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Found 12 papers, 4 papers with code
In Defense of the Indefensible: A Very Naive Approach to High-Dimensional Inference
In this paper, we consider a simple and very na\"{i}ve two-step procedure for this task, in which we (i) fit a lasso model in order to obtain a subset of the variables, and (ii) fit a least squares model on the lasso-selected set.
Metric-Optimized Example Weights
Real-world machine learning applications often have complex test metrics, and may have training and test data that are not identically distributed.
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
Distribution Embedding Network for Meta-Learning with Variable-Length Input
We propose Distribution Embedding Network (DEN) for meta-learning, which is designed for applications where both the distribution and the number of features could vary across tasks.
A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method
Data-driven methods for remaining useful life (RUL) prediction normally learn features from a fixed window size of a priori of degradation, which may lead to less accurate prediction results on different datasets because of the variance of local features.
Global Optimization Networks
We consider the problem of estimating a good maximizer of a black-box function given noisy examples.
Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks
We propose Distribution Embedding Networks (DEN) for classification with small data.
Predicting on the Edge: Identifying Where a Larger Model Does Better
Much effort has been devoted to making large and more accurate models, but relatively little has been put into understanding which examples are benefiting from the added complexity.
Multi-view Intent Disentangle Graph Networks for Bundle Recommendation
Specifically, MIDGN disentangles the user's intents from two different perspectives, respectively: 1) In the global level, MIDGN disentangles the user's intent coupled with inter-bundle items; 2) In the Local level, MIDGN disentangles the user's intent coupled with items within each bundle.
Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning
Conversational recommendation systems (CRS) aim to timely and proactively acquire user dynamic preferred attributes through conversations for item recommendation.
Multi-view Hypergraph Contrastive Policy Learning for Conversational Recommendation
Specifically, MHCPL timely chooses useful social information according to the interactive history and builds a dynamic hypergraph with three types of multiplex relations from different views.
Multidimensional Shape Constraints
We propose new multi-input shape constraints across four intuitive categories: complements, diminishers, dominance, and unimodality constraints.
