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September 2020

Solving the Phantom Inventory Problem: Near-optimal Entry-wise Anomaly Detection

September 16, 2020 @ 4:00 pm - 4:30 pm

Tianyi Peng (AeroAstro)

Online

ABSTRACT Tianyi will discuss the work about how to achieve the optimal detection rate for detecting anomalies in a low-rank matrix. The concrete application we are studying is a crucial inventory management problem ('phantom inventory') that by some measures costs retailers approximately 4% in annual sales. We observe that this problem can be modeled as a problem of identifying anomalies in a (low-rank) Poisson matrix. State of the art approaches to anomaly detection in low-rank matrices apparently fall short. Specifically,…

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Model-Based Reinforcement Learning for Countably Infinite State Space MDP

September 9, 2020 @ 4:00 pm - 4:30 pm

Bai Liu (LIDS)

Online

ABSTRACT With the rapid advance of information technology, network systems have become increasingly complex and hence the underlying system dynamics are typically unknown or difficult to characterize. Finding a good network control policy is of significant importance to achieving desirable network performance (e.g., high throughput or low average job delay). Online/sequential learning algorithms are well-suited to learning the optimal control policy from observed data for systems without the information of underlying dynamics. In this work, we consider using model-based reinforcement…

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May 2019

Representing Short-Term Uncertainties in Capacity Expansion Planning Using an Rolling-Horizon Operation Model

May 8, 2019 @ 3:00 pm - 4:00 pm

Espen Flo Boedal (Norwegian University of Science and Technology)

32 – LIDS Lounge

Flexible resources such as batteries and demand-side management technologies are needed to handle future large shares of variable renewable power. Wind and solar power introduce more short-term uncertainty that have to be considered when making investment decisions as it significantly impacts the value of flexible resources. In this work we present a method for using duals from a rolling horizon operational model, with wind power uncertainty and market representations, to represent power system operation in an investment problem. The method…

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Generalization and Learning under Dobrushin’s Condition

May 1, 2019 @ 3:00 pm - 4:00 pm

Yuval Dagan (EECS)

32 – LIDS Lounge

Statistical learning theory has largely focused on learning and generalization given independent and identically distributed (i.i.d.) samples. Motivated by applications involving time-series data, there has been a growing literature on learning and generalization in settings where data is sampled from an ergodic process. This work has also developed complexity measures, which appropriately extend Rademacher complexity to bound the generalization error and learning rates of hypothesis classes in this setting. Rather than time-series data, our work is motivated by settings where…

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April 2019

Hierarchical Bayesian Network Model for Probabilistic Estimation of EV Battery Life

April 24, 2019 @ 3:00 pm - 4:00 pm

Mehdi Jafari (LIDS)

32 – LIDS Lounge

Bayesian models are applied to probabilistic analysis of phenomena which deal with multiple external stochastic factors and unmeasurable variables. Considering the large amount of available data for the EV driving, recharging and grid services such as solar charging which contains uncertainties and measurement errors, and their hierarchical effect on the battery life, this application of Bayesian models can be useful for the aging probabilistic analysis. Causality is of utmost importance for batteries as their aging is affected by a high…

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