location: Researchers' Link
Efficient Compressed Sensing with L0 Projections
Many applications concern sparse signals, for example, detecting anomalies from the differences between consecutive images taken by surveillance cameras. In general, anomaly events are sparse. This talk focuses on the problem of recovering a K-sparse signal in N dimensions (coordinates). Classical theories in compressed sensing say the required number of …
Emotions in Engineering: Methods for the Interpretation of Ambiguous Emotional Content
Emotion has intrigued researchers for generations. This fascination has permeated the engineering community, motivating the development of affective computational models for classification. However, human emotion remains notoriously difficult to interpret both because of the mismatch between the emotional cue generation (the speaker) and cue perception (the observer) processes and because …
Iterative Collaborative Filtering for Sparse Noisy Tensor Estimation
We present a generalization of the collaborative filtering algorithm for the task of tensor estimation, i.e. estimating a low-rank 3-order n-by-n-by-n tensor from noisy observations of randomly chosen entries in the sparse regime. Not only does the algorithm have desirable computational properties, it also provably achieves sample complexity that (nearly) …
Adaptive Experimental Design for Multiple Testing and Best Identification
Adaptive experimental design (AED), or active learning, leverages already-collected data to guide future measurements, in a closed loop, to collect the most informative data for the learning problem at hand. In both theory and practice, AED can extract considerably richer insights than any measurement plan fixed in advance, using the …
Towards a Theoretical Understanding of Inverse Problems with Neural Priors
Inverse problems of various flavors span all of science, engineering, and design. Over the last five years, approaches based on neural networks have emerged as the tool of choice for solving such problems. However, a clear theoretical understanding of how well such approaches perform — together with quantitative sample-complexity and …
Learning with scarce data: The role of side information, simulators, and GANs
In this talk, I will present the role of side information, simulators, and GANs for learning with scarce data. In the first part, I will talk about the role of similarity graphs in recommendation systems. In the second part, the role of simulators and GANs will be discussed.
Learning from multiple biased sources
When high-quality labeled training data are unavailable, an alternative is to learn from training sources that are biased in some way. This talk will cover my group’s recent work on three problems where a learner has access to multiple biased sources. First, we consider the problem of classification given multiple …