Selected Publications
Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
We introduce rewarded soup, a new strategy to trade-off between multiple rewards when fine-tuning foundation models with RLHF; we first learn one network for each reward, and then linearly interpolate their weights.
Beyond task performance: evaluating and reducing the flaws of large multimodal models with in-context-learning
We investigate large multimodal models and their limitations such as hallucinations and lack of explainability. We then show that multimodal in-context learning can reduce some of these flaws.
Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization
We introduce and motivate a new regularization that enforces invariance in the domain-level gradient variances across the different training domains in order to improve out-of-distribution generalization.
MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks
We introduce a new generalized framework for learning multi-input multi-output subnetworks and study how to best mix the inputs. We obtain sota on CIFAR and Tiny ImageNet by better leveraging the expressiveness of large networks.
DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation
Driven by arguments from information theory, we introduce a new learning strategy for deep ensembles that increases diversity among members: we adversarially prevent features from being conditionally redundant.