Imperfect-Information Extensive-Form Games (IIEFGs) is a prevalent model for real-world games involving imperfect information and sequential plays. The Extensive-Form Correlated Equilibrium (EFCE) has been proposed as a natural solution concept for multi-player general-sum IIEFGs. However, existing algorithms for finding an EFCE require full feedback from the game, and it remains open how to efficiently learn the EFCE in the more challenging bandit feedback setting where the game can only be learned by observations from repeated playing. This paper presents the first sample-efficient algorithm for learning the EFCE from bandit feedback. We begin by proposing K-EFCE--a generalized definition that allows players to observe and deviate from the recommended actions for K times. The K-EFCE includes the EFCE as a special case at K = 1, and is an increasingly stricter notion of equilibrium as K increases.

Gradient inversion attack enables recovery of training samples from model gradients in federated learning (FL), and constitutes a serious threat to data privacy. To mitigate this vulnerability, prior work proposed both principled defenses based on differential privacy, as well as heuristic defenses based on gradient compression as countermeasures. These defenses have so far been very effective, in particular those based on gradient compression that allow the model to maintain high accuracy while greatly reducing the effectiveness of attacks. In this work, we argue that such findings underestimate the privacy risk in FL. As a counterexample, we show that existing defenses can be broken by a simple adaptive attack, where a model trained on auxiliary data is able to invert gradients on both vision and language tasks.

Shinji Watanabe, an associate professor in Carnegie Mellon University's School of Computer Science, has been named a fellow of the International Speech Communication Association (ISCA) "for wide-ranging, fundamental contributions to research and leadership in speech recognition technologies." Founded in 2007, the ISCA Fellows Program recognizes and honors outstanding ISCA members who have made significant contributions to the science and technology of speech communication. Fellows are nominated by association members and selected by a committee of their peers. Since its inception, the program has recognized nearly 100 fellows from countries around the globe. Watanabe, who is part of CMU's Language Technologies Institute, studies automatic speech recognition, speech enhancement, spoken language understanding, and machine learning for speech and language processing.

Abstract--Online detection of anomalies in time series is a key technique in various event-sensitive scenarios such a s robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources and demands are making this task more challenging than ever . First, the rapid increase of unlabeled data makes supervise d learning no longer suitable in many cases. Second, a great po rtion of time series have complex seasonality features. Third, on -line anomaly detection needs to be fast and reliable. In view of this, we in this paper adopt an unsupervised prediction-dri ven approach on the basis of a backbone model combining a series decomposition part and an inference part. We then propose a novel metric, Local Trend Inconsistency (L TI), along with a detection algorithm that efficiently computes L TI chronolo gically along the series and marks each data point with a score indica ting its probability of being anomalous. The result shows that our scheme outperforms several representative anomaly detection alg orithms in Area Under Curve (AUC) metric with decent time efficiency. While time series data has been ubiquitous before the coming of big data era, a large number of recently emerging technical scenarios like autonomous driving, edge computi ng and Internet of Things (IoT) pose new challenges to the detection of anomalies in this type of data. In the meantime, detection techniques that can provide early, reliable repo rts of anomaly has become crucial for a wide range of systems requiring 24/7 monitoring services. In cloud data centers, for example, a distributed monitoring system usually collects a variety of log data from virtual machine level to cluster lev el on a regular basis and sends them to a central detection module, which needs to analyze the aggregated time series to detect any anomalous events including hardware breakdown, unavailable services and cyber attacks. This requires an on - line detector capable of making reliable detections (i.e., with strong sensitivity and specificity), otherwise it could bri ng about unnecessary cost of maintenance.

Though distribution system operators have been adding more sensors to their networks, they still often lack an accurate real-time picture of the behavior of distributed energy resources such as demand responsive electric loads and residential solar generation. Such information could improve system reliability, economic efficiency, and environmental impact. Rather than installing additional, costly sensing and communication infrastructure to obtain additional real-time information, it may be possible to use existing sensing capabilities and leverage knowledge about the system to reduce the need for new infrastructure. In this paper, we disaggregate a distribution feeder's demand measurements into: 1) the demand of a population of air conditioners, and 2) the demand of the remaining loads connected to the feeder. We use an online learning algorithm, Dynamic Fixed Share (DFS), that uses the real-time distribution feeder measurements as well as models generated from historical building- and device-level data. We develop two implementations of the algorithm and conduct case studies using real demand data from households and commercial buildings to investigate the effectiveness of the algorithm. The case studies demonstrate that DFS can effectively perform online disaggregation and the choice and construction of models included in the algorithm affects its accuracy, which is comparable to that of a set of Kalman filters.

An overview of current debates and contemporary research devoted to the modeling of decision making processes and their facilitation directs attention to the Analytic Hierarchy Process (AHP). At the core of the AHP are various prioritization procedures (PPs) and consistency measures (CMs) for a Pairwise Comparison Matrix (PCM) which, in a sense, reflects preferences of decision makers. Certainly, when judgments about these preferences are perfectly consistent (cardinally transitive), all PPs coincide and the quality of the priority ratios (PRs) estimation is exemplary. However, human judgments are very rarely consistent, thus the quality of PRs estimation may significantly vary. The scale of these variations depends on the applied PP and utilized CM for a PCM. This is why it is important to find out which PPs and which CMs for a PCM lead directly to an improvement of the PRs estimation accuracy. The main goal of this research is realized through the properly designed, coded and executed seminal and sophisticated simulation algorithms in Wolfram Mathematica 8.0. These research results convince that the embedded in the AHP and commonly applied, both genuine PP and CM for PCM may significantly deteriorate the quality of PRs estimation; however, solutions proposed in this paper can significantly improve the methodology.