Learning Graphical Models
Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy Cameron Allen UC Berkeley Aaron Kirtland Brown University Ruo Y u Tao
These authors contributed equally and are ordered alphabetically.
Induced Model Matching: Restricted Models Help Train Full-Featured Models
This restricted model may be thought of as "side-information", and can come
Anormativetheoryofsocialconflict
Social conflict is a survival mechanism yielding both normal and pathological behaviors. Tounderstand its underlying principles, we collected behavioral and whole-brain neural data from mice advancing through stages of social conflict.
Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions
A notable example that motivates our work is the problem of minimizing costs (or maximizing rewards) in a single-trajectory Markov Decision Process (MDP).
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
We propose a novel loss function called Energy Discrepancy (ED) which does not rely on the computation of scores or expensive Markov chain Monte Carlo.
Robust Gaussian Processes via Relevance Pursuit
We compare the model's performance relative to other approaches on diverse regression and Bayesian optimization tasks, including the challenging but common
Detecting and Measuring Confounding Using Causal Mechanism Shifts
Existing methods frequently assume causal sufficiency, disregarding the presence of unobserved confounding variables.
