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Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
Geneviève Robin, Hoi-To Wai, Julie Josse, Olga Klopp, Eric Moulines
In this paper, we introduce alowrank interaction and sparse additive effects(LORIS) model which combines matrix regression on a dictionary and low-rank design, to estimate main effects andinteractions simultaneously.
Multiclass Learning from Contradictions
Sauptik Dhar, Vladimir Cherkassky, Mohak Shah
Neural Information Processing Systems http://nips.cc/
Efficient Potential-based Exploration in Reinforcement Learning using Inverse Dynamic Bisimulation Metric
While a number of RL methods have been proposed to boost exploration by designing an intrinsic reward signal as exploration bonus.
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters
W. O. K. Asiri Suranga Wijesinghe, Qing Wang
Neural Information Processing Systems http://nips.cc/
Proximal Graphical Event Models
Debarun Bhattacharjya, Dharmashankar Subramanian, Tian Gao
Neural Information Processing Systems http://nips.cc/
EAI: Emotional Decision-Making of LLMs in Strategic Games and Ethical Dilemmas
We introduce the novel EAI framework for integrating emotion modeling into LLMs to examine the emotional impact on ethics and LLM-based decision-making in various strategic games, including bargaining and repeated games. Our experimental study with various LLMs demonstrated that emotions can significantly alter the ethical decision-making landscape of LLMs, highlighting the need for robust mechanisms to ensure consistent ethical standards. Our game-theoretic analysis revealed that LLMs are susceptible to emotional biases influenced by model size, alignment strategies, and primary pretraining language. Notably, these biases often diverge from typical human emotional responses, occasionally leading to unexpected drops in cooperation rates, even under positive emotional influence.
GEQ: Gaussian Kernel Inspired Equilibrium Models
Moreover, GEQ can be perceived as a weight-tied neural network with infinite width and depth. GEQ also enjoys better theoretical properties and improved overall performance.
The continuous Bernoulli: fixing a pervasive error in variational autoencoders
Gabriel Loaiza-Ganem, John P. Cunningham
Neural Information Processing Systems http://nips.cc/
Learning latent variable structured prediction models with Gaussian perturbations
Neural Information Processing Systems http://nips.cc/
Diffusion-based ReinforcementLearningvia Q-weightedVariationalPolicyOptimization
UnlikeGaussian policies, the log-likelihood indiffusion policies isinaccessible; thus this entropy term is nontrivial. Moreover, to reduce the large variance of diffusion policies, we also develop an efficient behavior policy through action selection. This can further improve its sample efficiency during online interaction.