We present a new strategy for gap estimation in randomized algorithms for multiarmed bandits and combine it with the EXP3++ algorithm of Seldin and Slivkins (2014). In the stochastic regime the strategy reduces dependence of regret on a time horizon from $(\ln t)^3$ to $(\ln t)^2$ and eliminates an additive factor of order $\Delta e^{1/\Delta^2}$, where $\Delta$ is the minimal gap of a problem instance. In the adversarial regime regret guarantee remains unchanged.

Predicting the efficacy of a drug for a given individual, using high-dimensional genomic measurements, is at the core of precision medicine. However, identifying features on which to base the predictions remains a challenge, especially when the sample size is small. Incorporating expert knowledge offers a promising alternative to improve a prediction model, but collecting such knowledge is laborious to the expert if the number of candidate features is very large. We introduce a probabilistic model that can incorporate expert feedback about the impact of genomic measurements on the sensitivity of a cancer cell for a given drug. We also present two methods to intelligently collect this feedback from the expert, using experimental design and multi-armed bandit models. In a multiple myeloma blood cancer data set (n=51), expert knowledge decreased the prediction error by 8%. Furthermore, the intelligent approaches can be used to reduce the workload of feedback collection to less than 30% on average compared to a naive approach.

A new approach of solving the ill-conditioned inverse problem for analytical continuation is proposed. The root of the problem lies in the fact that even tiny noise of imaginary-time input data has a serious impact on the inferred real-frequency spectra. By means of a modern regularization technique, we eliminate redundant degrees of freedom that essentially carry the noise, leaving only relevant information unaffected by the noise. The resultant spectrum is represented with minimal bases and thus a stable analytical continuation is achieved. This framework further provides a tool for analyzing to what extent the Monte Carlo data need to be accurate to resolve details of an expected spectral function.

Many unsupervised kernel methods rely on the estimation of the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). Both kernel CO and kernel CCO are sensitive to contaminated data, even when bounded positive definite kernels are used. To the best of our knowledge, there are few well-founded robust kernel methods for statistical unsupervised learning. In addition, while the influence function (IF) of an estimator can characterize its robustness, asymptotic properties and standard error, the IF of a standard kernel canonical correlation analysis (standard kernel CCA) has not been derived yet. To fill this gap, we first propose a robust kernel covariance operator (robust kernel CO) and a robust kernel cross-covariance operator (robust kernel CCO) based on a generalized loss function instead of the quadratic loss function. Second, we derive the IF for robust kernel CCO and standard kernel CCA. Using the IF of the standard kernel CCA, we can detect influential observations from two sets of data. Finally, we propose a method based on the robust kernel CO and the robust kernel CCO, called {\bf robust kernel CCA}, which is less sensitive to noise than the standard kernel CCA. The introduced principles can also be applied to many other kernel methods involving kernel CO or kernel CCO. Our experiments on synthesized data and imaging genetics analysis demonstrate that the proposed IF of standard kernel CCA can identify outliers. It is also seen that the proposed robust kernel CCA method performs better for ideal and contaminated data than the standard kernel CCA.

Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhibit poor performance. We present a simple, effective technique for accounting for label noise when training deep neural networks. We augment a standard deep network with a softmax layer that models the label noise statistics. Then, we train the deep network and noise model jointly via end-to-end stochastic gradient descent on the (perhaps mislabeled) dataset. The augmented model is overdetermined, so in order to encourage the learning of a non-trivial noise model, we apply dropout regularization to the weights of the noise model during training. Numerical experiments on noisy versions of the CIFAR-10 and MNIST datasets show that the proposed dropout technique outperforms state-of-the-art methods.

I spoke with Michael Johnston, Lead Inventive Scientist at Interactions, about frequently asked questions about AI and Machine Learning as they apply to customer care. How do you define Artificial Intelligence and Machine Learning? Artificial Intelligence refers to the capability of a machine to imitate intelligent human behavior. Put another way, AI technologies are algorithms that attempt to mimic things that humans do. Machine Learning, on the other hand, is the science and engineering of giving computers the ability to learn without being explicitly programmed -- algorithms that learn from data.

A new working paper finds that the arrival of one new industrial robot in a local labor market coincides with an employment drop of 5.6 workers. These papers have not been peer-reviewed, but are circulated by their authors for comment and discussion. With the NBER's blessing, Making Sen$e is pleased to feature these summaries regularly on our page. The following summary was written by the NBER and doesn't necessarily reflect the views of Making Sen$e. With America's workers already squeezed by forces ranging from international competition to offshoring to new information technologies, concern is growing about the impact of robots on jobs and wages.

I've been working on a tutorial series for creating self-driving cars in Grand Theft Auto 5 for a bit now. The most recent creation is the result of day or so worth of collecting training data, and about 4 days of actual training of the model. It's currently a 30-layer convolutional neural network, it works purely on a frame-by-frame basis with no preprocessing other than an image resize and grayscale. It makes actions based on the current frame's pixel data, with no memory of what it's been doing. I plan to eventually incorporate some form of memory with something like recurrent layers, but...baby steps at a time!

The Watson win was a major win for IBM IBM, -0.31% at the time, underscoring its transition to a new-age technology company with artificially-intelligent computers. On Friday, shares of IBM fell 3% to $154.45 after billionaire investor Warren Buffett announced that his company Berkshire Hathaway Inc. BRK.A, -1.07% It has unloaded 30 million shares so far in 2017, from the 81 million shares it held at the end of 2016. Buffett bought more than $10 billion in IBM shares the year Watson won "Jeopardy," and increased his stake a few more times in the years proceeding. IBM reported profit and revenue declines, even as its newer software-as-a-service business saw revenue gains of 60%.

It may share the same name, but the video game Prey has little in common with its 2006 predecessor. Publisher Bethesda Softworks has just launched Prey, a science-fiction epic carrying the brand the video game publisher's parent company, ZeniMax Media, acquired nearly eight years ago. The original Prey, which publisher 2K Games launched in 2006, starred Native American protagonist Tommy, who embarks on a quest to stop aliens from destroying Earth after he's abducted along with his girlfriend and his grandfather. Players explored a spaceship with living flesh-like interiors and warp holes used to navigate. The 2017 Prey for PC, PlayStation 4 and Xbox One features Morgan Yu, a participant in experiments done aboard the space station Talos I.