Recognizing food types through sensor signals for unseen users remains remarkably challenging despite extensive recent studies. The efficacy of prior machine learning techniques is dwarfed by giant variations of data collected from multiple participants, partly because users have varied chewing habits and wear sensor devices in various manners. This work treats the problem as an instance of the domain adaptation problem, where each user represents a domain. We develop the first multi-source domain adaptation (MSDA) method for food-typing recognition, which consists of three major components: stratified normalization, a multi-source domain adaptor, and adaptive ensemble learning. New techniques are developed for each component. Using a real-world dataset comprised of 15 participants, we demonstrate that our method achieves \(1.33\times\) to \(2.13\times\) improvement in accuracy compared with nine state-of-the-art MSDA baselines. Additionally, we perform an in-depth ablation study to examine the behavior of each component and confirm its efficacy.

Given historical traffic distributions and associated urban conditions observed in a city, the conditional urban traffic estimation problem aims at estimating realistic future projections of the traffic under a set of new urban conditions, e.g., new bus routes, rainfall intensity, and travel demands. The problem is important in reducing traffic congestion, improving public transportation efficiency, and facilitating urban planning. However, solving this problem is challenging due to the strong spatial dependencies of traffic patterns and the complex relations between the traffic and urban conditions. Recently, we proposed a Complex-Condition-Controlled Generative Adversarial Network ( \(\boldsymbol{C^{3}}\) -GAN) , which tackles both of the challenges and solves the urban traffic estimation problem under various complex conditions by adding a fixed embedding network and an inference network on top of the standard conditional GAN model. The randomly chosen embedding network transforms the complex conditions to latent vectors, and the inference network enhances the connections between the embedded vectors and the traffic data. However, a randomly chosen embedding network cannot always successfully extract features of complex urban conditions, which indicates \(C^{3}\) -GAN is unable to uniquely map different urban conditions to proper latent distributions. Thus, \(C^{3}\) -GAN would fail in certain traffic estimation tasks. Besides, \(C^{3}\) -GAN is hard to train due to vanishing gradients and mode collapse problems. To address these issues, in this article, we extend our prior work by introducing a new deep generative model, namely, \(C^{3}\) -GAN \(+\) , which significantly improves the estimation performance and model stability. \(C^{3}\) -GAN \(+\) has new objective, architecture, and training algorithm. The new objective applies Wasserstein loss to the conditional generation case to encourage stable training. Shared convolutional layers between the discriminator and the inference network help to capture spatial dependencies of traffic more efficiently, part of the shared convolutional layers are used to update the embedding network periodically aiming to encourage good representation and avoid model divergence. Extensive experiments on real-world datasets demonstrate that our \(C^{3}\) -GAN \(+\) produces high-quality traffic estimations and outperforms state-of-the-art baseline methods.

Personalized recommender systems play a crucial role in modern society, especially in e-commerce, news, and ads areas. Correctly evaluating and comparing candidate recommendation models is as essential as constructing ones. The common offline evaluation strategy is holding out some user-interacted items from training data and evaluating the performance of recommendation models based on how many items they can retrieve. Specifically, for any hold-out item or so-called target item for a user, the recommendation models try to predict the probability that the user would interact with the item and rank it among overall items, which is called global evaluation . Intuitively, a good recommendation model would assign high probabilities to such hold-out/target items. Based on the specific ranks, some metrics like Recall@K and NDCG@K can be calculated to further quantify the quality of the recommender model. Instead of ranking the target items among all items, Koren first proposed to rank them among a small sampled set of items , then quantified the performance of the models, which is called sampling evaluation . Ever since then, there has been a large amount of work adopting sampling evaluation due to its efficiency and frugality. In recent work, Rendle and Krichene argued that the sampling evaluation is “inconsistent” with respect to a global evaluation in terms of offline top- K metrics. In this work, we first investigate the “inconsistent” phenomenon by taking a glance at the connections between sampling evaluation and global evaluation. We reveal the approximately linear relationship between sampling with respect to its global counterpart in terms of the top- K Recall metric. Second, we propose a new statistical perspective of the sampling evaluation—to estimate the global rank distribution of the entire population. After the estimated rank distribution is obtained, the approximation of the global metric can be further derived. Third, we extend the work of Krichene and Rendle, directly optimizing the error with ground truth, providing not only a comprehensive empirical study but also a rigorous theoretical understanding of the proposed metric estimators. To address the “blind spot” issue, where accurately estimating metrics for small top- K values in sampling evaluation is challenging, we propose a novel adaptive sampling method that generalizes the expectation-maximization algorithm to this setting. Last but not least, we also study the user sampling evaluation effect. This series of works outlines a clear roadmap for sampling evaluation and establishes a foundational theoretical framework. Extensive empirical studies validate the reliability of the sampling methods presented.

Disaggregating compute from storage is an emerging trend in cloud computing. Effectively utilizing resources in both compute and storage pool is the key to high performance. The state-of-the-art scheduler provides optimal scheduling decisions for workloads with homogeneous tasks. However, cloud applications often generate a mix of tasks with diverse compute and IO characteristics, resulting in sub-optimal performance for existing solutions. We present Pyxis, a system that provides optimal scheduling decisions for mixed workloads in disaggregated datacenters with theoretical guarantees. Pyxis is capable of maximizing overall throughput while meeting latency SLOs. Pyxis decouples the scheduling of different tasks. Our insight is that the optimal solution has an “all-or-nothing” structure that can be captured by a singleturning pointin the spectrum of tasks. Based on task characteristics, the turning point partitions the tasks either all to storage nodes or all to compute nodes (none to storage nodes). We theoretically prove that the optimal solution has such a structure, and design an online algorithm with sub-second convergence. We implement a prototype of Pyxis. Experiments on CloudLab with various synthetic and application workloads show that Pyxis improves the throughput by 3–21× over the state-of-the-art solution.

This paper revisits building machine learning algorithms that involve interactions between entities, such as those between financial assets in an actively managed portfolio, or interactions between users in a social network. Our goal is to forecast the future evolution of ensembles of multivariate time series in such applications (e.g., the future return of a financial asset or the future popularity of a Twitter account). Designing ML algorithms for such systems requires addressing the challenges of high-dimensional interactions and non-linearity. Existing approaches usually adopt an ad-hoc approach to integrating high-dimensional techniques into non-linear models and recent studies have shown these approaches have questionable efficacy in time-evolving interacting systems. To this end, we propose a novel framework, which we dub as the additive influence model. Under our modeling assumption, we show that it is possible to decouple the learning of high-dimensional interactions from the learning of non-linear feature interactions. To learn the high-dimensional interactions, we leverage kernel-based techniques, with provable guarantees, to embed the entities in a low-dimensional latent space. To learn the non-linear feature-response interactions, we generalize prominent machine learning techniques, including designing a new statistically sound non-parametric method and an ensemble learning algorithm optimized for vector regressions. Extensive experiments on two common applications demonstrate that our new algorithms deliver significantly stronger forecasting power compared to standard and recently proposed methods.

Since Rendle and Krichene argued that commonly used sampling-based evaluation metrics are ``inconsistent'' with respect to the global metrics (even in expectation), there have been a few studies on the sampling-based recommender system evaluation. Existing methods try either mapping the sampling-based metrics to their global counterparts or more generally, learning the empirical rank distribution to estimate the top-K metrics. However, despite existing efforts, there is still a lack of rigorous theoretical understanding of the proposed metric estimators, and the basic item sampling also suffers from the ``blind spot'' issue, i.e., estimation accuracy to recover the top-K metrics when K is small can still be rather substantial. In this paper, we provide an in-depth investigation into these problems and make two innovative contributions. First, we propose a new item-sampling estimator that explicitly optimizes the error with respect to the ground truth, and theoretically highlights its subtle difference against prior work. Second, we propose a new adaptive sampling method that aims to deal with the ``blind spot'' problem and also demonstrate the expectation-maximization (EM) algorithm can be generalized for such a setting. Our experimental results confirm our statistical analysis and the superiority of the proposed works. This study helps lay the theoretical foundation for adopting item sampling metrics for recommendation evaluation and provides strong evidence for making item sampling a powerful and reliable tool for recommendation evaluation.

The two leading solution paradigms for imitation learning (IL), BC and GAIL, each suffers from notable drawbacks. BC, a supervised learning approach to mimic expert actions, is vulnerable to covariate shift. GAIL applies adversarial training to minimize the discrepancy between expert and learner behaviors, which is prone to unstable training and mode collapse. In this work, we propose DC – Distributional Cloning – a novel IL approach for addressing the covariate shift and mode collapse problems simultaneously. DC directly maximizes the likelihood of observed expert and learner demonstrations, and gradually encourages the learner to evolve towards expert behaviors based on an averaging effect. The DC solution framework contains two stages in each training loop, where in stage one the mixed expert and learner state distribution is estimated via SoftFlow, and in stage two the learner policy is trained to match both the expert’s policy and state distribution via ADMM. Experimental evaluation of DC compared with several baselines in 10 different physics-based control tasks reveal superior results in learner policy performance, training stability, and mode distribution preservation.

We present the design and the implementation of a kernel principal component regression software that handles training datasets with a million or more observations. Kernel regressions are nonlinear and interpretable models that have wide downstream applications, and are shown to have a close connection to deep learning. Nevertheless, the exact regression of large-scale kernel models using currently available software has been notoriously difficult because it is both compute and memory intensive and it requires extensive tuning of hyperparameters.

Lightweight neural networks refer to deep networks with small numbers of parameters, which can be deployed in resource-limited hardware such as embedded systems. To learn such lightweight networks effectively and efficiently, in this paper we propose a novel convolutional layer, namely Channel-Split Recurrent Convolution (CSR-Conv), where we split the output channels to generate data sequences with length T as the input to the recurrent layers with shared weights. As a consequence, we can construct lightweight convolutional networks by simply replacing (some) linear convolutional layers with CSR-Conv layers. We prove that under mild conditions the model size decreases with the rate of $O\left( {\frac{1}{{{T^2}}}} \right)$. Empirically we demonstrate the state-of-the-art performance using VGG-16, ResNet-50, ResNet-56, ResNet-110, DenseNet-40, MobileNet, and EfficientNet as backbone networks on CIFAR-10 and ImageNet. Codes can be found on https://github.com/tuaxon/CSR_Conv.

Automatic food type recognition is an essential task of dietary monitoring. It helps medical professionals recognize a user's food contents, estimate the amount of energy intake, and design a personalized intervention model to prevent many chronic diseases, such as obesity and heart disease. Various wearable and mobile devices are utilized as platforms for food type recognition. However, none of them has been widely used in our daily lives and, at the same time, socially acceptable enough for continuous wear. In this paper, we propose a food type recognition method that takes advantage of Airpods Pro, a pair of widely used wireless in-ear headphones designed by Apple, to recognize 20 different types of food. As far as we know, we are the first to use this socially acceptable commercial product to recognize food types. Audio and motion sensor data are collected from Airpods Pro. Then 135 representative features are extracted and selected to construct the recognition model using the lightGBM algorithm. A real-world data collection is conducted to comprehensively evaluate the performance of the proposed method for seven human subjects. The results show that the average f1-score reaches 94.4% for the ten-fold cross-validation test and 96.0% for the self-evaluation test.

Given historical traffic distributions and associated urban conditions observed in a city, the conditional urban traffic estimation problem aims at estimating realistic future projections of the traffic under a set of new urban conditions, e.g., new bus routes, rainfall intensity and travel demands. The problem is important in reducing traffic congestion, improving public transportation efficiency, and facilitating urban planning. However, solving this problem is challenging due to the strong spatial dependencies of traffic patterns and the complex relations between the traffic and urban conditions. In this paper, we tackle the challenges by proposing a novel Complex-Condition-Controlled Urban Traffic Estimation through Generative Adversarial Networks (C3-GAN) for urban traffic estimation of a region under various complex conditions. C3-GAN features the following three novel designs on top of standard cGAN model: (1) an embedding network mapping the complex conditions to a latent space to find representations of the urban conditions; (2) an inference network to enhance the relations between the embedded latent vectors and the traffic data. Extensive experiments on real-world datasets demonstrate that our C3-GAN produces high-quality traffic estimations and outperforms state-of-the-art baseline methods.

Large-scale interactive web services and advanced AI applications make sophisticated decisions in real-time, based on executing a massive amount of computation tasks on thousands of servers. Task schedulers, which often operate in heterogeneous and volatile environments, require high throughput, i.e., scheduling millions of tasks per second, and low latency, i.e., incurring minimal scheduling delays for millisecond-level tasks. Scheduling is further complicated by other users’ workloads in a shared system, other background activities, and the diverse hardware configurations inside datacenters.We present Rosella, a new self-driving, distributed approach for task scheduling in heterogeneous clusters. Rosella automatically learns the compute environment and adjusts its scheduling policy in real-time. The solution provides high throughput and low latency simultaneously because it runs in parallel on multiple machines with minimum coordination and only performs simple operations for each scheduling decision. Our learning module monitors total system load and uses the information to dynamically determine optimal estimation strategy for the backends’ compute-power. Rosella generalizes power-of-two-choice algorithms to handle heterogeneous workers, reducing the max queue length of O(logn) obtained by prior algorithms to O(logn). We evaluate Rosella with a variety of workloads on a 32-node AWS cluster. Experimental results show that Rosella significantly reduces task response time, and adapts to environment changes quickly.

Python has become a popular programming language because of its excellent programmability. Many modern software packages utilize Python for high-level algorithm design and depend on native libraries written in C/C++/Fortran for efficient computation kernels. Interaction between Python code and native libraries introduces performance losses because of the abstraction lying on the boundary of Python and native libraries. On the one side, Python code, typically run with interpretation, is disjoint from its execution behavior. On the other side, native libraries do not include program semantics to understand algorithm defects.

Existing topic modeling and text segmentation methodologies generally require large datasets for training, limiting their capabilities when only small collections of text are available. In this work, we reexamine the inter-related problems of “topic identification” and “text segmentation” for sparse document learning, when there is a single new text of interest. In developing a methodology to handle single documents, we face two major challenges. First is sparse information : with access to only one document, we cannot train traditional topic models or deep learning algorithms. Second is significant noise : a considerable portion of words in any single document will produce only noise and not help discern topics or segments. To tackle these issues, we design an unsupervised, computationally efficient methodology called Biclustering Approach to Topic modeling and Segmentation (BATS). BATS leverages three key ideas to simultaneously identify topics and segment text: (i) a new mechanism that uses word order information to reduce sample complexity, (ii) a statistically sound graph-based biclustering technique that identifies latent structures of words and sentences, and (iii) a collection of effective heuristics that remove noise words and award important words to further improve performance. Experiments on six datasets show that our approach outperforms several state-of-the-art baselines when considering topic coherence, topic diversity, segmentation, and runtime comparison metrics.

Drug repositioning has received ever-increasing attention in the field of drug discovery over the last few years. However, the high efficient prediction methods taking full advantage of heterogeneous information networks (HINs) still deserves further research. To this end, this paper proposes an approach for drug repositioning via integrating HINs embedding and link prediction for more potential drug-target interactions. To utilize multiple side information, we introduce a graph convolutional network (GCN) based embedding method for HINs. The obtained drug-related and target-related information is adopted to improve the low-rank matrix completion (LRMC) model. Moreover, a regulation for alleviating the noise of negative samples is designed to enhance the optimization of LRMC. The experiments conducted on the comparative database demonstrate that the proposed method is more effective than the existing approaches in the prediction of drug repositioning.

Learning to make optimal decisions is a common yet complicated task. While computer agents can learn to make decisions by running reinforcement learning (RL), it remains unclear how human beings learn. In this paper, we perform the first data-driven case study on taxi drivers to validate whether humans mimic RL to learn. We categorize drivers into three groups based on their performance trends and analyze the correlations between human drivers and agents trained using RL. We discover that drivers that become more efficient at earning over time exhibit similar learning patterns to those of agents, whereas drivers that become less efficient tend to do the opposite. Our study (1) provides evidence that some human drivers do adapt RL when learning, (2) enhances the deep understanding of taxi drivers' learning strategies, (3) offers a guideline for taxi drivers to improve their earnings, and (4) develops a generic analytical framework to study and validate human learning strategies.

Efficient construction of checkpoints/snapshots is a critical tool for training and diagnosing deep learning models. In this paper, we propose a lossy compression scheme for checkpoint constructions (called LC-Checkpoint). LC-Checkpoint simultaneously maximizes the compression rate and optimizes the recovery speed, under the assumption that SGD is used to train the model. LC-Checkpoint uses quantization and priority promotion to store the most crucial information for SGD to recover, and then uses a Huffman coding to leverage the non-uniform distribution of the gradient scales. Our extensive experiments show that LC-Checkpoint achieves a compression rate up to 28{\texttimes} and recovery speedup up to 5.77{\texttimes} over a state-of-the-art algorithm (SCAR).

We study the low rank regression problem y = Mx + ε, where x and y are d1 and d2 dimensional vectors respectively. We consider the extreme high-dimensional setting where the number of observations n is less than d1 + d2. Existing algorithms are designed for settings where n is typically as large as rank(M)(d1+d2). This work provides an efficient algorithm which only involves two SVD, and establishes statistical guarantees on its performance. The algorithm decouples the problem by first estimating the precision matrix of the features, and then solving the matrix denoising problem. To complement the upper bound, we introduce new techniques for establishing lower bounds on the performance of any algorithm for this problem. Our preliminary experiments confirm that our algorithm often out-performs existing baseline, and is always at least competitive.

Many real-world human behaviors can be modeled and characterized as sequential decision-making processes, such as a taxi driver’s choices of working regions and times. Each driver possesses unique preferences on the sequential choices over time and improves the driver’s working efficiency. Understanding the dynamics of such preferences helps accelerate the learning process of taxi drivers. Prior works on taxi operation management mostly focus on finding optimal driving strategies or routes, lacking in-depth analysis on what the drivers learned during the process and how they affect the performance of the driver. In this work, we make the first attempt to establish Dynamic Human Preference Analytics. We inversely learn the taxi drivers’ preferences from data and characterize the dynamics of such preferences over time. We extract two types of features (i.e., profile features and habit features) to model the decision space of drivers. Then through inverse reinforcement learning, we learn the preferences of drivers with respect to these features. The results illustrate that self-improving drivers tend to keep adjusting their preferences to habit features to increase their earning efficiency while keeping the preferences to profile features invariant. However, experienced drivers have stable preferences over time. The exploring drivers tend to randomly adjust the preferences over time.

This paper studies a stylized, yet natural, learning-to-rank problem and points out the critical incorrectness of a widely used nearest neighbor algorithm. We consider a model with n agents (users) {xi}i∈[n] and m alternatives (items) {yl}l∈[m], each of which is associated with a latent feature vector. Agents rank items nondeterministically according to the Plackett-Luce model, where the higher the utility of an item to the agent, the more likely this item will be ranked high by the agent. Our goal is to identify near neighbors of an arbitrary agent in the latent space for prediction.We first show that the Kendall-tau distance based kNN produces incorrect results in our model. Next, we propose a new anchor-based algorithm to find neighbors of an agent. A salient feature of our algorithm is that it leverages the rankings of many other agents (the so-called “anchors”) to determine the closeness/similarities of two agents. We provide a rigorous analysis for one-dimensional latent space, and complement the theoretical results with experiments on synthetic and real datasets. The experiments confirm that the new algorithm is robust and practical.

Molecular graph generation aims to design molecules with desired biochemical properties, which is promising in drug discovery. Existing methods typically combine deep reinforcement models with adversarial training. However, the reinforced rewards in molecule generation are delayed and sparse while adversarial training suffers mode collapse issue. Moreover, they optimize multiple properties with a linear combination manner, where the models get distracted by potentially conflicting objectives. To tackle the above challenges, we propose a Deep Reinforced framework with Adversarial Imitation and Multitask learning (DR-AIM). First, the reinforced agent generates discrete molecular graphs via deep Q-learning, where the trajectories of high rewards are cached as experts. Then policies are extracted directly from expert trajectories by adversarial imitation learning, in which the discriminator delivers behavior distribution signals to the agent as dense rewards. Second, we propose to realize multi-goal molecule generation as a multitask learning process, where different property optimizations are treated as different tasks to be trained jointly. Extensive experiments demonstrate the effectiveness of DR-AIM in molecule generation.

Many real world human behaviors can be modeled and characterized as sequential decision making processes, such as taxi driver's choices of working regions and times. Each driver possesses unique preferences on the sequential choices over time and improves their working efficiency. Understanding the dynamics of such preferences helps accelerate the learning process of taxi drivers. Prior works on taxi operation management mostly focus on finding optimal driving strategies or routes, lacking in-depth analysis on what the drivers learned during the process and how they affect the performance of the driver. In this work, we make the first attempt to inversely learn the taxi drivers' preferences from data and characterize the dynamics of such preferences over time. We extract two types of features, i.e., profile features and habit features, to model the decision space of drivers. Then through inverse reinforcement learning we learn the preferences of drivers with respect to these features. The results illustrate that self-improving drivers tend to keep adjusting their preferences to habit features to increase their earning efficiency, while keeping the preferences to profile features invariant. On the other hand, experienced drivers have stable preferences over time.

Deep learning shows great promise in providing more intelligence to augmented reality (AR) devices, but few AR apps use deep learning due to lack of infrastructure support. Deep learning algorithms are computationally intensive, and front-end devices cannot deliver sufficient compute power for real-time processing. In this work, we design a framework that ties together front-end devices with more powerful backend “helpers” (e.g., home servers) to allow deep learning to be executed locally or remotely in the cloud/edge. We consider the complex interaction between model accuracy, video quality, battery constraints, network data usage, and network conditions to determine an optimal offloading strategy. Our contributions are: (1) extensive measurements to understand the tradeoffs between video quality, network conditions, battery consumption, processing delay, and model accuracy; (2) a measurement-driven mathematical framework that efficiently solves the resulting combinatorial optimization problem; (3) an Android application that performs real-time object detection for AR applications, with experimental results that demonstrate the superiority of our approach.

Discovering statistical structure from links is a fundamental problem in the analysis of social networks. Choosing a misspecified model, or equivalently, an incorrect inference algorithm will result in an invalid analysis or even falsely uncover patterns that are in fact artifacts of the model. This work focuses on unifying two of the most widely used link-formation models: the stochastic block model (SBM) and the small world (or latent space) model (SWM). Integrating techniques from kernel learning, spectral graph theory, and nonlinear dimensionality reduction, we develop the first statistically sound polynomial-time algorithm to discover latent patterns in sparse graphs for both models. When the network comes from an SBM, the algorithm outputs a block structure. When it is from an SWM, the algorithm outputs estimates of each node's latent position.

We study a fundamental question that arises in social recommender systems: whether it is possible to simultaneously maximize: 1) an individual's benefit from using a social network, and 2) the efficiency of the network in disseminating information. To tackle this question, our study consists of three components. First, we introduce a stylized stochastic model for recommendation diffusion. Such a model allows us to highlight the connection between user experience at the individual level, and network efficiency at the macroscopic level. We also propose a set of metrics for quantifying both user experience and network efficiency. Second, based on these metrics, we extensively study the tradeoff between the two factors in a Yelp dataset, concluding that Yelp's social network is surprisingly efficient, though not optimal. Finally, we design a friend recommendation and news feed curation algorithm that can simultaneously address individuals' need to connect to high-quality friends, and service providers' need to maximize network efficiency in information propagation.

We study a fundamental question that arises in social recommender systems: whether it is possible to simultaneously maximize (a) an individual's benefit from using a social network and (b) the efficiency of the network in disseminating information. To tackle this question, our study consists of three components. First, we introduce a stylized stochastic model for recommendation diffusion. Such a model allows us to highlight the connection between user experience at the individual level, and network efficiency at the macroscopic level. We also propose a set of metrics for quantifying both user experience and network efficiency. Second, based on these metrics, we extensively study the tradeoff between the two factors in a Yelp dataset, concluding that Yelp's social network is surprisingly efficient, though not optimal. Finally, we design a friend recommendation and news feed curation algorithm that can simultaneously address individuals' need to connect to high quality friends, and service providers' need to maximize network efficiency in information propagation.

Recent increases in network traffic have led to severe congestion in broadband networks. We propose to mitigate this problem with a two-level edge-based solution that incentivizes users to moderate their bandwidth usage based on their actual needs. In the first level, home gateways are given QoE (quality of experience) credits that they can spend to receive more bandwidth at congested times; to ensure fairness, the credits are redistributed to other gateways after they are spent. We show that this scheme guarantees long-term fairness and maximizes users' total satisfaction at the equilibrium. In the second level, each gateway allocates bandwidth among its users and apps according to its own priorities. Gateways can thus customize their bandwidth allocation depending on individual preferences. We develop a prototype of this second-level allocation on commodity wireless routers. We then consider an example scenario and show by simulation and implementation results that our solution outperforms an equal bandwidth allocation, increasing users' overall utility and fairly allocating bandwidth across users.

We revisit the problem of predicting directional movements of stock prices based on news articles: here our algorithm uses daily articles from The Wall Street Journal to predict the closing stock prices on the same day. We propose a unified latent space model to characterize the "co-movements" between stock prices and news articles. Unlike many existing approaches, our new model is able to simultaneously leverage the correlations: (a) among stock prices, (b) among news articles, and (c) between stock prices and news articles. Thus, our model is able to make daily predictions on more than 500 stocks (most of which are not even mentioned in any news article) while having low complexity. We carry out extensive back testing on trading strategies based on our algorithm. The result shows that our model has substantially better accuracy rate (55.7%) compared to many widely used algorithms. The return (56%) and Sharpe ratio due to a trading strategy based on our model are also much higher than baseline indices.

We consider the problem of reconstructing a specific connected community S ⊂V in a graph G = (V, E), where each node v is associated with a signal whose strength grows with the likelihood that v belongs to S. This problem appears in social or protein interaction network, the latter also referred to as the signaling pathway reconstruction problem. We study this community reconstruction problem under several natural generative models, and make the following two contributions. First, in the context of social networks, where the signals are modeled as bounded-supported random variables, we design an efficient algorithm for recovering most members in S with well-controlled false positive overhead, by utilizing the network structure for a large family of “homogeneous” generative models. This positive result is complemented by an information theoretic lower bound for the case where the network structure is unknown or the network is heterogeneous. Second, we consider the case in which the graph represents the protein interaction network, in which it is customary to consider signals that have unbounded support, we generalize our first contribution to give the first theoretical justification of why existing Steiner-tree type heuristics work well in practice.

Software Defined Networks (SDNs) support diverse network policies by offering direct, network-wide control over how switches handle traffic. Unfortunately, many controller platforms force applications to grapple simultaneously with end-to-end connectivity constraints, routing policy, switch memory limits, and the hop-by-hop interactions between forwarding rules. We believe solutions to this complex problem should be factored in to three distinct parts: (1) high-level SDN applications should define their end-point connectivity policy on top of a "one big switch" abstraction; (2) a mid-level SDN infrastructure layer should decide on the hop-by-hop routing policy; and (3) a compiler should synthesize an effective set of forwarding rules that obey the user-defined policies and adhere to the resource constraints of the underlying hardware. In this paper, we define and implement our proposed architecture, present efficient rule-placement algorithms that distribute forwarding policies across general SDN networks while managing rule-space constraints, and show how to support dynamic, incremental update of policies. We evaluate the effectiveness of our algorithms analytically by providing complexity bounds on their running time and rule space, as well as empirically, using both synthetic benchmarks, and real-world firewall and routing policies.

There has been significant interest in the networking community on the impact of cascade effects on the diffusion of networking technology upgrades in the Internet. Thinking of the global Internet as a graph, where each node represents an economically-motivated Internet Service Provider (ISP), a key problem is to determine the smallest set of nodes that can trigger a cascade that causes every other node in the graph to adopt the protocol. We design the first approximation algorithm with a provable performance guarantee for this problem, in a model that captures the following key issue: a node's decision to upgrade should be influenced by the decisions of the remote nodes it wishes to communicate with. Given an internetwork G(V, E) and threshold function θ, we assume that node u activates (upgrades to the new technology) when it is adjacent to a connected component of active nodes in G of size exceeding node u's threshold θ(u). Our objective is to choose the smallest set of nodes that can cause the rest of the graph to activate. Our main contribution is an approximation algorithm based on linear programming, which we complement with computational hardness results and a near-optimum integrality gap Our algorithm, which does not rely on submodular optimization techniques, also highlights the substantial algorithmic difference between our problem and similar questions studied in the context of social networks.

We consider the online distributed non-stochastic experts problem, where the distributed system consists of one coordinator node that is connected to k sites, and the sites are required to communicate with each other via the coordinator. At each time-step t, one of the k site nodes has to pick an expert from the set {1, . . . , n}, and the same site receives information about payoffs of all experts for that round. The goal of the distributed system is to minimize regret at time horizon T, while simultaneously keeping communication to a minimum. The two extreme solutions to this problem are: (i) Full communication: This essentially simulates the non-distributed setting to obtain the optimal O(\sqrt{log(n)T}) regret bound at the cost of T communication. (ii) No communication: Each site runs an independent copy – the regret is O(\sqrt{log(n)kT}) and the communication is 0. This paper shows the difficulty of simultaneously achieving regret asymptotically better than \sqrt{kT} and communication better than T. We give a novel algorithm that for an oblivious adversary achieves a non-trivial trade-off: regret O(\sqrt{k^{5(1+\epsilon)/6} T}) and communication O(T/k^\epsilon), for any value of \epsilon in (0, 1/5). We also consider a variant of the model, where the coordinator picks the expert. In this model, we show that the label-efficient forecaster of Cesa-Bianchi et al. (2005) already gives us strategy that is near optimal in regret vs communication trade-off.

We consider the continual count tracking problem in a distributed environment where the input is an aggregate stream that originates from k distinct sites and the updates are allowed to be non-monotonic, i.e. both increments and decrements are allowed. The goal is to continually track the count within a prescribed relative accuracy ε at the lowest possible communication cost. Specifically, we consider an adversarial setting where the input values are selected and assigned to sites by an adversary but the order is according to a random permutation or is a random i.i.d process. The input stream of values is allowed to be non-monotonic with an unknown drift -1≤μ=1 where the case μ = 1 corresponds to the special case of a monotonic stream of only non-negative updates. We show that a randomized algorithm guarantees to track the count accurately with high probability and has the expected communication cost Õ(min√k/(|#956;|ε), √k n/ε, n}), for an input stream of length n, and establish matching lower bounds. This improves upon previously best known algorithm whose expected communication cost is Θ(min√k/ε,n]) that applies only to an important but more restrictive class of monotonic input streams, and our results are substantially more positive than the communication complexity of Ω(n) under fully adversarial input. We also show how our framework can also accommodate other types of random input streams, including fractional Brownian motion that has been widely used to model temporal long-range dependencies observed in many natural phenomena. Last but not least, we show how our non-monotonic counter can be applied to track the second frequency moment and to a Bayesian linear regression problem.

We study a natural information dissemination problem for multiple mobile agents in a bounded Euclidean space.Agents are placed uniformly at random in the d-dimensional space {-n, ..., n} d at time zero, and one of the agents holds a piece of information to be disseminated.All the agents then perform independent random walks over the space, and the information is transmitted from one agent to another if the two agents are sufficiently close.We wish to bound the total time before all agents receive the information (with high probability).Our work extends Pettarin et al's work [10], which solved the problem for d ≤ 2. We present tight bounds up to polylogarithmic factors for the case d = 3. (While our results extend to higher dimensions, for space and readability considerations we provide only the case d = 3 here.)Our results show the behavior when d ≥ 3 is qualitatively different from the case d ≤ 2. In particular, as the ratio between the volume of the space and the number of agents varies, we show an interesting phase transition for three dimensions that does not occur in one or two dimensions.

We prove the first Chernoff-Hoeffding bounds for general nonreversible finite-state Markov chains based on the standard L_1 (variation distance) mixing-time of the chain. Specifically, consider an ergodic Markov chain M and a weight function f: [n] -> [0,1] on the state space [n] of M with mean mu = E_{v = delta mu t ], is at most exp(-Omega(delta^2 mu t / T)) for 0 <= delta <= 1, and exp(-Omega(delta mu t / T)) for delta > 1. In fact, the bounds hold even if the weight functions f_i's for i in [t] are distinct, provided that all of them have the same mean mu. We also obtain a simplified proof for the Chernoff-Hoeffding bounds based on the spectral expansion lambda of M, which is the square root of the second largest eigenvalue (in absolute value) of M tilde{M}, where tilde{M} is the time-reversal Markov chain of M. We show that the probability Pr [ |X - mu t| >= delta mu t ] is at most exp(-Omega(delta^2 (1-lambda) mu t)) for 0 <= delta <= 1, and exp(-Omega(delta (1-lambda) mu t)) for delta > 1. Both of our results extend to continuous-time Markov chains, and to the case where the walk starts from an arbitrary distribution x, at a price of a multiplicative factor depending on the distribution x in the concentration bounds.

Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. In this paper, we propose an extension to the independent cascade model that incorporates the emergence and propagation of negative opinions. The new model has an explicit parameter called quality factor to model the natural behavior of people turning negative to a product due to product defects. Our model incorporates negativity bias (negative opinions usually dominate over positive opinions) commonly acknowledged in the social psychology literature. The model maintains some nice properties such as submodularity, which allows a greedy approximation algorithm for maximizing positive influence within a ratio of 1 – 1/e. We define a quality sensitivity ratio (qs-ratio) of influence graphs and show a tight bound of on the qs-ratio, where n is the number of nodes in the network and k is the number of seeds selected, which indicates that seed selection is sensitive to the quality factor for general graphs. We design an efficient algorithm to compute influence in tree structures, which is nontrivial due to the negativity bias in the model. We use this algorithm as the core to build a heuristic algorithm for influence maximization for general graphs. Through simulations, we show that our heuristic algorithm has matching influence with a standard greedy approximation algorithm while being orders of magnitude faster.

In their seminal work, Alon, Matias, and Szegedy introduced several sketching techniques, including showing that $4$-wise independence is sufficient to obtain good approximations of the second frequency moment. In this work, we show that their sketching technique can be extended to product domains $[n]^k$ by using the product of $4$-wise independent functions on $[n]$. Our work extends that of Indyk and McGregor, who showed the result for $k = 2$. Their primary motivation was the problem of identifying correlations in data streams. In their model, a stream of pairs $(i,j) \in [n]^2$ arrive, giving a joint distribution $(X,Y)$, and they find approximation algorithms for how close the joint distribution is to the product of the marginal distributions under various metrics, which naturally corresponds to how close $X$ and $Y$ are to being independent. By using our technique, we obtain a new result for the problem of approximating the $\ell_2$ distance between the joint distribution and the product of the marginal distributions for $k$-ary vectors, instead of just pairs, in a single pass. Our analysis gives a randomized algorithm that is a $(1\pm \epsilon)$ approximation (with probability $1-\delta$) that requires space logarithmic in $n$ and $m$ and proportional to $3^k$.

Floating codes are codes designed to store multiple values in a Write Asymmetric Memory, with applications to flash memory. In this model, a memory consists of a block ofncells, with each cell in one of q states {0,1,...,q -1}. The cells are used to represent k variable values from an ¿-ary alphabet. Cells can move from lower values to higher values easily, but moving any cell from a higher value to a lower value requires first resetting the entire block to an all 0 state. Reset operations are to be avoided; generally a block can only experience a large but finite number of resets before wearing out entirely. A code here corresponds to a mapping from cell states to variable values, and a transition function that gives how to rewrite cell states when a variable is changed. Previous work has focused on developing codes that maximize the worst-case number of variable changes, or equivalently cell rewrites, that can be experienced before resetting. In this paper, we introduce the problem of maximizing theexpectednumber of variable changes before resetting, given an underlying Markov chain that models variable changes. We demonstrate that codes designed for expected performance can differ substantially from optimal worst-case codes, and suggest constructions for some simple cases. We then study the related question of the performance of random codes, again focusing on the issue of expected behavior.

We consider deletion channels and insertion channels under an additional segmentation assumption: the input consists of disjoint segments ofbconsecutive bits, with at most one error per segment. Under this assumption, we demonstrate simple and computationally efficient deterministic encoding and decoding schemes that achieve a high provable rate even under worst case errors. We also consider more complex schemes that experimentally achieve higher rates under random error.

We consider deletion channels and insertion channels under an additional segmentation assumption: the input consists of disjoint segments of b consecutive bits, with at most one error per segment. Under this assumption, we demonstrate simple and computationally efficient deterministic encoding and decoding schemes that achieve a high provable rate even under worst-case errors. We also consider more complex schemes that experimentally achieve higher rates under random error.