70 citations · 129 across the 10 of their papers we have counts for
12 papers
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…
Direct Advantage Estimation for Scalable and Sample-efficient Deep Reinforcement Learning
Hsiao-Ru Pan, Bernhard Schölkopf
Direct Advantage Estimation (DAE) has been shown to improve the sample efficiency of deep reinforcement learning algorithms. However, its reliance on full environment observability…
On the Variance of Temporal Difference Learning and its Reduction Using Control Variates
Hsiao-Ru Pan, Bernhard Schölkopf
We analyze the variance of temporal difference (TD) learning using the phased setting with tabular representation, and show that one of the mechanisms behind its ability to reduce…
Intrinsically Interpretable Attention via Sparse Post-Training
Florent Draye, Anson Lei, Hsiao-Ru Pan +2
We introduce a simple post-training method that makes transformer attention sparse without sacrificing performance. Applying a flexible sparsity regularisation under a constrained-…
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…
Homomorphism Autoencoder -- Learning Group Structured Representations from Observed Transitions
Hamza Keurti, Hsiao-Ru Pan, Michel Besserve +2
How can agents learn internal models that veridically represent interactions with the real world is a largely open question. As machine learning is moving towards representations c…
Damping signatures at JUNO, a medium-baseline reactor neutrino oscillation experiment
JUNO collaboration, Jun Wang, Jiajun Liao +598
We study damping signatures at the Jiangmen Underground Neutrino Observatory (JUNO), a medium-baseline reactor neutrino oscillation experiment. These damping signatures are motivat…
Direct Advantage Estimation
Hsiao-Ru Pan, Nico Gürtler, Alexander Neitz +1
The predominant approach in reinforcement learning is to assign credit to actions based on the expected return. However, we show that the return may depend on the policy in a way w…
Radioactivity control strategy for the JUNO detector
JUNO collaboration, Angel Abusleme, Thomas Adam +594
JUNO is a massive liquid scintillator detector with a primary scientific goal of determining the neutrino mass ordering by studying the oscillated anti-neutrino flux coming from tw…
JUNO Physics and Detector
JUNO Collaboration, Angel Abusleme, Thomas Adam +607
The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kton LS detector at 700-m underground. An excellent energy resolution and a large fiducial volume offer exciting opport…
The Design and Sensitivity of JUNO's scintillator radiopurity pre-detector OSIRIS
JUNO Collaboration, Angel Abusleme, Thomas Adam +598
The OSIRIS detector is a subsystem of the liquid scintillator fillling chain of the JUNO reactor neutrino experiment. Its purpose is to validate the radiopurity of the scintillator…
TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution
JUNO Collaboration, Angel Abusleme, Thomas Adam +585
The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A ton-level liquid scintillato…