21 citations · 49 across the 24 of their papers we have counts for
8 papers · 1 filter
ABC: Advantage-Based Control Variates for Reinforcement Learning with Verifiable Rewards
Hsiao-Ru Pan, Florent Draye, Bernhard Schölkopf
Recent progress in reinforcement learning with verifiable rewards (RLVR) has highlighted the effectiveness of simple critic-free policy-gradient methods such as Group Relative Poli…
LoRA and Privacy: When Random Projections Help (and When They Don't)
Yaxi Hu, Johanna Düngler, Bernhard Schölkopf +1
We introduce the (Wishart) projection mechanism, a randomized map of the form with and study its differential privacy properties. For ve…
Online Learning and Unlearning
Yaxi Hu, Bernhard Schölkopf, Amartya Sanyal
We formalize the problem of online learning-unlearning, where a model is updated sequentially in an online setting while accommodating unlearning requests between updates. After a…
Causality can systematically address the monsters under the bench(marks)
Felix Leeb, Zhijing Jin, Bernhard Schölkopf
Effective and reliable evaluation is essential for advancing empirical machine learning. However, the increasing accessibility of generalist models and the progress towards ever mo…
MathGAP: Out-of-Distribution Evaluation on Problems with Arbitrarily Complex Proofs
Andreas Opedal, Haruki Shirakami, Bernhard Schölkopf +2
Large language models (LLMs) can solve arithmetic word problems with high accuracy, but little is known about how well they generalize to more complex problems. This is difficult t…
Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation
Amartya Sanyal, Yaxi Hu, Yaodong Yu +3
"Accuracy-on-the-line" is a widely observed phenomenon in machine learning, where a model's accuracy on in-distribution (ID) and out-of-distribution (OOD) data is positively correl…
Skill or Luck? Return Decomposition via Advantage Functions
Hsiao-Ru Pan, Bernhard Schölkopf
Learning from off-policy data is essential for sample-efficient reinforcement learning. In the present work, we build on the insight that the advantage function can be understood a…
Investigating the Impact of Action Representations in Policy Gradient Algorithms
Jan Schneider, Pierre Schumacher, Daniel Häufle +2
Reinforcement learning~(RL) is a versatile framework for learning to solve complex real-world tasks. However, influences on the learning performance of RL algorithms are often poor…