Instructional Material
Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation
Although the population (i.e, infinite-particle) limit dynamics of SVGD is well characterized, its behavior in the finite-particle regime is far less understood.
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
Easily integrated, FamO2O statistically enhances existing algorithms' performance.
A Evaluation Tasks 450 Table 4 lists the tasks in our evaluation, and examples from each task
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Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies Oscar Li1, James Harrison, Jascha Sohl-Dickstein, Virginia Smith
First-order optimization methods are a foundational tool in machine learning.
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies Oscar Li1, James Harrison, Jascha Sohl-Dickstein, Virginia Smith
First-order optimization methods are a foundational tool in machine learning.
A TT A: Anomaly-aware Test-Time Adaptation for Out-of-Distribution Detection in Segmentation
We quantify the drop in PEBAL's performance with the added domain shift and compare it to the performance when combined with our method or existing test-time adaptation methods such as
PT ADisc: A Cross-Course Dataset Supporting Personalized Learning in Cold-Start Scenarios
The focus of our work is on diagnostic tasks in personalized learning, such as cognitive diagnosis and knowledge tracing. The goal of these tasks is to assess students'
