CSML Seminar Series

  • The efficiency of kernel methods on structured datasets

    Wed, May 5, 2021, 4:30 pm
    Inspired by the proposal of tangent kernels of neural networks (NNs), a recent research line aims to design kernels with a better generalization performance on standard datasets. Indeed, a few recent works showed that certain kernel machines perform as well as NNs on certain datasets, despite their separations in specific cases implied by theoretical results. Furthermore, it was shown that the induced kernels of convolutional neural networks perform much better than any former handcrafted kernels. These empirical results pose a theoretical challenge to understanding the performance gaps in kernel machines and NNs in different scenarios.
  • Preconditioning Helps: Faster Convergence in Statistical and Reinforcement Learning

    Mon, Apr 19, 2021, 4:30 pm
    While exciting progress has been made in understanding the global convergence of vanilla gradient methods for solving challenging nonconvex problems in statistical estimation and machine learning, their computational efficacy is still far from satisfactory for ill-posed or ill-conditioned problems. In this talk, we discuss how the trick of preconditioning further boosts the convergence speed with minimal computation overheads through two examples: low-rank matrix estimation in statistical learning and policy optimization in entropy-regularized reinforcement learning.
  • Deep Networks from First Principles

    Fri, Jan 15, 2021, 4:30 pm
    In this talk, we offer an entirely “white box’’ interpretation of deep (convolutional) networks from the perspective of data compression. In particular, we show how modern deep architectures, linear (convolution) operators and nonlinear activations, and parameters of each layer can be derived from the principle of rate reduction (and invariance).
  • Running and Analyzing Large-scale Psychology Experiments

    Fri, Mar 6, 2020, 12:00 pm

    Psychology has traditionally been a laboratory discipline, focused on small-scale experiments conducted in person. However, recent technological innovations have made it possible to collect far more data from far more people than ever before. In this talk, I will explore some of the consequences of being able to conduct psychological research at a larger scale, highlighting some of the tools that we have developed for doing so. In particular, I will focus on three recent projects exploring aspects of human decision-making.

  • Preference Modeling with Context-Dependent Salient Features

    Mon, Feb 24, 2020, 4:00 pm

    This talk considers the preference modeling problem and addresses the fact that pairwise comparison data often reflects irrational choice, e.g. intransitivity. Our key observation is that two items compared in isolation from other items may be compared based on only a salient subset of features. Formalizing this idea, I will introduce our proposal for a “salient feature preference model” and discuss sample complexity results for learning the parameters of our model and the underlying ranking with maximum likelihood estimation.

  • Scalable Semidefinite Programming

    Mon, Feb 10, 2020, 4:00 pm
    Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking potential for data science applications. This talk develops new provably correct algorithms for solving large SDP problems by economizing on both the storage and the arithmetic costs. We present two methods: one based on sketching, and the other on complementarity. Numerical evidence shows that these methods are effective for a range of applications, including relaxations of MaxCut, abstract phase retrieval, and quadratic assignment, and can handle SDP instances where the matrix variable has over 10^13 entries.
  • Targeted Machine Learning for Causal Inference

    Fri, Feb 7, 2020, 12:00 pm
    We review targeted minimum loss estimation (TMLE), which provides a general template for the construction of asymptotically efficient plug-in estimators of a target estimand for infinite dimensional models.
  • Wearable Brain-Machine Interface Architectures for Neurocognitive Stress

    Mon, Feb 10, 2020, 4:30 pm
    The human body responds to neurocognitive stress in multiple ways through its autonomic nervous system. Changes in skin conductance measurements indicate sudomotor nerve activity, and could be used in inferring the underlying autonomic nervous system stimulation. We model skin conductance measurements using a state-space model with sparse impulsive events as inputs to the model.


Subscribe to CSML Seminar Series