activity
20212024
most citedInvariant Causal Imitation Learning for Generalizable Policies

10 citations · 20 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20241 cited

Improving fine-grained understanding in image-text pre-training

Ioana Bica, Anastasija Ilić, Matthias Bauer +8

We introduce SPARse Fine-grained Contrastive Alignment (SPARC), a simple method for pretraining more fine-grained multimodal representations from image-text pairs. Given that multi…

stat.ML202310 cited

Invariant Causal Imitation Learning for Generalizable Policies

Ioana Bica, Daniel Jarrett, Mihaela van der Schaar

Consider learning an imitation policy on the basis of demonstrated behavior from multiple environments, with an eye towards deployment in an unseen environment. Since the observabl…

stat.ML20237 cited

Time-series Generation by Contrastive Imitation

Daniel Jarrett, Ioana Bica, Mihaela van der Schaar

Consider learning a generative model for time-series data. The sequential setting poses a unique challenge: Not only should the generator capture the conditional dynamics of (stepw…

cs.LG2022

DAPDAG: Domain Adaptation via Perturbed DAG Reconstruction

Yanke Li, Hatt Tobias, Ioana Bica +1

Leveraging labelled data from multiple domains to enable prediction in another domain without labels is a significant, yet challenging problem. To address this problem, we introduc…

cs.LG20212 cited

Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time

Jeroen Berrevoets, Alicia Curth, Ioana Bica +2

Choosing the best treatment-plan for each individual patient requires accurate forecasts of their outcome trajectories as a function of the treatment, over time. While large observ…