16 citations · 61 across the 37 of their papers we have counts for
10 papers · 1 filter
The Reward Was in Your Data All Along: Correcting Flow Matching with Discriminator-Guided RL
Nicolas Beltran-Velez, Felix Friedrich, Zhang Xiaofeng +4
Score- and flow-matching models often rely on preference-based reinforcement learning for two purposes: aligning with subjective preferences and, surprisingly, recovering propertie…
Grounding Computer Use Agents on Human Demonstrations
Aarash Feizi, Shravan Nayak, Xiangru Jian +14
Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen elements. While large datasets exist for web…
Improving the Scaling Laws of Synthetic Data with Deliberate Practice
Reyhane Askari-Hemmat, Mohammad Pezeshki, Elvis Dohmatob +6
Inspired by the principle of deliberate practice in human learning, we propose Deliberate Practice for Synthetic Data Generation (DP), a novel framework that improves sample effici…
PairBench: Are Vision-Language Models Reliable at Comparing What They See?
Aarash Feizi, Sai Rajeswar, Adriana Romero-Soriano +4
Understanding how effectively large vision language models (VLMs) compare visual inputs is crucial across numerous applications, yet this fundamental capability remains insufficien…
DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning
Jonathan Lebensold, Maziar Sanjabi, Pietro Astolfi +4
Text-to-image diffusion models have been shown to suffer from sample-level memorization, possibly reproducing near-perfect replica of images that they are trained on, which may be…
Graph Inductive Biases in Transformers without Message Passing
Liheng Ma, Chen Lin, Derek Lim +5
Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works…
Learning to Substitute Ingredients in Recipes
Bahare Fatemi, Quentin Duval, Rohit Girdhar +2
Recipe personalization through ingredient substitution has the potential to help people meet their dietary needs and preferences, avoid potential allergens, and ease culinary explo…
On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects
Sumana Basu, Marc-André Legault, Adriana Romero-Soriano +1
Drug dosing is an important application of AI, which can be formulated as a Reinforcement Learning (RL) problem. In this paper, we identify two major challenges of using RL for dru…
Parameter Prediction for Unseen Deep Architectures
Boris Knyazev, Michal Drozdzal, Graham W. Taylor +1
Deep learning has been successful in automating the design of features in machine learning pipelines. However, the algorithms optimizing neural network parameters remain largely ha…
Graph Attention Networks with Positional Embeddings
Liheng Ma, Reihaneh Rabbany, Adriana Romero-Soriano
Graph Neural Networks (GNNs) are deep learning methods which provide the current state of the art performance in node classification tasks. GNNs often assume homophily -- neighbori…