We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities p(y|x, D), e.g., for applications involving bandits or active learning. One simple approach to this is to use online Monte Carlo methods, such as SGLD (stochastic gradient Langevin dynamics). Unfortunately, such a method needs to store many copies of the parameters (which wastes memory), and needs to make predictions using many versions of the model (which wastes time). We describe a method for "distilling" a Monte Carlo approximation to the posterior predictive density into a more compact form, namely a single deep neural network. We compare to two very recent approaches to Bayesian neural networks, namely an approach based on expectation propagation [HLA15] and an approach based on variational Bayes [BCKW15]. Our method performs better than both of these, is much simpler to implement, and uses less computation at test time.

We study the performance of standard online learning algorithms when the feedback is delayed by an adversary. We show that online-gradient-descent [1] and follow-the-perturbed-leader [2] achieve regret O( D) in the delayed setting, where D is the sum of delays of each round's feedback. This bound collapses to an optimal O( T) bound in the usual setting of no delays (where D = T). Our main contribution is to show that standard algorithms for online learning already have simple regret bounds in the most general setting of delayed feedback, making adjustments to the analysis and not to the algorithms themselves. Our results help affirm and clarify the success of recent algorithms in optimization and machine learning that operate in a delayed feedback model.

The goal of minimizing misclassification error on a training set is often just one of several real-world goals that might be defined on different datasets. For example, one may require a classifier to also make positive predictions at some specified rate for some subpopulation (fairness), or to achieve a specified empirical recall. Other real-world goals include reducing churn with respect to a previously deployed model, or stabilizing online training. In this paper we propose handling multiple goals on multiple datasets by training with dataset constraints, using the ramp penalty to accurately quantify costs, and present an efficient algorithm to approximately optimize the resulting non-convex constrained optimization problem. Experiments on both benchmark and real-world industry datasets demonstrate the effectiveness of our approach.

The mean and variance of the series are constant over time. The series has a constant trend over time. The auto-covariance function of the series is dependent on time. The series has a periodic pattern over time. A moving average uses past errors, while an autoregressive model uses past values of the dependent variable. A moving average uses only one past value, while an autoregressive model uses multiple past values.

Eightfold's experience offers insight into the potential and the challenge of applying A.I. to high-stakes decisions like hiring, promoting and charting career paths for workers. The company is at the forefront of using A.I. and data to assess a person's potential for success in a job. That assessment is based on measuring skills and experience rather than on university degrees or personal connections. The skills-based perspective has been embraced by labor market and policy experts as a vehicle for broadening opportunity in America, especially for the nearly two-thirds of workers who do not have four-year college degrees. Screening by degrees hits minority workers particularly hard, eliminating 72 percent of Black adults and 79 percent of Latino adults, compared with 58 percent of non-Hispanic white adults.

We operate with a privacy-first student-first approach and take due care when handling student data and information. Proctortrack has the technology to detect over 500 virtual machines and stealth apps alongside a wide range of pre-defined exam violations. Advanced LMS like D2L pairs up seamlessly with Proctortrack's Proctored Exam-in-Browser (PEBble) and can secure online exams for D2L Online Courses against cheating attempts using unauthorized chatbots and browser extensions; the PEBble browser is an efficient solution.,

Today I am happy to announce that a new tidymodels-centric version of my free, online, interactive course, Supervised Machine Learning: Case Studies in R, has been published! This is at least the third version of this course I've built at this point but I believe it to be the best, in terms of how it communicates machine learning concepts and how useful to your real-world problems the demonstrated code will be. Similar to the last time I launched this course, it provides four case studies using data from the real world for you to practice your predictive modeling skills. One question we sometimes field from R users is about choosing to use tidymodels vs. caret. The original version of my course mostly used caret, and caret is a stable and broadly used framework for modeling and machine learning in R.

One of the most, if not the most, well-known models in Artificial intelligence (AI) is the "YOLO" model series. When I was in school, YOLO used to mean something else. And yet, here I am 15 years later writing an article about it -- who would have thought? YOLO (You Only Look Once) is a popular set of object detection models used for real-time object detection and classification in computer vision. Originally developed by Joseph Redmon, Ali Farhadi, and Santosh Divvala, YOLO aims to achieve high accuracy in object detection with real-time speed.

We study how a principal can efficiently and effectively intervene on the rewards of a previously unseen learning agent in order to induce desirable outcomes. This is relevant to many real-world settings like auctions or taxation, where the principal may not know the learning behavior nor the rewards of real people. Moreover, the principal should be few-shot adaptable and minimize the number of interventions, because interventions are often costly. We introduce MERMAIDE, a model-based meta-learning framework to train a principal that can quickly adapt to out-of-distribution agents with different learning strategies and reward functions. We validate this approach step-by-step. First, in a Stackelberg setting with a best-response agent, we show that meta-learning enables quick convergence to the theoretically known Stackelberg equilibrium at test time, although noisy observations severely increase the sample complexity. We then show that our model-based meta-learning approach is cost-effective in intervening on bandit agents with unseen explore-exploit strategies. Finally, we outperform baselines that use either meta-learning or agent behavior modeling, in both $0$-shot and $K=1$-shot settings with partial agent information.

The widespread usage of computer-based assessments and individualized learning platforms has resulted in an increased demand for the rapid production of high-quality items. Automated item generation (AIG), the process of using item models to generate new items with the help of computer technology, was proposed to reduce reliance on human subject experts at each step of the process. AIG has been used in test development for some time. Still, the use of machine learning algorithms has introduced the potential to improve the efficiency and effectiveness of the process greatly. The approach presented in this paper utilizes OpenAI's latest transformer-based language model, GPT-3, to generate reading passages. Existing reading passages were used in carefully engineered prompts to ensure the AI-generated text has similar content and structure to a fourth-grade reading passage. For each prompt, we generated multiple passages, the final passage was selected according to the Lexile score agreement with the original passage. In the final round, the selected passage went through a simple revision by a human editor to ensure the text was free of any grammatical and factual errors. All AI-generated passages, along with original passages were evaluated by human judges according to their coherence, appropriateness to fourth graders, and readability.