8 citations · 20 across the 7 of their papers we have counts for
7 papers
Imbalanced Gradients in RL Post-Training of Multi-Task LLMs
Runzhe Wu, Ankur Samanta, Ayush Jain +7
Multi-task post-training of large language models (LLMs) is typically performed by mixing datasets from different tasks and optimizing them jointly. This approach implicitly assume…
Simple Optimizers for Convex Aligned Multi-Objective Optimization
Ben Kretzu, Karen Ullrich, Yonathan Efroni
It is widely recognized in modern machine learning practice that access to a diverse set of tasks can enhance performance across those tasks. This observation suggests that, unlike…
Projection-Free Online Convex Optimization with Time-Varying Constraints
Dan Garber, Ben Kretzu
We consider the setting of online convex optimization with adversarial time-varying constraints in which actions must be feasible w.r.t. a fixed constraint set, and are also requir…
Projection-free Online Exp-concave Optimization
Dan Garber, Ben Kretzu
We consider the setting of online convex optimization (OCO) with \textit{exp-concave} losses. The best regret bound known for this setting is , where is the dimens…
New Projection-free Algorithms for Online Convex Optimization with Adaptive Regret Guarantees
Dan Garber, Ben Kretzu
We present new efficient \textit{projection-free} algorithms for online convex optimization (OCO), where by projection-free we refer to algorithms that avoid computing orthogonal p…
Revisiting Projection-free Online Learning: the Strongly Convex Case
Dan Garber, Ben Kretzu
Projection-free optimization algorithms, which are mostly based on the classical Frank-Wolfe method, have gained significant interest in the machine learning community in recent ye…
Improved Regret Bounds for Projection-free Bandit Convex Optimization
Dan Garber, Ben Kretzu
We revisit the challenge of designing online algorithms for the bandit convex optimization problem (BCO) which are also scalable to high dimensional problems. Hence, we consider al…