In a case proposed by the research team, a device like a flat smartphone could be folded and reconfigured into the shape of a game controller. Or, in a less practical example, you could simply roll your phone out into a rectangular log with a postage-stamp sized display on one end. For users who were never very good at spatial reasoning or origami, an algorithm will help determine the best way to twist and fold the screen into the desired shape. While the device is still in the awkward prototype phase at this point, the research team will present it to a panel at the International Conference on Robotics and Automation in Stockholm later this week.

Dictionary learning has recently received significant attention due to the increased importance of finding sparse representations of signals/data. In dictionary learning, the goal is to construct an overcomplete basis using input signals such that each signal can be described by a small number of atoms (columns) [1]. Although the existing literature has focused on one-dimensional data, many signals in practice are multidimensional and have a tensor structure: examples include 2-dimensional images and 3-dimensional signals produced via magnetic resonance imaging or computed tomography systems. In traditional dictionary learning techniques, multidimensional data are processed after vectorizing of signals. This can result in poor sparse representations as the structure of the data is neglected [2]. In this paper we provide fundamental limits on learning dictionaries for multidimensional data with tensor structure: we call such dictionaries Kronecker-structured (KS). Several algorithms have been proposed to learn KS dictionaries [2]-[7] but there has been little work on the theoretical guarantees of such algorithms. The lower bounds we provide on the minimax risk of learning a KS dictionary give a measure to evaluate the performance of the existing algorithms.

The big news on March 12 of this year was of the Go-playing AI-system AlphaGo securing victory against 18-time world champion Lee Se-dol by winning the third straight game of a five-game match in Seoul, Korea. After Deep Blue's victory against chess world champion Gary Kasparov in 1997, the game of Go was the next grand challenge for game-playing artificial intelligence. Go has defied the brute-force methods in game-tree search that worked so successfully in chess. In 2012, Communications published a Research Highlight article by Sylvain Gelly et al. on computer Go, which reported that "Programs based on Monte-Carlo tree search now play at human-master levels and are beginning to challenge top professional players." AlphaGo combines tree-search techniques with search-space reduction techniques that use deep learning.

In this paper, we proposed a new approximate heuristic search algorithm: Cascading A*, which is a two-phrase algorithm combining A* and IDA* by a new concept "envelope ball". The new algorithm CA* is efficient, able to generate approximate solution and any-time solution, and parallel friendly.

The big news on March 12 of this year was of the Go-playing AI-system AlphaGo securing victory against 18-time world champion Lee Se-dol by winning the third straight game of a five-game match in Seoul, Korea. After Deep Blue's victory against chess world champion Gary Kasparov in 1997, the game of Go was the next grand challenge for game-playing artificial intelligence. Go has defied the brute-force methods in game-tree search that worked so successfully in chess. In 2012, Communications published a Research Highlight article by Sylvain Gelly et al. on computer Go, which reported that "Programs based on Monte-Carlo tree search now play at human-master levels and are beginning to challenge top professional players." AlphaGo combines tree-search techniques with search-space reduction techniques that use deep learning. Its victory is a stunning achievement and another milestone in the inexorable march of AI research.

Face recall is a basic human cognitive process performed routinely, e.g., when meeting someone and determining if we have met that person before. Assisting a subject during face recall by suggesting candidate faces can be challenging. One of the reasons is that the search space - the face space - is quite large and lacks structure. A commercial application of face recall is facial composite systems - such as Identikit, PhotoFIT, and CD-FIT - where a witness searches for an image of a face that resembles his memory of a particular offender. The inherent uncertainty and cost in the evaluation of the objective function, the large size and lack of structure of the search space, and the unavailability of the gradient concept makes this problem inappropriate for traditional optimization methods. In this paper we propose a novel evolutionary approach for searching the face space that can be used as a facial composite system. The approach is inspired by methods of Bayesian optimization and differs from other applications in the use of the skew-normal distribution as its acquisition function. This choice of acquisition function provides greater granularity, with regularized, conservative, and realistic results.

The big news on March 12 of this year was of the Go-playing AI-system AlphaGo securing victory against 18-time world champion Lee Se-dol by winning the third straight game of a five-game match in Seoul, Korea. After Deep Blue's victory against chess world champion Gary Kasparov in 1997, the game of Go was the next grand challenge for game-playing artificial intelligence. Go has defied the brute-force methods in game-tree search that worked so successfully in chess. In 2012, Communications published a Research Highlight article by Sylvain Gelly et al. on computer Go, which reported that "Programs based on Monte-Carlo tree search now play at human-master levels and are beginning to challenge top professional players." AlphaGo combines tree-search techniques with search-space reduction techniques that use deep learning. Its victory is a stunning achievement and another milestone in the inexorable march of AI research.

This paper presents a novel nonmyopic adaptive Gaussian process planning (GPP) framework endowed with a general class of Lipschitz continuous reward functions that can unify some active learning/sensing and Bayesian optimization criteria and offer practitioners some flexibility to specify their desired choices for defining new tasks/problems. In particular, it utilizes a principled Bayesian sequential decision problem framework for jointly and naturally optimizing the exploration-exploitation trade-off. In general, the resulting induced GPP policy cannot be derived exactly due to an uncountable set of candidate observations. A key contribution of our work here thus lies in exploiting the Lipschitz continuity of the reward functions to solve for a nonmyopic adaptive epsilon-optimal GPP (epsilon-GPP) policy. To plan in real time, we further propose an asymptotically optimal, branch-and-bound anytime variant of epsilon-GPP with performance guarantee. We empirically demonstrate the effectiveness of our epsilon-GPP policy and its anytime variant in Bayesian optimization and an energy harvesting task.

Algorithms based on first-order knowledge compilation are currently the state-of-the-art for lifted inference. These algorithms typically compile a probabilistic relational model into an intermediate data structure and use it to answer many inference queries. In this paper, we propose compiling a probabilistic relational model directly into a low-level target (e.g., C or C++) program instead of an intermediate data structure and taking advantage of advances in program compilation. Our experiments represent orders of magnitude speedup compared to existing approaches.

Kidney exchange is a type of barter market where patients exchange willing but incompatible donors. These exchanges are conducted via cycles---where each incompatible patient-donor pair in the cycle both gives and receives a kidney---and chains, which are started by an altruist donor who does not need a kidney in return. Finding the best combination of cycles and chains is hard. The leading algorithms for this optimization problem use either branch and price — a combination of branch and bound and column generation — or constraint generation. We show a correctness error in the leading prior branch-and-price-based approach [Glorie et al. 2014]. We develop a provably correct fix to it, which also necessarily changes the algorithm's complexity, as well as other improvements to the search algorithm. Next, we compare our solver to the leading constraint-generation-based solver and to the best prior correct branch-and-price-based solver. We focus on the setting where chains have a length cap. A cap is desirable in practice since if even one edge in the chain fails, the rest of the chain fails: the cap precludes very long chains that are extremely unlikely to execute and instead causes the solution to have more parallel chains and cycles that are more likely to succeed. We work with the UNOS nationwide kidney exchange, which uses a chain cap. Algorithms from our group autonomously make the transplant plans for that exchange. On that real data and demographically-accurate generated data, our new solver scales significantly better than the prior leading approaches.