Automated Scalable Bayesian Inference via Data Summarization - 2018
Explore scalable Bayesian inference techniques using data summarization. Learn about coresets, theoretical guarantees, and practical applications in large-scale data settings.
A Bayesian View of Inductive Learning in Humans and Machines - 2004
Explore how humans make successful generalizations from limited evidence through Bayesian inferences constrained by implicit domain theories, with applications to machine learning.
The Signal and the Noise by Nate Silver - Part 2
Explore Nate Silver's insights on data analysis and prediction in "The Signal and the Noise" through an expert review of key concepts and applications.
Bayesian Inference of Causal Graphs: Current Status and Future Directions
Explore Bayesian inference for causal discovery, examining recent progress, challenges, and assumptions in modeling cause-effect relationships from observational data.
UAI 2023 Poster Spotlights - Advanced Topics in Uncertainty and AI
Explore cutting-edge research in uncertainty and AI, covering topics from point processes and GNN spatial convolutions to variational inference and Bayesian approaches in reinforcement learning.
Statistical Physics and Statistical Inference - Lecture 2
Explore statistical physics and inference, covering Bayesian methods, error correction, spin glasses, and advanced algorithms like belief propagation. Gain insights into phase transitions and applications in machine learning.
Gravitational Wave Parameter Estimation with Compressed Likelihood Evaluations
Explore advanced techniques for efficient gravitational wave parameter estimation, focusing on Reduced Order Quadratures (ROQs) to accelerate likelihood evaluations and improve Bayesian inference in GW astronomy.
Bayesian Field-Level Inference with Weak Lensing Data by Supranta Boruah
Explore Bayesian field-level inference techniques for weak lensing data analysis in cosmology. Learn advanced methods to extract valuable information from large-scale structure surveys.
Challenge of Characterising High-Mass Compact Mergers - The Case of GW190521
Explores challenges in analyzing gravitational waves from massive binary mergers, focusing on GW190521. Discusses Bayesian inference, prior choices, and alternative interpretations like eccentric mergers or exotic objects.
Decision Making and Learning in the Life Sciences - Lecture 2
Explore decision-making and learning in life sciences, focusing on advanced concepts and their applications in biological systems and research.
Learning Battery Physics from Images
Explore advanced techniques for extracting battery physics from synchrotron imaging data, combining electrochemical models with PDE-constrained optimization and Bayesian inference to uncover hidden information.
Application of Particle Filters on Data Assimilation
Explore particle filters in data assimilation, a powerful technique for integrating observations and forecasts to estimate atmospheric states and uncertainties in weather and climate modeling.
Google's Dual-Agent AI Architecture: Thinking Fast and Slow
Explore Google's innovative dual-agent AI system that combines fast, intuitive responses with deep, deliberate reasoning for enhanced human-AI interactions and complex problem-solving.
Bayesian Inference Using Probabilistic Programming
Explore Bayesian inference techniques and probabilistic programming concepts with expert insights from Hong Ge at LAFI'24.
Formal Verification and Learning of Complex Systems - Professor Alessandro Abate
Exploring formal verification techniques for complex systems, combining model-based and data-driven methods to address scalability issues and provide system-level assertions in Cyber-Physical Systems.
The Hamiltonian Monte Carlo Revolution Is Open Source - Probabilistic Programming with PyMC3
Explore Bayesian statistical computation and Hamiltonian Monte Carlo algorithms using PyMC3, an open-source probabilistic programming package. Learn to apply these advanced techniques to complex models in Python.
Dreamcoder- Bootstrapping Inductive Program Synthesis With Wake-Sleep Library Learning
Explore program induction and library learning in AI, focusing on DreamCoder's approach to synthesizing models and systems through neural-guided Bayesian inference and abstraction refinement.
Computation in Very Wide Neural Networks
Explore infinite-width neural networks, their correspondence to Gaussian processes, and implications for deep learning performance and dynamics in parameter space.
Methods for Scalable Probabilistic Inference - IPAM at UCLA
Explore scalable probabilistic inference methods for astrophysics, focusing on Gaussian Processes for time series analysis and open-source tools to accelerate Bayesian inference with large datasets.
Learning What We Know and Knowing What We Learn - Gaussian Process Priors for Neural Data Analysis
Explore Gaussian process priors for neural data analysis, covering latent variable models, Bayesian inference, covariance kernels, and factor analysis. Gain insights into advanced techniques for understanding complex neural datasets.
Data is as Data Does - The Influence of Computation on Inference
Exploring the impact of computational approximations on probabilistic models in machine learning, with focus on Gaussian Process approximations and their applications in neurobiology.