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Found 7 papers, 4 papers with code
Benefits of Transformer: In-Context Learning in Linear Regression Tasks with Unstructured Data
In practice, it is observed that transformer-based models can learn concepts in context in the inference stage.
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective
Pre-training is known to generate universal representations for downstream tasks in large-scale deep learning such as large language models.
AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing
Diffusion model has become a main paradigm for synthetic data generation in many subfields of modern machine learning, including computer vision, language model, or speech synthesis.
Leveraging Untrustworthy Commands for Multi-Robot Coordination in Unpredictable Environments: A Bandit Submodular Maximization Approach
MetaBSG leverages a meta-algorithm to learn whether the robots should follow the commands or a recently developed submodular coordination algorithm, Bandit Sequential Greedy (BSG) [1], which has performance guarantees even in unpredictable and partially-observable environments.
Estimating Treatment Effects Under Heterogeneous Interference
One popular application of this estimation lies in the prediction of the impact of a treatment (e. g., a promotion) on an outcome (e. g., sales) of a particular unit (e. g., an item), known as the individual treatment effect (ITE).
Bandit Submodular Maximization for Multi-Robot Coordination in Unpredictable and Partially Observable Environments
We are motivated by the future of autonomy that involves multiple robots coordinating actions in dynamic, unstructured, and partially observable environments to complete complex tasks such as target tracking, environmental mapping, and area monitoring.
FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification
Existing pruning techniques preserve deep neural networks' overall ability to make correct predictions but may also amplify hidden biases during the compression process.
