Abstract: Systems and methods for dialogue response prediction can leverage a plurality of machine-learned language models to generate a plurality of candidate outputs, which can be processed by a dialogue management model to determine a predicted dialogue response. The plurality of machine-learned language models can include a plurality of experts trained on different intents, emotions, and/or tasks. The particular candidate output selected may be selected by the dialogue management model based on semantics determined based on a language representation. The language representation can be a representation generated by processing the conversation history of a conversation to determine conversation semantics.

Abstract: A computer system uses Learning from Demonstration (LfD) techniques in which a multitude of tasks are demonstrated without requiring careful task set up, labeling, and engineering, and learns multiple modes of behavior from visual data, rather than averaging the multiple modes. As a result, the computer system may be used to control a robot or other system to exhibit the multiple modes of behavior in appropriate circumstances.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network system used to control an agent interacting with an environment. One of the methods includes obtaining a plurality of transitions that are each generated as a result of an agent interacting with an environment, and training a Q neural network having a mixed-integer programming (MIP) formulation on the transitions. The Q neural network is configured to process an observation and initial action constraints in accordance with the Q network parameters to generate a MIP problem based on a Q value objective and the initial action constraints. The initial action constraints specify a set of possible actions that can be performed by the agent to interact with the environment.

Abstract: Sample size determination techniques in sequential hypothesis testing in a digital medium environment are described. The sample size may be determined before a test to define a number of samples (e.g., user interactions with digital marketing content) that are likely to be tested as part of the sequential hypothesis testing in order to achieve a result. The sample size may also be determined in real time to define a number of samples that likely remain for testing in order to achieve a result. The sample size may be determined in a variety of ways, such as through simulation, based on a gap between conversion rates for different options being tested, and so on.

Abstract: Sequential hypothesis testing techniques are described, which involve testing sequences of increasingly larger number of samples until a winner is determined. In particular, sequential hypothesis testing techniques is based on whether a result of a statistic has reached statistical significance that defines a confidence level in the accuracy of the results. Sequential hypothesis testing also permits the user to “peek” into the test through use of a user interface (e.g., dashboard) to monitor the test in real time as it is being run. Real time output of this information in a user interface as a part of sequential hypothesis testing may be leveraged in a variety of ways. In a first example, a user may make changes as the test is run. In another example, flexible execution is also made possible in that the test may continue to run even if initial accuracy guarantees have been met.

Abstract: Embodiments of the present invention are directed at providing a sequential multiple hypothesis testing system. In one embodiment, feedback is collected for hypothesis tests of a multiple hypothesis tests. Based on the collected feedback, a sequential p-value is calculated for each of the hypothesis tests utilizing a sequential statistic procedure that is designed to compare an alternative case with a base case for a respective hypothesis test. A sequential rejection procedure can then be applied to determine whether any of the hypothesis tests have concluded based on the respective p-value. A result of the determination can then be output to apprise a user of a state of the multiple hypothesis test. This process can then be repeated until a maximum sample size is reached, termination criterion is met, or all tests are concluded. Other embodiments may be described and/or claimed.

Abstract: A computer system uses Learning from Demonstration (LfD) techniques in which a multitude of tasks are demonstrated without requiring careful task set up, labeling, and engineering, and learns multiple modes of behavior from visual data, rather than averaging the multiple modes. As a result, the computer system may be used to control a robot or other system to exhibit the multiple modes of behavior in appropriate circumstances.

Abstract: Systems and methods for selecting optimal policies that maximize expected return subject to given risk tolerance and confidence levels. In particular, methods and systems for selecting an optimal ad recommendation policy—based on user data, a set of ad recommendation policies, and risk thresholds—by sampling the user data and estimating gradients. The system or methods utilize the estimated gradients to select a good ad recommendation policy (an ad recommendation policy with high expected return) subject to the risk tolerance and confidence levels. To assist in selecting a risk-sensitive ad recommendation policy, a gradient-based algorithm is disclosed to find a near-optimal policy for conditional-value-at-risk (CVaR) risk-sensitive optimization.

Abstract: Embodiments of the present invention are directed at providing a sequential multiple hypothesis testing system. In one embodiment, feedback is collected for hypothesis tests of a multiple hypothesis tests. Based on the collected feedback, a sequential p-value is calculated for each of the hypothesis tests utilizing a sequential statistic procedure that is designed to compare an alternative case with a base case for a respective hypothesis test. A sequential rejection procedure can then be applied to determine whether any of the hypothesis tests have concluded based on the respective p-value. A result of the determination can then be output to apprise a user of a state of the multiple hypothesis test. This process can then be repeated until a maximum sample size is reached, termination criterion is met, or all tests are concluded. Other embodiments may be described and/or claimed.

Abstract: Sequential hypothesis testing techniques are described, which involve testing sequences of increasingly larger number of samples until a winner is determined. In particular, sequential hypothesis testing techniques is based on whether a result of a statistic has reached statistical significance that defines a confidence level in the accuracy of the results. Sequential hypothesis testing also permits the user to “peek” into the test through use of a user interface (e.g., dashboard) to monitor the test in real time as it is being run. Real time output of this information in a user interface as a part of sequential hypothesis testing may be leveraged in a variety of ways. In a first example, a user may make changes as the test is run. In another example, flexible execution is also made possible in that the test may continue to run even if initial accuracy guarantees have been met.

Abstract: Sample size determination techniques in sequential hypothesis testing in a digital medium environment are described. The sample size may be determined before a test to define a number of samples (e.g., user interactions with digital marketing content) that are likely to be tested as part of the sequential hypothesis testing in order to achieve a result. The sample size may also be determined in real time to define a number of samples that likely remain for testing in order to achieve a result. The sample size may be determined in a variety of ways, such as through simulation, based on a gap between conversion rates for different options being tested, and so on.

Abstract: Systems and methods for selecting optimal policies that maximize expected return subject to given risk tolerance and confidence levels. In particular, methods and systems for selecting an optimal ad recommendation policy—based on user data, a set of ad recommendation policies, and risk thresholds—by sampling the user data and estimating gradients. The system or methods utilize the estimated gradients to select a good ad recommendation policy (an ad recommendation policy with high expected return) subject to the risk tolerance and confidence levels. To assist in selecting a risk-sensitive ad recommendation policy, a gradient-based algorithm is disclosed to find a near-optimal policy for conditional-value-at-risk (CVaR) risk-sensitive optimization.