Abstract: Described herein are compounds that block the activity of LOX family members having good IC50 values, no cellular toxicity below 10 ?M, induce sensitization of the cells to doxorubicin, strong activity in a recombinant LOX/LOXL2 activity, and a chemical structure that is drug-like and does not have a PAINS flag, as well as, methods of treatment using the compounds with respect to cancer, organ fibrosis, neurodegenerative and cardiovascular diseases.

Abstract: A system for determining predictive models associated with online advertising can include a communications interface, a processor, and a display. The communications interface can be configured to receive a partial dataset. The partial dataset may include user information. The processor can be communicatively coupled to the communications interface and configured to identify the partial dataset. The processor can also be configured to determine a first predictive model corresponding to at least part of the partial dataset and a second predictive model by combining a probability distribution with the first predictive model. The display can be communicatively coupled to the processor and configured to display the second predictive model.

Abstract: A method for combining multiple probability of click models in an online advertising system into a combined predictive model, the method commencing by receiving a feature set slice (e.g. corresponding to demographics or taxonomies or clusters), and using the sliced data for training multiple slice-wise predictive models. The trained slice-wise predictive models are combined by overlaying a weighted distribution model over the trained slice-wise predictive models. The combined predictive model then is used in predicting the probability of a click given a query-advertisement pair in online advertising. The method can flexibly receive slice specifications, and can overlay any one or more of a variety of distribution models, such as a linear combination or a log-linear combination.

Abstract: A machine-learning method for estimating probability of a click event in online advertising systems by computing and comparing an aggregated predictive model (a global model) and one or more data-wise sliced predictive models (local models). The method comprises receiving training data having a plurality of features stored in a feature set and constructing a global predictive model that estimates the probability of a click event for the processed feature set. Then, partitioning the global predictive model into one or more data-wise sliced training sets for training a local model from each of the data-wise slices, and then determining whether a particular local model estimates probability of click event for the feature set better than the global model. A given feature set may be collected from historical data, and may comprise a feature vector for a plurality of query-advertisement pairs and a corresponding indicator that represents a click on the advertisement.

Abstract: Embodiments employ a maximum likelihood estimation (MLE) under a covariance matrix floor constraint to predict missing data from observed data. An MLE solution is obtained for approximately Gaussian distributions under the constraint that the covariance matrix is greater than or equal to a positive-definite matrix. In one embodiment, an offline model estimation is performed using an expectation-maximization (EM) approach to estimate various statistical parameters based on observed data. Then, in an online approach, parameters for various missing CTR data may be predicted based on the offline estimated statistical parameters. A non-limiting, non-exhaustive example using the constrained MLE approach is described for predicting missing click-through rate data useable in selecting an advertisement to display with a search query result.

Abstract: Sponsored search advertising utilizes a click probability as one factor in selecting and ranking advertisements that are displayed with search results. The probability of click may also be referred to as a predicted click-through rate (“CTR”) that may be multiplied by an advertiser's bid for a particular advertisement to rank the display of advertisements. An accurate prediction of the click probability improves the potential revenue that is generated by advertisements in a pay per click system. Other advertising systems may benefit from an accurate and reliable estimate for an advertisement's probability of click in different environments and scenarios.

Abstract: A method for combining multiple probability of click models in an online advertising system into a combined predictive model, the method commencing by receiving a feature set slice (e.g. corresponding to demographics or taxonomies or clusters), and using the sliced data for training multiple slice-wise predictive models. The trained slice-wise predictive models are combined by overlaying a weighted distribution model over the trained slice-wise predictive models. The combined predictive model then is used in predicting the probability of a click given a query-advertisement pair in online advertising. The method can flexibly receive slice specifications, and can overlay any one or more of a variety of distribution models, such as a linear combination or a log-linear combination.

Abstract: A machine-learning method for estimating probability of a click event in online advertising systems by computing and comparing an aggregated predictive model (a global model) and one or more data-wise sliced predictive models (local models). The method comprises receiving training data having a plurality of features stored in a feature set and constructing a global predictive model that estimates the probability of a click event for the processed feature set. Then, partitioning the global predictive model into one or more data-wise sliced training sets for training a local model from each of the data-wise slices, and then determining whether a particular local model estimates probability of click event for the feature set better than the global model. A given feature set may be collected from historical data, and may comprise a feature vector for a plurality of query-advertisement pairs and a corresponding indicator that represents a click on the advertisement.

Abstract: Sponsored search advertising utilizes a click probability as one factor in selecting and ranking advertisements that are displayed with search results. The probability of click may also be referred to as a predicted click-through rate (“CTR”) that may be multiplied by an advertiser's bid for a particular advertisement to rank the display of advertisements. An accurate prediction of the click probability improves the potential revenue that is generated by advertisements in a pay per click system. Other advertising systems may benefit from an accurate and reliable estimate for an advertisement's probability of click in different environments and scenarios.

Abstract: Embodiments employ a maximum likelihood estimation (MLE) under a covariance matrix floor constraint to predict missing data from observed data. An MLE solution is obtained for approximately Gaussian distributions under the constraint that the covariance matrix is greater than or equal to a positive-definite matrix. In one embodiment, an offline model estimation is performed using an expectation-maximization (EM) approach to estimate various statistical parameters based on observed data. Then, in an online approach, parameters for various missing CTR data may be predicted based on the offline estimated statistical parameters. A non-limiting, non-exhaustive example using the constrained MLE approach is described for predicting missing click-through rate data useable in selecting an advertisement to display with a search query result.

Abstract: An improved system and method using text features for click prediction of sponsored search advertising is provided. A maximum entropy click prediction model that predicts the click probability of query-advertisement pairs may be generated from click feedback features and word pair features of a query and an advertisement. The maximum entropy click prediction model may be used to obtain click probabilities for query-advertisement pairs to determine and serve a ranked list of advertisements for display with query results in an online keyword search auction. A search query may be received and word features from the search query may be input into the maximum entropy click prediction model to obtain click probabilities for query-advertisement pairs. A list of advertisements may be ranked using click probabilities for query-advertisement pairs, the list of ranked advertisements may be priced in an online search keyword auction and served for display with search query results.