Past Events

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Join us for a special virtual installment of the ML Seminar Series: In this talk, we aim to quantify the robustness of distributed training against worst-case failures and adversarial nodes. We show that there is a gap between robustness guarantees, depending on whether adversarial nodes have full control of the hardware, the training data, or both. Using ideas from robust statistics and coding theory we establish robust and scalable training methods for centralized, parameter server systems.

Few-shot classification, the task of adapting a classifier to unseen classes given a small labeled dataset, is an important step on the path toward human-like machine learning. I will present what I think are some of the key advances and open questions in this area. I will then focus on the fundamental issue of overfitting in the few-shot scenario. Bayesian methods are well-suited to tackling this issue because they allow practitioners to specify prior beliefs and update those beliefs in light of observed data.

Large-scale machine learning training, in particular, distributed stochastic gradient descent (SGD), needs to be robust to inherent system variability such as unpredictable computation and communication delays. This work considers a distributed SGD framework where each worker node is allowed to perform local model updates and the resulting models are averaged periodically. Our goal is to analyze and improve the true speed of error convergence with respect to wall-clock time (instead of the number of iterations).

Machine learning today bears resemblance to the field of aviation soon after the Wright Brothers’ pioneering flights in the early 1900s. It took half a century of aeronautical engineering advances for the ‘Jet Age’ (i.e., commercial aviation) to become a reality. Similarly, machine learning (ML) is currently experiencing a renaissance, yet fundamental barriers must be overcome to fully unlock the potential of ML-powered technology. In this talk, I describe our work to help democratize ML by tackling barriers related to scalability, privacy, and safety.

Much of the prior work on scheduling algorithms for wireless networks focuses on maximizing throughput. However, for many real-time applications, delays and deadline guarantees on packet delivery can be more important than long-term throughput. In this talk, we consider the problem of scheduling deadline-constrained packets in wireless networks, under a conflict-graph interference model. The objective is to guarantee that at least a certain fraction of packets of each link are delivered within their deadlines, which is referred to as delivery ratio.

In this talk, I will talk about principled ways of solving a classical reinforcement learning (RL) problem and introduce its robust variant.

We will discuss two problems that have different application spaces but share a common mathematical core. These problems combine stochastic approximation, an iterative method for finding the fixed point of a function from noisy observations, and consensus, a general averaging technique for multiple agents to cooperatively solve a distributed problem.

Submodular functions model the intuitive notion of diminishing returns. Due to their far-reaching applications, they have been rediscovered in many fields such as information theory, operations research, statistical physics, economics, and machine learning. They also enjoy computational tractability as they can be minimized exactly or maximized approximately.  The goal of this talk is simple. We see how a little bit of randomness, a little bit of greediness, and the right combination can lead to pretty good methods for offline, streaming, and distributed solutions.

Join us for the 17th Texas Wireless Summit on November 12, 2019. ​This year's Summit will highlight advances and opportunities at the intersection of human-centered computing, sensing and connectivity. Sessions and panels will focus on wearables, virtual and mixed reality, bio-interfaces, and perception. We will explore the challenges and demands of the communication infrastructure required to support and enhance devices and experiences.