Ordinal Bucketing for Game Trees using Dynamic Quantile Approximation

In this paper, we present a simple and cheap ordinal bucketing algorithm that approximately generates $q$-quantiles from an incremental data stream. The bucketing is done dynamically in the sense that the amount of buckets $q$ increases with the number of seen samples. We show how this can be used in Ordinal Monte Carlo Tree Search (OMCTS) to yield better bounds on time and space complexity, especially in the presence of noisy rewards. Besides complexity analysis and quality tests of quantiles, we evaluate our method using OMCTS in the General Video Game Framework (GVGAI). Our results demonstrate its dominance over vanilla Monte Carlo Tree Search in the presence of noise, where OMCTS without bucketing has a very bad time and space complexity.Comment: preprin

An Ordinal Agent Framework

In this thesis, we introduce algorithms to solve ordinal multi-armed bandit problems, Monte-Carlo tree search, and reinforcement learning problems. With ordinal problems, an agent does not receive numerical rewards, but ordinal rewards that cope without any distance measure. For humans, it is often hard to define or to determine exact numerical feedback signals but simpler to come up with an ordering over possibilities. For instance, when looking at medical treatment, the ordering patient death < patient ill < patient cured is easy to come up with but it is hard to assign numerical values to them. As most state-of-the-art algorithms rely on numerical operations, they can not be applied in the presence of ordinal rewards. We present a preference-based approach leveraging dueling bandits to sequential decision problems and discuss its disadvantages in terms of sample efficiency and scalability. Following another idea, our final approach to identify optimal arms is based on the comparison of reward distributions using the Borda method. We test this approach on multi-armed bandits, leverage it to Monte-Carlo tree search, and also apply it to reinforcement learning. To do so, we introduce a framework that encapsulates the similarities of the different problem definitions. We test our ordinal algorithms on frameworks like the General Video Game Framework (GVGAI), OpenAI, or synthetic data and compare it to ordinal, numerical, or domain-specific algorithms. Since our algorithms are time-dependent on the number of perceived ordinal rewards, we introduce a binning method that artificially reduces the number of rewards

Efficient estimation of statistical functions while preserving client-side privacy

Aggregating service users’ personal data for analytical purposes is a common practice in today’s Internet economy. However, distrust in the data aggregator, data breaches and risks of subpoenas pose significant challenges in the availability of data. The framework of differential privacy is enjoying wide attention due to its scalability and rigour of privacy protection it provides, and has become a de facto standard for facilitating privacy preserving information extraction. In this dissertation, we design and implement resource efficient algorithms for three fundamental data analysis primitives, marginal, range, and count queries while providing strong differential privacy guarantees. The first two queries are studied in the strict scenario of untrusted aggregation (aka local model) in which the data collector is allowed to only access the noisy/perturbed version of users’ data but not their true data. To the best of our knowledge, marginal and range queries have not been studied in detail in the local setting before our works. We show that our simple data transfomation techniques help us achieve great accuracy in practice and can be used for performing more interesting analysis. Finally, we revisit the problem of count queries under trusted aggregation. This setting can also be viewed as a relaxation of the local model called limited precision local differential privacy. We first discover certain weakness in a well-known optimization framework leading to solutions exhibiting pathological behaviours. We then propose more constraints in the framework to remove these weaknesses without compromising too much on utility

LIPIcs, Volume 261, ICALP 2023, Complete Volume

LIPIcs, Volume 261, ICALP 2023, Complete Volum

Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum