We study the problem of learning from group comparisons, with applications in predicting outcomes of sports and online games. Most of the previous works in this area focus on learning individual effects--they assume each player has an underlying score, and the "ability" of the team is modeled by the sum of team

Distributed learning allows a group of independent data owners to collaboratively learn a model over their data sets without exposing their private data. We present a distributed learning approach that combines differential privacy with secure multi-party computation. We explore two popular methods of differential privacy, output perturbation and gradient perturbation, and advance the state-of-the-art for both methods in the distributed learning setting. In our output perturbation method, the parties combine local models within a secure computation and then add the required differential privacy noise before revealing the model. In our gradient perturbation method, the data owners collaboratively train a global model via an iterative learning algorithm. At each iteration, the parties aggregate their local gradients within a secure computation, adding sufficient noise to ensure privacy before the gradient updates are revealed. For both methods, we show that the noise can be reduced in the multi-party setting by adding the noise inside the secure computation after aggregation, asymptotically improving upon the best previous results. Experiments on real world data sets demonstrate that our methods provide substantial utility gains for typical privacy requirements.

These mechanisms typically retrieve items in a single step and are fixed after training.

We present a new approach to the problems of evaluating and learning personalized decision policies from observational data of past contexts, decisions, and outcomes. Only the outcome of the enacted decision is available and the historical policy is unknown. These problems arise in personalized medicine using electronic health records and in internet advertising. Existing approaches use inverse propensity weighting (or, doubly robust versions) to make historical outcome (or, residual) data look like it were generated by a new policy being evaluated or learned. But this relies on a plug-in approach that rejects data points with a decision that disagrees with the new policy, leading to high variance estimates and ineffective learning. We propose a new, balance-based approach that too makes the data look like the new policy but does so directly by finding weights that optimize for balance between the weighted data and the target policy in the given, finite sample, which is equivalent to minimizing worst-case or posterior conditional mean square error.

In many biological and medical contexts, we construct a large labeled corpus by aggregating many sources to use in target prediction tasks. Unfortunately, many of the sources may be irrelevant to our target task, so ignoring the structure of the dataset is detrimental. This work proposes a novel approach, the Multiple Domain Matching Network (MDMN), to exploit this structure. MDMN embeds all data into a shared feature space while learning which domains share strong statistical relationships. These relationships are often insightful in their own right, and they allow domains to share strength without interference from irrelevant data. This methodology builds on existing distribution-matching approaches by assuming that source domains are varied and outcomes multi-factorial. Therefore, each domain should only match a relevant subset. Theoretical analysis shows that the proposed approach can have a tighter generalization bound than existing multiple-domain adaptation approaches. Empirically, we show that the proposed methodology handles higher numbers of source domains (up to 21 empirically), and provides state-of-the-art performance on image, text, and multi-channel time series classification, including clinically relevant data of a novel treatment of Autism Spectrum Disorder.

Neural Information Processing Systems http://nips.cc/

First, we show that MMC is theoretically possible (well-posed).

A major difficulty in studying the neural mechanisms underlying olfactory perception is the lack of obvious structure in the relationship between odorants and the neural activity patterns they elicit. Here we use odor-evoked responses in piriform cortex to identify a latent manifold specifying latent distance relationships between olfactory stimuli. Our approach is based on the Gaussian process latent variable model, and seeks to map odorants to points in a low-dimensional embedding space, where distances between points in the embedding space relate to the similarity of population responses they elicit. The model is specified by an explicit continuous mapping from a latent embedding space to the space of high-dimensional neural population firing rates via nonlinear tuning curves, each parametrized by a Gaussian process. Population responses are then generated by the addition of correlated, odor-dependent Gaussian noise. We fit this model to large-scale calcium fluorescence imaging measurements of population activity in layers 2 and 3 of mouse piriform cortex following the presentation of a diverse set of odorants. The model identifies a low-dimensional embedding of each odor, and a smooth tuning curve over the latent embedding space that accurately captures each neuron's response to different odorants.

A surrealist painting from the 1940s by Frida Kahlo has sold for $54.7m (£41.8m) - shattering the auction record for an artwork by a female artist. The painting went for more than 1,000 times its original auction price in 1980, after a tense bidding battle between two collectors, according to the Sotheby's auction house. The auction also broke the previous record for the highest amount paid for a Kahlo portrait, which sold for $34.9 million in 2021. The work - titled El sueño (la cama), which is translated to The dream (The bed) - depicts Kahlo asleep in a canopy bed beneath a skeleton entwined with dynamite. It marks one of the Mexican artist's most psychologically charged self portraits, Sotheby's said, and was painted during a turbulent chapter in Kahlo's life - the year her former lover was assassinated and shortly after her divorce and remarriage.

The growth of artificial intelligence (AI) may eventually lead to fewer entry-level graduates being hired, the boss of accountancy giant PwC has told the BBC. However, global chairman Mohamed Kande said AI was not behind recent job cuts at the firm, adding that the company actually needed to hire hundreds of new AI engineers but was struggling to find them. But some observers say the technology itself threatens thousands of junior jobs across the professional services industry. Speaking on the sidelines of a business summit in Singapore, Mr Kande also said big changes in the global economy, such as US President Donald Trump's sweeping tariffs, had been good for the firm's consulting business. He also addressed the company's suspension in China last year over its work on the collapsed property giant Evergrande, promising that the same mistakes would not happen again.