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Exploring counterfactual prediction in AI: enhancing prediction algorithms with 'what if' capabilities for improved decision-making and fairness in healthcare, insurance, and beyond.
Explore challenges and solutions for online safety, AI moderation, and disinformation in the digital age with experts discussing regulation, privacy, and social media's role.
Exploring ethical challenges in using data and technology for COVID-19 response, balancing public health benefits with privacy concerns and trust in government interventions.
Explore how digital platforms and machine learning can enhance citizen engagement in policy-making, addressing challenges and opportunities in direct democracy initiatives.
Explore neural opinion dynamics for predicting user stance shifts in digital democracy platforms, enhancing citizen engagement and policy-making through machine learning techniques.
Explores ethical considerations and practical principles for implementing machine learning in children's social care, addressing risks, data quality, and strategies to improve outcomes for families.
Explore efficient cross-validation techniques for large datasets using linear approximation and dimensionality reduction. Learn about error bounds, high-dimensional challenges, and practical applications in machine learning.
Explore benign overfitting in machine learning, focusing on linear regression, deep networks, and statistical implications. Gain insights into effective rank, regularization, and future research directions.
Explore function spaces of overparameterized neural networks, focusing on weight-bounded networks and their approximation capabilities. Insights from Radon transform analysis reveal novel perspectives on learning with ReLU networks.
Explore differentiable approaches to integrate permutations, sorting, and ranking in machine learning, focusing on embedding techniques and relaxation of ranking operators.
Exploring global convergence of gradient descent in non-convex optimization for deep learning, highlighting challenges in bridging theory and practice in machine learning algorithms.
Exploring causal inference, autoencoders, and gene regulation through genomics and 3D genome organization. Integrates data modalities, analyzes overparameterized autoencoders, and links memorization to real-valued data retrieval.
Explore low-rank tensor regression using importance sketching for efficient high-dimensional data analysis, with theoretical guarantees and practical applications in neuroimaging.
Explore big data's low-rank structure, generalized low-rank models, and their applications in machine learning. Learn about dimensionality reduction, autoML, and latent variable models for efficient data analysis.
Exploring the necessity of memorization in learning algorithms, this talk delves into privacy concerns, long-tail distributions, and the role of memorization in achieving optimal generalization error in deep learning.
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