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Contributed Talks
in
Workshop: Synthetic Data for Empowering ML Research

Contributed Talks Part 1/2


Abstract:

Papers:

  1. Generating Synthetic Datasets by Interpolating along Generalized Geodesics
  2. Synthetic Clinical Trial Data while Preserving Subject-Level Privacy
  3. Stutter-TTS: Synthetic Generation of Diverse Stuttered Voice Profiles
  4. ReSPack: A Large-Scale Rectilinear Steiner Tree Packing Data Generator and Benchmark
  5. Visual Pre-training for Navigation: What Can We Learn from Noise?
  6. HAPNEST: An efficient tool for generating large-scale genetics datasets from limited training data
  7. Improving dermatology classifiers across populations using images generated by large diffusion models
  8. Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding
  9. Leading by example: Guiding knowledge transfer with adversarial data augmentation
  10. Exploring Biases in Facial Expression Analysis
  11. Noise-Aware Statistical Inference with Differentially Private Synthetic Data
  12. A source data privacy framework for synthetic clinical trial data
  13. Approaches to Optimizing Medical Treatment Policy using Temporal Causal Model-Based Simulation
  14. Entity-Controlled Synthetic Text Generation using Contextual Question and Answering with Pre-trained Language Models
  15. Distributional Privacy for Data Sharing
  16. Vine Copula Based Data Generation for Machine Learning With an Application to Industrial Processes
  17. Systematic review of effect of data augmentation using paraphrasing on Named entity recognition
  18. PRISIM: Privacy Preserving Synthetic Data Simulator
  19. Fair Synthetic Data Does not Necessarily Lead to Fair Models
  20. FARE: Provably Fair Representation Learning

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