenever a social media user decides to share a story, she is typically pleased to receive likes, comments, shares, or, more generally, feedback from her followers. As a result, she may feel compelled to use the feedback she receives to (re-)estimate her followers' preferences and decides which stories to share next to receive more (positive) feedback. Under which conditions can she succeed? In this work, we first look into this problem from a theoretical perspective and then provide a set of practical algorithms to identify and characterize such behavior in social media. More specifically, we address the above problem from the viewpoint of sequential decision making and utility maximization. For a wide variety of utility functions, we first show that, to succeed, a user needs to actively trade off exploitation-- sharing stories which lead to more (positive) feedback--and exploration-- sharing stories to learn about her followers' preferences. However, exploration is not necessary if a user utilizes the feedback her followers provide to other users in addition to the feedback she receives. Then, we develop a utility estimation framework for observation data, which relies on statistical hypothesis testing to determine whether a user utilizes the feedback she receives from each of her followers to decide what to post next. Experiments on synthetic data illustrate our theoretical findings and show that our estimation framework is able to accurately recover users' underlying utility functions. Experiments on several real datasets gathered from Twitter and Reddit reveal that up to 82% (43%) of the Twitter (Reddit) users in our datasets do use the feedback they receive to decide what to post next.

Learning Y our Way Without Map or Compass: Panoramic T arget Driven Visual Navigation David Watkins-V alls,1, Jingxi Xu,1, Nicholas Waytowich 2 and Peter Allen 1 Abstract -- We present a robot navigation system that uses an imitation learning framework to successfully navigate in complex environments. Our framework takes a pre-built 3D scan of a real environment and trains an agent from pre-generated expert trajectories to navigate to any position given a panoramic view of the goal and the current visual input without relying on map, compass, odometry, GPS or relative position of the target at runtime. Our end-to-end trained agent uses RGB and depth (RGBD) information and can handle large environments (up to 1031 m 2) across multiple rooms (up to 40) and generalizes to unseen targets. We show that when compared to several baselines using deep reinforcement learning and RGBD SLAM, our method (1) requires fewer training examples and less training time, (2) reaches the goal location with higher accuracy, (3) produces better solutions with shorter paths for long-range navigation tasks, and (4) generalizes to unseen environments given an RGBD map of the environment. I NTRODUCTION The ability to navigate efficiently and accurately within an environment is fundamental to intelligent behavior and has been a focus of research in robotics for many years. Traditionally, robotic navigation is solved using model-based methods with an explicit focus on position inference and mapping, such as Simultaneous Localization and Mapping (SLAM) [1]. These models use path planning algorithms, such as Probabilistic Roadmaps (PRM) [2] and Rapidly Exploring Random Trees (RRT) [3], [4] to plan a collision-free path. These methods ignore the rich information from visual input and are highly sensitive to robot odometry and noise in sensor data.

Police officer presence at an intersection discourages a potential traffic violator from violating the law. It also alerts the motorists' consciousness to take precaution and follow the rules. However, due to the abundant intersections and shortage of human resources, it is not possible to assign a police officer to every intersection. In this paper, we propose an intelligent and optimal policing strategy for traffic violation prevention. Our model consists of a specific number of targeted intersections and two police officers with no prior knowledge on the number of the traffic violations in the designated intersections. At each time interval, the proposed strategy, assigns the two police officers to different intersections such that at the end of the time horizon, maximum traffic violation prevention is achieved. Our proposed methodology adapts the PROLA (Play and Random Observe Learning Algorithm) algorithm [1] to achieve an optimal traffic violation prevention strategy . Finally, we conduct a case study to evaluate and demonstrate the performance of the proposed method.

Deep Reinforcement Learning is a promising paradigm for robotic control which has been shown to be capable of learning policies for high-dimensional, continuous control of unmodeled systems. However, RoboticReinforcement Learning currently lacks clearly defined benchmark tasks, which makes it difficult for researchers to reproduce and compare against prior work. ``Reacher'' tasks, which are fundamental to robotic manipulation, are commonly used as benchmarks, but the lack of a formal specification elides details that are crucial to replication. In this paper we present a novel empirical analysis which shows that the unstated spatial constraints in commonly used implementations of Reacher tasks make it dramatically easier to learn a successful control policy with DeepDeterministic Policy Gradients (DDPG), a state-of-the-art Deep RL algorithm. Our analysis suggests that less constrained Reacher tasks are significantly more difficult to learn, and hence that existing de facto benchmarks are not representative of the difficulty of general robotic manipulation.

W e review three limitations of BLEU and ROUGE - the most popul ar metrics used to assess reference summaries against hypothesis summ aries, come up with criteria for what a good metric should behave like and propos e concrete ways to use recent Transformers-based Language Models to assess re ference summaries against hypothesis summaries.

Hierarchical neural networks are often used to model inherent structures within dialogues. For goal-oriented dialogues, these models miss a mechanism adhering to the goals and neglect the distinct conversational patterns between two interlocutors. In this work, we propose Goal-Embedded Dual Hierarchical Attentional Encoder-Decoder (G-DuHA) able to center around goals and capture interlocutor-level disparity while modeling goal-oriented dialogues. Experiments on dialogue generation, response generation, and human evaluations demonstrate that the proposed model successfully generates higher-quality, more diverse and goal-centric dialogues. Moreover, we apply data augmentation via goal-oriented dialogue generation for task-oriented dialog systems with better performance achieved.

The focus of my PhD thesis is on exploring parallel approaches to efficiently solve problems modeled by constraints and presenting a new proposal. Current solvers are very advanced; they are carefully designed to effectively manage the high-level problems' description and include refined strategies to avoid useless work. Despite this, finding a solution can take an unacceptable amount of time. Parallelization can mitigate this problem when the instance of the problem modeled is large, as it happens in real world problems. It is done by propagating constraints in parallel and concurrently exploring different parts of the search space. I am developing on a constraint solver that exploits the many cores available on Graphics Processing Units (GPU) to speed up the search.

We explore the interpretation of sound for robot decision-making, inspired by human speech comprehension. While previous methods use natural language processing to translate sound to text, we propose an end-to-end deep neural network which directly learns control polices from images and sound signals. The network is trained using reinforcement learning with auxiliary losses on the sight and sound network branches. We demonstrate our approach on two robots, a TurtleBot3 and a Kuka-IIWA arm, which hear a command word, identify the associated target object, and perform precise control to reach the target. For both systems, we perform ablation studies in simulation to show the effectiveness of our network empirically. We also successfully transfer the policy learned in simulator to a real-world TurtleBot3, which effectively understands word commands, searches for the object, and moves toward that location with more intuitive motion than a traditional motion planner with perfect information.