Events

A key challenge in data-driven decision-making is the presence of estimation errors in the prediction models, which tend to be amplified by the subsequent optimization model — a phenomenon that is often referred to as the Optimizer’s Curse. A contemporary approach to combat such estimation errors is offered by distributionally robust problem formulations that consider all data-generating distributions close to the empirical distribution derived from historical samples, where ‘closeness’ is determined by the Wasserstein distance. While those techniques show significant promise in problems where all input features are continuous, they scale exponentially when categorical features are present. This work demonstrates that such mixed-feature problems can indeed be solved in polynomial time. We present a practically efficient algorithm to solve mixed-feature problems and compare our method against alternative techniques.

ISTD PhD Oral Defense Seminar presented by Perry Lam – Sparsity in Text-to-Speech

ISTD PhD Oral Defense presented by Gong Jia – Towards Data Efficient, Reliable and Flexible 3D Digital Human Modeling

ISTD PhD Oral Defense presented by Haoran Li – Overcoming the Limitations of Autoregressive and Non-Autoregressive Neural Models

ISTD PhD Oral Defense presented by Li Xu – Towards Effective, Robust, and Continual Multi-modal Learning

Combinatorial Game Theory is the branch of Mathematics and Computer Science that studies two-player games (the players areLeft/Positive and Right/Negative) with perfect and complete information; there is no chance, and there are no hidden moves. In the 1970s John Conway defined normal play game comparison via the rule G > H if Left wins G-H playing second. He understood that normal play games constitute a group structure, with respect to the disjunctive sum operator. Nowadays we rather define this inequality by G > H if, for all games X, the perfect play outcome of G + X is no worse than that of H + X, with respect to player Left. And we call Conway’s discovery, that the notions are equivalent, the “fundamental theorem of normal play”. We explain the relevance of this result, and as an illustration, compute game values of some classical ruleset positions, such as Hackenbush, Domineering, Toppling Dominoes, Toads and Frogs and more.

In this talk, the design dilemma of “What will be different between near-field communications (NFC) and far-field communications (FFC)?” is discussed from four perspectives. (1) From the channel modelling perspective, the differences between near-field and far-field channel models are discussed. (2) From the performance analysis perspective, analytical results for characterizing the degrees of freedom and the power scaling laws in the near-field region are provided. (3) From the beamforming perspective, the features of far-field beamsteering and near-field beamfocusing are compared. A couple of new beamforming structures for NFC are also introduced. (4) From the application perspective, several new designs are discussed in the context of promising next-generation technologies in NFC. Finally, research opportunities and problem are discussed.

ISTD PhD Oral Defense presented by Rulin Chen – Modeling and Design of Assemblies with Discrete Equivalence Classes

ISTD PhD Oral Defense presented by Ms. Menglin Li – Leveraging Pre-trained Language Models for Social Geolocation

This talk aims to present the current research efforts on the development of implementing distributed machine learning algorithms in wireless systems. Specifically, we provide a comprehensive coverage of a distributed learning paradigm based on over-the-air computing, a.k.a. over-the-air federated learning (OTA-FL). We will present an analytical framework that quantifies the convergence rate of OTA-FL. Then, we discuss the system enhancements from an algorithmic perspective, i.e., adopting adaptive optimizations to accelerate the model training. We also introduce model pruning schemes that reduce the computation and communication overheads for OTA-FL. Finally, we will elaborate on the analysis of generalization error of the statistical models trained by OTA-FL, which shows that wireless interference has the positive potential of improving the generalization capability.