Auditing: Active Learning with Outcome-Dependent Query Costs

We propose a learning setting in which unlabeled data is free, and the cost of a label depends on its value, which is not known in advance. We study binary classification in an extreme case, where the algorithm only pays for negative labels. Our motivation are applications such as fraud detection, in which investigating an honest transaction should be avoided if possible. We term the setting auditing, and consider the auditing complexity of an algorithm: the number of negative labels the algorithm requires in order to learn a hypothesis with low relative error. We design auditing algorithms for simple hypothesis classes (thresholds and rectangles), and show that with these algorithms, the auditing complexity can be significantly lower than the active label complexity. We also discuss a general competitive approach for auditing and possible modifications to the framework.Comment: Corrections in section

Factorizing LambdaMART for cold start recommendations

Recommendation systems often rely on point-wise loss metrics such as the mean squared error. However, in real recommendation settings only few items are presented to a user. This observation has recently encouraged the use of rank-based metrics. LambdaMART is the state-of-the-art algorithm in learning to rank which relies on such a metric. Despite its success it does not have a principled regularization mechanism relying in empirical approaches to control model complexity leaving it thus prone to overfitting. Motivated by the fact that very often the users' and items' descriptions as well as the preference behavior can be well summarized by a small number of hidden factors, we propose a novel algorithm, LambdaMART Matrix Factorization (LambdaMART-MF), that learns a low rank latent representation of users and items using gradient boosted trees. The algorithm factorizes lambdaMART by defining relevance scores as the inner product of the learned representations of the users and items. The low rank is essentially a model complexity controller; on top of it we propose additional regularizers to constraint the learned latent representations that reflect the user and item manifolds as these are defined by their original feature based descriptors and the preference behavior. Finally we also propose to use a weighted variant of NDCG to reduce the penalty for similar items with large rating discrepancy. We experiment on two very different recommendation datasets, meta-mining and movies-users, and evaluate the performance of LambdaMART-MF, with and without regularization, in the cold start setting as well as in the simpler matrix completion setting. In both cases it outperforms in a significant manner current state of the art algorithms

Fast Differentially Private Matrix Factorization

Differentially private collaborative filtering is a challenging task, both in terms of accuracy and speed. We present a simple algorithm that is provably differentially private, while offering good performance, using a novel connection of differential privacy to Bayesian posterior sampling via Stochastic Gradient Langevin Dynamics. Due to its simplicity the algorithm lends itself to efficient implementation. By careful systems design and by exploiting the power law behavior of the data to maximize CPU cache bandwidth we are able to generate 1024 dimensional models at a rate of 8.5 million recommendations per second on a single PC

Regularized fitted Q-iteration: application to planning

We consider planning in a Markovian decision problem, i.e., the problem of finding a good policy given access to a generative model of the environment. We propose to use fitted Q-iteration with penalized (or regularized) least-squares regression as the regression subroutine to address the problem of controlling model-complexity. The algorithm is presented in detail for the case when the function space is a reproducing kernel Hilbert space underlying a user-chosen kernel function. We derive bounds on the quality of the solution and argue that data-dependent penalties can lead to almost optimal performance. A simple example is used to illustrate the benefits of using a penalized procedure

Unravelling the Structure of Magnus' Pink Salt

A combination of multinuclear ultra-wideline solid-state NMR, powder X-ray diffraction (pXRD), X-ray absorption fine structure experiments, and first principles calculations of platinum magnetic shielding tensors has been employed to reveal the previously unknown crystal structure of Magnus’ pink salt (MPS), [Pt(NH3)4][PtCl4], study the isomeric Magnus’ green salt (MGS), [Pt(NH3)4][PtCl4], and examine their synthetic precursors K2PtCl4 and Pt(NH3)4Cl2·H2O. A simple synthesis of MPS is detailed which produces relatively pure product in good yield. Broad 195Pt, 14N, and 35Cl SSNMR powder patterns have been acquired using the WURST-CPMG and BRAIN-CP/WURST-CPMG pulse sequences. Experimentally measured and theoretically calculated platinum magnetic shielding tensors are shown to be very sensitive to the types and arrangements of coordinating ligands as well as intermolecular Pt–Pt metallophilic interactions. High-resolution 195Pt NMR spectra of select regions of the broad 195Pt powder patterns, in conjunction with an array of 14N and 35Cl spectra, reveal clear structural differences between all compounds. Rietveld refinements of synchrotron pXRD patterns, guided by first principles geometry optimization calculations, yield the space group, unit cell parameters, and atomic positions of MPS. The crystal structure has P-1 symmetry and resides in a pseudotetragonal unit cell with a distance of >5.5 Å between Pt sites in the square-planar Pt units. The long Pt–Pt distances and nonparallel orientation of Pt square planes prohibit metallophilic interactions within MPS. The combination of ultra-wideline NMR, pXRD, and computational methods offers much promise for future investigation and characterization of Pt-containing systems

The Role of Friction in Compaction and Segregation of Granular Materials

We investigate the role of friction in compaction and segregation of granular materials by combining Edwards' thermodynamic hypothesis with a simple mechanical model and mean-field based geometrical calculations. Systems of single species with large friction coefficients are found to compact less. Binary mixtures of grains differing in frictional properties are found to segregate at high compactivities, in contrary to granular mixtures differing in size, which segregate at low compactivities. A phase diagram for segregation vs. friction coefficients of the two species is generated. Finally, the characteristics of segregation are related directly to the volume fraction without the explicit use of the yet unclear notion of compactivity.Comment: 9 pages, 6 figures, submitted to Phys. Rev.