Settling the Sample Complexity of Single-parameter Revenue Maximization

This paper settles the sample complexity of single-parameter revenue maximization by showing matching upper and lower bounds, up to a poly-logarithmic factor, for all families of value distributions that have been considered in the literature. The upper bounds are unified under a novel framework, which builds on the strong revenue monotonicity by Devanur, Huang, and Psomas (STOC 2016), and an information theoretic argument. This is fundamentally different from the previous approaches that rely on either constructing an $\epsilon$-net of the mechanism space, explicitly or implicitly via statistical learning theory, or learning an approximately accurate version of the virtual values. To our knowledge, it is the first time information theoretical arguments are used to show sample complexity upper bounds, instead of lower bounds. Our lower bounds are also unified under a meta construction of hard instances.Comment: 49 pages, Accepted by STOC1

25 Years Ago: The First Asynchronous Microprocessor

Twenty-five years ago, in December 1988, my research group at Caltech submitted the world’s first asynchronous (“clockless”) microprocessor design for fabrication to MOSIS. We received the chips in early 1989; testing started in February 1989. The chips were found fully functional on first silicon. The results were presented at the Decennial Caltech VLSI Conference in March of the same year. The first entirely asynchronous microprocessor had been designed and successfully fabricated. As the technology finally reaches industry, and with the benefit of a quarter-century hindsight, here is a recollection of this landmark project

Efficient Variational Inference for Hierarchical Models of Images, Text, and Networks

Variational inference provides a general optimization framework to approximate the posterior distributions of latent variables in probabilistic models. Although effective in simple scenarios, variational inference may be inaccurate or infeasible when the data is high-dimensional, the model structure is complicated, or variable relationships are non-conjugate. We propose solutions to these problems through the smart design and leverage of model structures, the rigorous derivation of variational bounds, and the creation of flexible algorithms for various models with rich, non-conjugate dependencies.Concretely, we first design an interpretable generative model for natural images, in which the hundreds of thousands of pixels per image are split into small patches represented by Gaussian mixture models. Through structured variational inference, the evidence lower bound of this model automatically recovers the popular expected patch log-likelihood method for image processing. A nonparametric extension using hierarchical Dirichlet processes further enables self-similarities to be captured and image-specific clusters created during inference, boosting image denoising and inpainting accuracy.Then we move on to text data, and design hierarchical topic graphs that generalize the bipartite noisy-OR models previously used for medical diagnosis. We derive auxiliary bounds to overcome the non-conjugacy of noisy-OR conditionals, and use stochastic variational inference to efficiently train on datasets with hundreds of thousands of documents. We dramatically increase the algorithm speed through a constrained family of variational bounds, so that only the ancestors of the sparse observed tokens of each document need to be considered.Finally, we propose a general-purpose Monte Carlo variational inference strategy that is directly applicable to any model with discrete variables. Compared to REINFORCE-style stochastic gradient updates, our coordinate-ascent updates have lower variance and converge much faster. Compared to auxiliary-variable bounds crafted for each individual model, our algorithm is simpler to derive and may be easily integrated into probabilistic programming languages for broader use. By avoiding auxiliary variables, we also tighten likelihood bounds and increase robustness to local optima. Extensive experiments on real-world models of images, text, and networks illustrate these appealing advantages

Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar

This paper presents a self-supervised framework for learning to detect robust keypoints for odometry estimation and metric localisation in radar. By embedding a differentiable point-based motion estimator inside our architecture, we learn keypoint locations, scores and descriptors from localisation error alone. This approach avoids imposing any assumption on what makes a robust keypoint and crucially allows them to be optimised for our application. Furthermore the architecture is sensor agnostic and can be applied to most modalities. We run experiments on 280km of real world driving from the Oxford Radar RobotCar Dataset and improve on the state-of-the-art in point-based radar odometry, reducing errors by up to 45% whilst running an order of magnitude faster, simultaneously solving metric loop closures. Combining these outputs, we provide a framework capable of full mapping and localisation with radar in urban environments.Comment: Video summary: https://youtu.be/L-PO7nxWpJ

Mississippi Statewide Accountability System: A Measure Of Academic Attainment Or Other Factors

This quantitative study analyzed the construct validity of the Mississippi Statewide Accountability System through an analysis of the relationship between teacher, financial, socio-economic, and social characteristics and the Quality of distribution index of public school districts in Mississippi. This study sought to determine if there were constructs outside the control of schools and districts that significantly correlated to outcomes of the Mississippi Statewide Accountability System that were not accounted for in the calculations. Educational leaders, communities, and other educational stakeholders have paid close attention to the Mississippi Statewide Accountability System as legislators have chosen to use it to rank schools and districts from A-F. The major component of the Mississippi Statewide Accountability System is the Quality of Distribution Index which is based on student test scores. This research used the Quality of Distribution Index results from 148 public school districts from SY 2011-2012 as the dependent variable. Data was collected from reputable sources from SY 2011-2012 for twelve independent variables, not in control of school personnel that were a part of all school districts. Correlations were determined using a Pearson Product Moment Correlation Coefficient and a Coefficient of Determination at the .01 level (two tailed) of significance. The research findings indicated a significant correlation between Quality of distribution index and eleven of the twelve constructs and thus: The Mississippi Statewide Accountability System has issues with construct validity

Diversity awareness training : a quasi-experimental evaluation of changes in trainees' attitudes, knowledge and skills

Includes bibliographical references (leaves 73-79).A quasi-experimental evaluation was undertaken to measure the extent to which a I-day diversity awareness training programme resulted in changes in trainees' attitudes, knowledge and skills. A pre, post and post-post test design was employed. Changes in attitudes, knowledge and skills were measured using the Quick Discrimination Index (Ponterotto, et aI., 1995) as well as a Diversity Questionnaire developed by the researcher. Results show that immediately after the training intervention, increased levels of knowledge and skill were measured. However, three months after the training, no significant changes in trainees' attitudes and levels of knowledge and skill were found, leading to the conclusion that the training had no lasting effect. Amongst other things these results offered support for the proposition that factors in the work environment critically contribute to the sustainability of anticipated outcomes of diversity training programmes

Bayesian Nonparametric Relational Topic Model through Dependent Gamma Processes

© 2016 IEEE. Traditional relational topic models provide a successful way to discover the hidden topics from a document network. Many theoretical and practical tasks, such as dimensional reduction, document clustering, and link prediction, could benefit from this revealed knowledge. However, existing relational topic models are based on an assumption that the number of hidden topics is known a priori, which is impractical in many real-world applications. Therefore, in order to relax this assumption, we propose a nonparametric relational topic model using stochastic processes instead of fixed-dimensional probability distributions in this paper. Specifically, each document is assigned a Gamma process, which represents the topic interest of this document. Although this method provides an elegant solution, it brings additional challenges when mathematically modeling the inherent network structure of typical document network, i.e., two spatially closer documents tend to have more similar topics. Furthermore, we require that the topics are shared by all the documents. In order to resolve these challenges, we use a subsampling strategy to assign each document a different Gamma process from the global Gamma process, and the subsampling probabilities of documents are assigned with a Markov Random Field constraint that inherits the document network structure. Through the designed posterior inference algorithm, we can discover the hidden topics and its number simultaneously. Experimental results on both synthetic and real-world network datasets demonstrate the capabilities of learning the hidden topics and, more importantly, the number of topics