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July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. -
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July 9, 202610 min read
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Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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AISTATS 20232023We revisit the problem of fair principal component analysis (PCA), where the goal is to learn the best low-rank linear approximation of the data that obfuscates demographic information. We propose a conceptually simple approach that allows for an analytic solution similar to standard PCA and can be kernelized. Our methods have the same complexity as standard PCA, or kernel PCA, and run much faster than
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AAAI 2023 Spring Symposium Series2023This paper describes the development of algorithms that decide when to move, where to move, and how to look for people in a home environment. We introduce a design framework that defines the design principles, key decision points, and technical approaches for a social robot to proactively be with people for companionship and assistance in the home. Through a series of evaluations ranging from simulations
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ICRA 20232023This paper introduces a deep learning (DL) approach to predicting congestion delays in large multi-robot systems. The problem is motivated by real-world problems in modern logistics automation, such as a warehouse with hundreds to thousands of coordinated mobile robots. Here, the large scale, the complexity of the control software, and the uncertainties of the robots’ dynamics make direct (simulated) prediction
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ICASSP 20232023In this study, we present an approach to train a single speech enhancement network that can perform both personalized and nonpersonalized speech enhancement. This is achieved by incorporating a frame-wise conditioning input that specifies the type of enhancement output. To improve the quality of the enhanced output and mitigate oversuppression, we experiment with re-weighting frames by the presence or absence
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ICLR 20232023Recent vision transformer based video models mostly follow the “image pretraining then finetuning” paradigm and have achieved great success on multiple video benchmarks. However, full finetuning such a video model could be computationally expensive and unnecessary, given the pre-trained image transformer models have demonstrated exceptional transferability. In this work, we propose a novel method to Adapt
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Gretchen Ertl
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