Bayesian Inference
Scalable Deep Generative Relational Model with High-Order Node Dependence
Xuhui Fan, Bin Li, Caoyuan Li, Scott SIsson, Ling Chen
Neural Information Processing Systems http://nips.cc/
Distributionally Robust Graphical Models
Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian Ziebart
Neural Information Processing Systems http://nips.cc/
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation
Chaitanya Ryali, Gautam Reddy, Angela J. Yu
Neural Information Processing Systems http://nips.cc/
Overlapping Clustering Models, and One (class) SVM to Bind Them All
Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti
Neural Information Processing Systems http://nips.cc/
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes
Jun Yang, Shengyang Sun, Daniel M. Roy
The developments of Rademacher complexity and PAC-Bayesian theory have been largely independent.
A Bayes-Sard Cubature Method
Toni Karvonen, Chris J. Oates, Simo Sarkka
Neural Information Processing Systems http://nips.cc/
Empirical Gaussian Processes
Lin, Jihao Andreas, Ament, Sebastian, Tiao, Louis C., Eriksson, David, Balandat, Maximilian, Bakshy, Eytan
Gaussian processes (GPs) are powerful and widely used probabilistic regression models, but their effectiveness in practice is often limited by the choice of kernel function. This kernel function is typically handcrafted from a small set of standard functions, a process that requires expert knowledge, results in limited adaptivity to data, and imposes strong assumptions on the hypothesis space. We study Empirical GPs, a principled framework for constructing flexible, data-driven GP priors that overcome these limitations. Rather than relying on standard parametric kernels, we estimate the mean and covariance functions empirically from a corpus of historical observations, enabling the prior to reflect rich, non-trivial covariance structures present in the data. Theoretically, we show that the resulting model converges to the GP that is closest (in KL-divergence sense) to the real data generating process. Practically, we formulate the problem of learning the GP prior from independent datasets as likelihood estimation and derive an Expectation-Maximization algorithm with closed-form updates, allowing the model handle heterogeneous observation locations across datasets. We demonstrate that Empirical GPs achieve competitive performance on learning curve extrapolation and time series forecasting benchmarks.
Overleaf Example
Large transformer-based foundation models have been commonly used as pre-trained models that can be adapted to different challenging datasets and settings with state-of-the-art generalization performance.
