Every team deserves a second chance:an extended study on predicting team performance

Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance in finding the correct answer to complex problems. We present a novel benefit of voting, that has not been observed before: we can use the voting patterns to assess the performance of a team and predict their final outcome. This prediction can be executed at any moment during problem-solving and it is completely domain independent. Hence, it can be used to identify when a team is failing, allowing an operator to take remedial procedures (such as changing team members, the voting rule, or increasing the allocation of resources). We present three main theoretical results: (1) we show a theoretical explanation of why our prediction method works; (2) contrary to what would be expected based on a simpler explanation using classical voting models, we show that we can make accurate predictions irrespective of the strength (i.e., performance) of the teams, and that in fact, the prediction can work better for diverse teams composed of different agents than uniform teams made of copies of the best agent; (3) we show that the quality of our prediction increases with the size of the action space. We perform extensive experimentation in two different domains: Computer Go and Ensemble Learning. In Computer Go, we obtain high quality predictions about the final outcome of games. We analyze the prediction accuracy for three different teams with different levels of diversity and strength, and show that the prediction works significantly better for a diverse team. Additionally, we show that our method still works well when trained with games against one adversary, but tested with games against another, showing the generality of the learned functions. Moreover, we evaluate four different board sizes, and experimentally confirm better predictions in larger board sizes. We analyze in detail the learned prediction functions, and how they change according to each team and action space size. In order to show that our method is domain independent, we also present results in Ensemble Learning, where we make online predictions about the performance of a team of classifiers, while they are voting to classify sets of items. We study a set of classical classification algorithms from machine learning, in a data-set of hand-written digits, and we are able to make high-quality predictions about the final performance of two different teams. Since our approach is domain independent, it can be easily applied to a variety of other domains

Simultaneous influencing and mapping for health interventions

Influence Maximization is an active topic, but it was always assumed full knowledge of the social network graph. However, the graph may actually be unknown beforehand. For example, when selecting a subset of a homeless population to attend interventions concerning health, we deal with a network that is not fully known. Hence, we introduce the novel problem of simultaneously influencing and mapping (i.e., learning) the graph. We study a class of algorithms, where we show that: (i) traditional algorithms may have arbitrarily low performance; (ii) we can effectively influence and map when the independence of objectives hypothesis holds; (iii) when it does not hold, the upper bound for the influence loss converges to 0. We run extensive experiments over four real-life social networks, where we study two alternative models, and obtain significantly better results in both than traditional approaches

Female Anopheles gambiae antennae: increased transcript accumulation of the mosquito-specific odorant-binding-protein OBP2

<p>Abstract</p> <p>Background</p> <p>New interventions are required to optimally and sustainably control the <it>Anopheles </it>sp. mosquitoes that transmit malaria and filariasis. The mosquito olfactory system is important in host seeking (transmission) and mate finding (reproduction). Understanding olfactory function could lead to development of control strategies based on repelling parasite-carrying mosquitoes or attracting them into a fatal trap.</p> <p>Findings</p> <p>Our initial focus is on odorant binding proteins with differential transcript accumulation between female and male mosquitoes. We report that the odorant binding protein, OBP2 (AGAP003306), had increased expression in the antennae of female vs. male <it>Anopheles gambiae </it><it>sensu stricto </it>(G3 strain). The increased expression in antennae of females of this gene by quantitative RT-PCR was 4.2 to 32.3 fold in three independent biological replicates and two technical replicate experiments using <it>A. gambiae </it>from two different laboratories. OBP2 is a member of the vast OBP superfamily of insect odorant binding proteins and belongs to the predominantly dipteran clade that includes the <it>Culex </it>oviposition kairomone-binding OBP1. Phylogenetic analysis indicates that its orthologs are present across culicid mosquitoes and are likely to play a conserved role in recognizing a molecule that might be critical for female behavior.</p> <p>Conclusions</p> <p>OBP2 has increased mRNA transcript accumulation in the antennae of female as compared to male <it>A. gambiae</it>. This molecule and related molecules may play an important role in female mosquito feeding and breeding behavior. This finding may be a step toward providing a foundation for understanding mosquito olfactory requirements and developing control strategies based on reducing mosquito feeding and breeding success.</p

Representation Learning for Perception and Control

The goal of extracting reusable and rich representations that capture what you care about for downstream tasks remains challenging even though the field of deep learning has made tremendous progress in this direction. This thesis presents a few promising contributions to further that goal. The two axes of contributions are: (1) self-supervised (or unsupervised) representation learning; (2) deep neural network architectures powered by self-attention. Progress in architectures and the ability to leverage massive amounts of unlabeled data have been responsible for major advances in NLP such as GPT-x and BERT. This thesis presents small steps towards realizing such progress for perceptual and reinforcement learning tasks. This is a thesis by articles containing four articles, two focused on computer vision benchmarks, with the other two focused on reinforcement learning.With respect to the first axis, the thesis presents three articles: (1) Data-Efficient Image Recognition using Contrastive Predictive Coding (CPCv2); (2) Contrastive Unsupervised Representations for Reinforcement Learning (CURL); (3) Reinforcement Learning with Augmented Data (RAD). The first two articles explore a form of unsupervised learning called contrastive learning, a technique better suited for raw inputs such as images compared to generative pre-training that is popular for language. The first article presents results for label-efficient image recognition. The second article presents the benefits of contrastive learning for sample-efficient reinforcement learning from pixels. Contrastive learning in practice is heavily dependent on data augmentations, and the third article presents a detailed investigation and discussion of its role.As for the second axis, the thesis presents a thorough empirical investigation of the benefits of self-attention and Transformer-like architectures for computer vision through the article: Bottleneck Transformers for Visual Recognition. Self-attention has revolutionized language processing but computer vision presents a challenge to vanilla Transformers through high resolution inputs that challenge the quadratic memory and computational complexity of the primitive. The article presents the empirical effectiveness of a straightforward hybrid composed of convolutions and self-attention and unifies the ResNet and Transformer based architecture design for computer vision

Dynamic Action Repetition for Deep Reinforcement Learning

One of the long standing goals of Artificial Intelligence (AI) is to build cognitive agents which can perform complex tasks from raw sensory inputs without explicit supervision. Recent progress in combining Reinforcement Learning objective functions and Deep Learning architectures has achieved promising results for such tasks. An important aspect of such sequential decision making problems, which has largely been neglected, is for the agent to decide on the duration of time for which to commit to actions. Such action repetition is important for computational efficiency, which is necessary for the agent to respond in real-time to events (in applications such as self-driving cars). Action Repetition arises naturally in real life as well as simulated environments. The time scale of executing an action enables an agent (both humans and AI) to decide the granularity of control during task execution. Current state of the art Deep Reinforcement Learning models, whether they are off-policy or on-policy, consist of a framework with a static action repetition paradigm, wherein the action decided by the agent is repeated for a fixed number of time steps regardless of the contextual state while executing the task. In this paper, we propose a new framework - Dynamic Action Repetition which changes Action Repetition Rate (the time scale of repeating an action) from a hyper-parameter of an algorithm to a dynamically learnable quantity. At every decision-making step, our models allow the agent to commit to an action and the time scale of executing the action. We show empirically that such a dynamic time scale mechanism improves the performance on relatively harder games in the Atari 2600 domain, independent of the underlying Deep Reinforcement Learning algorithm used