September 9, 2020
Bai Liu (LIDS)
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...
September 16, 2020
Tianyi Peng (AeroAstro)
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...
September 23, 2020
Maryann Rui (LIDS)
The design of data markets has gained in importance as firms increasingly use predictions from machine learning models to make their operations more effective, yet need to externally acquire the necessary training data to fit such models. This is...
September 30, 2020
Horia Mania (LIDS)
While the identification of nonlinear dynamical systems is a fundamental building block of model-based reinforcement learning and feedback control, its sample complexity is only understood for systems that either have discrete states and actions or...
October 7, 2020
Joshua Robinson (CSAIL)
We study generalization properties of weakly supervised learning. That is, learning where only a few true labels are present for a task of interest but many more “weak” labels are available. In particular, we show that embeddings trained using weak...
October 14, 2020
Jingzhao Zhang (LIDS)
While stochastic gradient descent (SGD) is still the de facto algorithm in deep learning, adaptive methods like Adam have been observed to outperform SGD across important tasks, such as NLP models. The settings under which SGD performs poorly in...
October 21, 2020
Jason Altschuler (LIDS)
Low-rank approximation of the Gaussian kernel is a core component of many data-science algorithms. Often the approximation is desired over an algebraic variety (e.g., in problems involving sparse data or low-rank data). Can better approximations be...
October 28, 2020
Sarah Cen (LIDS)
Social media platforms moderate content using a process known as algorithmic filtering (AF). While AF has the potential to greatly improve the user experience, it has also drawn intense scrutiny for its roles in, for example, spreading fake news,...
November 4, 2020
Alireza Fallah (LIDS)
In Federated Learning, we aim to train models across multiple computing units (users), while users can only communicate with a common central server, without exchanging their data samples. This mechanism exploits the computational power of all users...
November 18, 2020
Ezra Tal (LIDS)
In recent years, autonomous unmanned aerial vehicles (UAVs) that can execute aggressive (i.e., fast and agile) maneuvers have attracted significant attention. We focus on the design of control algorithms for accurate tracking of such maneuvers. This...
December 2, 2020
Farzan Farnia (EECS)
The success of minimax learning problems of generative adversarial networks (GANs) and adversarial training has been observed to depend on the minimax optimization algorithm used for their training. This dependence is commonly attributed to the...
December 9, 2020
Xiang Cheng (LIDS)
