CSML Seminar Series

  • 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.
  • Exploration by Optimization in Partial Monitoring

    Tue, Nov 12, 2019, 4:30 pm

    In many real-world problems learners cannot directly observe their own rewards but can still infer whether some particular action is successful. How should a learner take actions to balance its need of information while maximizing their reward in this setting? Partial monitoring is a framework introduced a few decades ago to model learning situations of this kind. The simplest version of partial monitoring generalizes learning with full-, bandit-, or even graph-information based feedback.

  • Optimizing for Fairness in ML

    Thu, Nov 7, 2019, 4:30 pm
    Recent events have made evident the fact that algorithms can be discriminatory, reinforce human prejudices, accelerate the spread of misinformation, and are generally not as objective as they are widely thought to be.

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