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Found 5 papers, 2 papers with code
Adaptive Preference Scaling for Reinforcement Learning with Human Feedback
By incorporating an adaptive scaling parameter into the loss for each pair, our method increases the flexibility of the reward function.
SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process
We conduct extensive experiments in both event type prediction and uncertainty quantification of arrival time.
Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification
Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of marked STPPs through score-matching and offers uncertainty quantification for the predicted event time, location and mark by computing confidence regions over the generated samples.
Stochastic Inexact Augmented Lagrangian Method for Nonconvex Expectation Constrained Optimization
In this paper, we design and analyze stochastic inexact augmented Lagrangian methods (Stoc-iALM) to solve problems involving a nonconvex composite (i. e. smooth+nonsmooth) objective and nonconvex smooth functional constraints.
Transformer Hawkes Process
Modern data acquisition routinely produce massive amounts of event sequence data in various domains, such as social media, healthcare, and financial markets.
