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07/06/2023
Optimal Scalarizations for Sublinear Hypervolume Regret
Scalarization is a general technique that can be deployed in any multiob...
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07/05/2023
Set Learning for Accurate and Calibrated Models
Model overconfidence and poor calibration are common in machine learning...
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04/14/2023
Robust Algorithms on Adaptive Inputs from Bounded Adversaries
We study dynamic algorithms robust to adaptive input generated from sour...
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09/30/2022
Optimal Query Complexities for Dynamic Trace Estimation
We consider the problem of minimizing the number of matrix-vector querie...
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05/26/2022
Towards Learning Universal Hyperparameter Optimizers with Transformers
Meta-learning hyperparameter optimization (HPO) algorithms from prior ex...
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03/29/2021
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks
Can deep learning solve multiple tasks simultaneously, even when they ar...
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05/15/2020
Learning the gravitational force law and other analytic functions
Large neural network models have been successful in learning functions o...
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02/19/2020
Span Recovery for Deep Neural Networks with Applications to Input Obfuscation
The tremendous success of deep neural networks has motivated the need to...
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05/11/2019
Solving Empirical Risk Minimization in the Current Matrix Multiplication Time
Many convex problems in machine learning and computer science share the ...
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11/02/2018