Yichen Zhou
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Motivated by recent advances in large language models for NLP, we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of datasets, matches the accuracy of state-of-the-art supervised forecasting models for each individual dataset. Our model is based on pretraining a patched-decoder style attention model on a large time series dataset, and can work well across different forecasting history lengths, prediction lengths and temporal granularities.
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In machine learning applications such as ranking fairness or fairness over intersectional groups, one often encounters optimization problems with an extremely large number of constraints. In particular, with ranking fairness tasks, there may even be a variable number of constraints, e.g. one for each query in the training set. In these cases, the standard approach of optimizing a Lagrangian while maintaining one Lagrange multiplier per constraint may no longer be practical. Our proposal is to associate a feature vector with each constraint, and to learn a "multiplier model" that maps each such vector to the corresponding Lagrange multiplier. We prove optimality and feasibility guarantees under assumptions on the flexibility of the multiplier model, and empirically demonstrate that our method is effective on real-world case studies.
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