Warren B. Powell is Professor Emeritus at Princeton University, where he taught for 39 years, and is currently Chief Analytics Officer at Optimal Dynamics. He is the founder and director of CASTLE Labs, which developed models and algorithms in stochastic optimization, with applications to energy systems, transportation, health, e-commerce, and the laboratory sciences (see www.castlelab.princeton.edu). He has pioneered the use of approximate dynamic programming for high-dimensional applications, and the knowledge gradient for active learning problems. His recent work has focused on developing a unified framework for sequential decision problems under uncertainty, spanning active learning to a wide range of dynamic resource allocation problems. He has authored books on Approximate Dynamic Programming and (with Ilya Ryzhov) Optimal Learning, and is the author of Reinforcement Learning and Stochastic Optimization: A unified framework for sequential decisions.

Control of nonlinear uncertain systems is a common challenge in the robotics field. Nonlinear latent force models, which incorporate latent uncertainty characterized as Gaussian processes, carry the promise of representing such systems effectively, and we focus on the control design for them in this work. To enable the design, we adopt the state-space representation of a Gaussian process to recast the nonlinear latent force model and thus build the ability to predict the future state and uncertainty concurrently. Using this feature, a stochastic model predictive control problem is formulated. To derive a computational algorithm for the problem, we use the scenario-based approach to formulate a deterministic approximation of the stochastic optimization. We evaluate the resultant scenario-based model predictive control approach through a simulation study based on motion planning of an autonomous vehicle, which shows much effectiveness. The proposed approach can find prospective use in various other robotics applications.

The promise of self-supervised learning (SSL) is to leverage large amounts of unlabeled data to solve complex tasks. While there has been excellent progress with simple, image-level learning, recent methods have shown the advantage of including knowledge of image structure. However, by introducing hand-crafted image segmentations to define regions of interest, or specialized augmentation strategies, these methods sacrifice the simplicity and generality that makes SSL so powerful. Instead, we propose a self-supervised learning paradigm that discovers this image structure by itself. Our method, Odin, couples object discovery and representation networks to discover meaningful image segmentations without any supervision. The resulting learning paradigm is simpler, less brittle, and more general, and achieves state-of-the-art transfer learning results for object detection and instance segmentation on COCO, and semantic segmentation on PASCAL and Cityscapes, while strongly surpassing supervised pre-training for video segmentation on DAVIS.

Recently, Qiao, Duan, and Cheng~(2019) proposed a distributed nearest-neighbor classification method, in which a massive dataset is split into smaller groups, each processed with a $k$-nearest-neighbor classifier, and the final class label is predicted by a majority vote among these groupwise class labels. This paper shows that the distributed algorithm with $k=1$ over a sufficiently large number of groups attains a minimax optimal error rate up to a multiplicative logarithmic factor under some regularity conditions, for both regression and classification problems. Roughly speaking, distributed 1-nearest-neighbor rules with $M$ groups has a performance comparable to standard $\Theta(M)$-nearest-neighbor rules. In the analysis, alternative rules with a refined aggregation method are proposed and shown to attain exact minimax optimal rates.

Over-parameterized models can perfectly learn various types of data distributions, however, generalization error is usually lower for real data in comparison to artificial data. This suggests that the properties of data distributions have an impact on generalization capability. This work focuses on the search space defined by the input data and assumes that the correlation between labels of neighboring input values influences generalization. If correlation is low, the randomness of the input data space is high leading to high generalization error. We suggest to measure the randomness of an input data space using Maurer's universal. Results for synthetic classification tasks and common image classification benchmarks (MNIST, CIFAR10, and Microsoft's cats vs. dogs data set) find a high correlation between the randomness of input data spaces and the generalization error of deep neural networks for binary classification problems.

Data Envelopment Analysis (DEA) is a technique used to measure the efficiency of decision-making units (DMUs). In order to measure the efficiency of DMUs, the essential requirement is input-output data. Data is usually collected by humans, machines, or both. Due to human/machine errors, there are chances of having some missing values or inaccuracy, such as vagueness/uncertainty/hesitation in the collected data. In this situation, it will be difficult to measure the efficiencies of DMUs accurately. To overcome these shortcomings, a method is presented that can deal with missing values and inaccuracy in the data. To measure the performance efficiencies of DMUs, an input minimization BCC (IMBCC) model in a fully intuitionistic fuzzy (IF) environment is proposed. To validate the efficacy of the proposed fully intuitionistic fuzzy input minimization BCC (FIFIMBCC) model and the technique to deal with missing values in the data, a real-life application to measure the performance efficiencies of Indian police stations is presented.

To reduce carbon emissions, electrical power systems are Outside of the power systems community, novel machine increasingly incorporating renewable generation resources into learning techniques for partial differential equations (PDEs) the energy mix. These resources are often dependent on have been used to efficiently learn evolution equations for weather inputs and, as a result, they behave stochastically PDFs of system states. We refer to such equations as PDF in the short and long terms, posing planning and operational equations, and unlike the FPE [9], many are unclosed.

We study optimization for data-driven decision-making when we have observations of the uncertain parameters within the optimization model together with concurrent observations of covariates. Given a new covariate observation, the goal is to choose a decision that minimizes the expected cost conditioned on this observation. We investigate three data-driven frameworks that integrate a machine learning prediction model within a stochastic programming sample average approximation (SAA) for approximating the solution to this problem. Two of the SAA frameworks are new and use out-of-sample residuals of leave-one-out prediction models for scenario generation. The frameworks we investigate are flexible and accommodate parametric, nonparametric, and semiparametric regression techniques. We derive conditions on the data generation process, the prediction model, and the stochastic program under which solutions of these data-driven SAAs are consistent and asymptotically optimal, and also derive convergence rates and finite sample guarantees. Computational experiments validate our theoretical results, demonstrate the potential advantages of our data-driven formulations over existing approaches (even when the prediction model is misspecified), and illustrate the benefits of our new data-driven formulations in the limited data regime.

Plasma supports collective modes and particle-wave interactions that leads to complex behavior in inertial fusion energy applications. While plasma can sometimes be modeled as a charged fluid, a kinetic description is useful towards the study of nonlinear effects in the higher dimensional momentum-position phase-space that describes the full complexity of plasma dynamics. We create a differentiable solver for the plasma kinetics 3D partial-differential-equation and introduce a domain-specific objective function. Using this framework, we perform gradient-based optimization of neural networks that provide forcing function parameters to the differentiable solver given a set of initial conditions. We apply this to an inertial-fusion relevant configuration and find that the optimization process exploits a novel physical effect that has previously remained undiscovered.

Effective customer engagement is a requisite for modern financial service providers that are adopting advanced methods to increase the level of personalization of their services [1]. Although artificial intelligence (AI) has become a ubiquitous tool in financial technology [2], research in the field has yet to significantly advance levels of personalization [3]. Asset management is an active research topic in AI for finance; however, the research opportunities presented by the need for personalized services are usually neglected [4]. Whereas personalized investment advice is typically based on questionnaires, we propose the use of micro-segmentation based on spending behavior. Traditionally, customer segmentation has been grounded in demographics which provide only a coarse segmentation [5]; it fails to capture nuanced differences between individuals with the potential for undesirable ramifications, e.g.