Tootfinder

Q-Learning with Shift-Aware Upper Confidence Bound in Non-Stationary Reinforcement Learning
Ha Manh Bui, Felix Parker, Kimia Ghobadi, Anqi Liu
https://arxiv.org/abs/2510.03181 h…

Q-Learning with Shift-Aware Upper Confidence Bound in Non-Stationary Reinforcement Learning
We study the Non-Stationary Reinforcement Learning (RL) under distribution shifts in both finite-horizon episodic and infinite-horizon discounted Markov Decision Processes (MDPs). In the finite-horizon case, the transition functions may suddenly change at a particular episode. In the infinite-horizon setting, such changes can occur at an arbitrary time step during the agent's interaction with the environment. While the Q-learning Upper Confidence Bound algorithm (QUCB) can discover a proper pol…

Ethical Considerations Around Machine Learning-Engaged Online Participatory Research - poster from Zooniverse community at #FF2025 https://zenodo.org/records/17779992

InsideOut: An EfficientNetV2-S Based Deep Learning Framework for Robust Multi-Class Facial Emotion Recognition
Ahsan Farabi, Israt Khandaker, Ibrahim Khalil Shanto, Md Abdul Ahad Minhaz, Tanisha Zaman
https://arxiv.org/abs/2510.03066

InsideOut: An EfficientNetV2-S Based Deep Learning Framework for Robust Multi-Class Facial Emotion Recognition
Facial Emotion Recognition (FER) is a key task in affective computing, enabling applications in human-computer interaction, e-learning, healthcare, and safety systems. Despite advances in deep learning, FER remains challenging due to occlusions, illumination and pose variations, subtle intra-class differences, and dataset imbalance that hinders recognition of minority emotions. We present InsideOut, a reproducible FER framework built on EfficientNetV2-S with transfer learning, strong data augme…

I'm astonished to see the stats of my LinkedIn Learning course on OpenShift… and to discover that almost 14'000 students worldwide took it in the past 2 years 😲
https://www.linkedin.com/learning/learning-openshift/

Learning OpenShift Online Class | LinkedIn Learning, formerly Lynda.com
Learn the skills you need to know as a Kubernetes developer to start working with OpenShift 4 clusters.

Consolidating Reinforcement Learning for Multimodal Discrete Diffusion Models
Tianren Ma, Mu Zhang, Yibing Wang, Qixiang Ye
https://arxiv.org/abs/2510.02880 https://

Consolidating Reinforcement Learning for Multimodal Discrete Diffusion Models
Optimizing discrete diffusion model (DDM) with rewards remains a challenge: the non-autoregressive paradigm makes importance sampling intractable and rollout complex, puzzling reinforcement learning methods such as Group Relative Policy Optimization (GRPO). In this study, we introduce MaskGRPO, the first viable approach to enable scalable multimodal reinforcement learning in discrete diffusion with effective importance sampling and modality-specific adaptations. To this end, we first clarify th…

An Investigation into the Performance of Non-Contrastive Self-Supervised Learning Methods for Network Intrusion Detection
Hamed Fard, Tobias Schalau, Gerhard Wunder
https://arxiv.org/abs/2510.02349

An Investigation into the Performance of Non-Contrastive Self-Supervised Learning Methods for Network Intrusion Detection
Network intrusion detection, a well-explored cybersecurity field, has predominantly relied on supervised learning algorithms in the past two decades. However, their limitations in detecting only known anomalies prompt the exploration of alternative approaches. Motivated by the success of self-supervised learning in computer vision, there is a rising interest in adapting this paradigm for network intrusion detection. While prior research mainly delved into contrastive self-supervised methods, th…

Higher-arity PAC learning, VC dimension and packing lemma
Artem Chernikov, Henry Towsner
https://arxiv.org/abs/2510.02420 https://arxiv.org/pdf/2510.02420

Higher-arity PAC learning, VC dimension and packing lemma
The aim of this note is to overview some of our work in Chernikov, Towsner'20 (arXiv:2010.00726) developing higher arity VC theory (VC$_n$ dimension), including a generalization of Haussler packing lemma, and an associated tame (slice-wise) hypergraph regularity lemma; and to demonstrate that it characterizes higher arity PAC learning (PAC$_n$ learning) in $n$-fold product spaces with respect to product measures introduced by Kobayashi, Kuriyama and Takeuchi'15. We also point out how some of th…

To Distill or Decide? Understanding the Algorithmic Trade-off in Partially Observable Reinforcement Learning
Yuda Song, Dhruv Rohatgi, Aarti Singh, J. Andrew Bagnell
https://arxiv.org/abs/2510.03207

To Distill or Decide? Understanding the Algorithmic Trade-off in Partially Observable Reinforcement Learning
Partial observability is a notorious challenge in reinforcement learning (RL), due to the need to learn complex, history-dependent policies. Recent empirical successes have used privileged expert distillation--which leverages availability of latent state information during training (e.g., from a simulator) to learn and imitate the optimal latent, Markovian policy--to disentangle the task of "learning to see" from "learning to act". While expert distillation is more computationally efficient tha…

Distributional Inverse Reinforcement Learning
Feiyang Wu, Ye Zhao, Anqi Wu
https://arxiv.org/abs/2510.03013 https://arxiv.org/pdf/2510.03013

Distributional Inverse Reinforcement Learning
We propose a distributional framework for offline Inverse Reinforcement Learning (IRL) that jointly models uncertainty over reward functions and full distributions of returns. Unlike conventional IRL approaches that recover a deterministic reward estimate or match only expected returns, our method captures richer structure in expert behavior, particularly in learning the reward distribution, by minimizing first-order stochastic dominance (FSD) violations and thus integrating distortion risk mea…

Calibrated Uncertainty Sampling for Active Learning
Ha Manh Bui, Iliana Maifeld-Carucci, Anqi Liu
https://arxiv.org/abs/2510.03162 https://arxiv.org/pdf/25…

Calibrated Uncertainty Sampling for Active Learning
We study the problem of actively learning a classifier with a low calibration error. One of the most popular Acquisition Functions (AFs) in pool-based Active Learning (AL) is querying by the model's uncertainty. However, we recognize that an uncalibrated uncertainty model on the unlabeled pool may significantly affect the AF effectiveness, leading to sub-optimal generalization and high calibration error on unseen data. Deep Neural Networks (DNNs) make it even worse as the model uncertainty from…