Search Results for author:
Found 8 papers, 3 papers with code
Investigating the Histogram Loss in Regression
It is becoming increasingly common in regression to train neural networks that model the entire distribution even if only the mean is required for prediction.
Label Alignment Regularization for Distribution Shift
Recent work has highlighted the label alignment property (LAP) in supervised learning, where the vector of all labels in the dataset is mostly in the span of the top few singular vectors of the data matrix.
Representation Alignment in Neural Networks
We then highlight why alignment between the top singular vectors and the targets can speed up learning and show in a classic synthetic transfer problem that representation alignment correlates with positive and negative transfer to similar and dissimilar tasks.
Off-Policy Actor-Critic with Emphatic Weightings
A variety of theoretically-sound policy gradient algorithms exist for the on-policy setting due to the policy gradient theorem, which provides a simplified form for the gradient.
Hallucinating Value: A Pitfall of Dyna-style Planning with Imperfect Environment Models
Dyna-style reinforcement learning (RL) agents improve sample efficiency over model-free RL agents by updating the value function with simulated experience generated by an environment model.
An implicit function learning approach for parametric modal regression
We empirically demonstrate on several synthetic problems that our method (i) can learn multi-valued functions and produce the conditional modes, (ii) scales well to high-dimensional inputs, and (iii) can even be more effective for certain uni-modal problems, particularly for high-frequency functions.
An Off-policy Policy Gradient Theorem Using Emphatic Weightings
There have been a host of theoretically sound algorithms proposed for the on-policy setting, due to the existence of the policy gradient theorem which provides a simplified form for the gradient.
Improving Regression Performance with Distributional Losses
We provide theoretical support for this alternative hypothesis, by characterizing the norm of the gradients of this loss.
