Learning Graphical Models
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
In a slight abuse of notation, we use π to denote both the target distribution and its density.
Principled Bayesian Optimisation in Collaboration with Human Experts
We consider a setup where experts provide advice on the next query point through binary accept/reject recommendations (labels).
OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators
Unfortunately, choosing the best OPE algorithm for each task and domain is still unclear.
Integrating Suboptimal Human Knowledge with Hierarchical Reinforcement Learning for Large-Scale Multiagent Systems
While agents' learning is data-driven, sampling from millions
From Causal to Concept-Based Representation Learning
A key question in this growing field is to provide rigorous conditions under which latent factors can be identified and thus, potentially learned.
HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection
Data serves as the fundamental basis for advancing deep learning.
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data
Kai Helli, David Schnurr, Noah Hollmann, Samuel Müller, Frank Hutter
Until now, no tabular method has consistently outperformed classical supervised learning, which ignores these shifts.
Stopping Bayesian Optimization with Probabilistic Regret Bounds
Monte Carlo stopping rules in a manner that is both sample efficient and robust to estimation error.
Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors
Recent advancements in solving Bayesian inverse problems have spotlighted de-noising diffusion models (DDMs) as effective priors. Although these have great potential, DDM priors yield complex posterior distributions that are challenging to sample.
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning Qi Wang 1, 2 Junming Y ang 3 Y unbo Wang
Training offline RL models using visual inputs poses two significant challenges, i.e.,