3 citations · 7 across the 12 of their papers we have counts for
12 papers
Gemma 4 Technical Report
Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…
RewardUQ: A Unified Framework for Uncertainty-Aware Reward Models
Daniel Yang, Samuel Stante, Florian Redhardt +5
Reward models are central to aligning large language models (LLMs) with human preferences. Yet most approaches rely on pointwise reward estimates that overlook the epistemic uncert…
LITE: Efficiently Estimating Gaussian Probability of Maximality
Nicolas Menet, Jonas Hübotter, Parnian Kassraie +1
We consider the problem of computing the probability of maximality (PoM) of a Gaussian random vector, i.e., the probability for each dimension to be maximal. This is a key challeng…
Bandits with Preference Feedback: A Stackelberg Game Perspective
Barna Pásztor, Parnian Kassraie, Andreas Krause
Bandits with preference feedback present a powerful tool for optimizing unknown target functions when only pairwise comparisons are allowed instead of direct value queries. This mo…
Progressive Entropic Optimal Transport Solvers
Parnian Kassraie, Aram-Alexandre Pooladian, Michal Klein +3
Optimal transport (OT) has profoundly impacted machine learning by providing theoretical and computational tools to realign datasets. In this context, given two large point clouds…
Anytime Model Selection in Linear Bandits
Parnian Kassraie, Nicolas Emmenegger, Andreas Krause +1
Model selection in the context of bandit optimization is a challenging problem, as it requires balancing exploration and exploitation not only for action selection, but also for mo…
Hallucinated Adversarial Control for Conservative Offline Policy Evaluation
Jonas Rothfuss, Bhavya Sukhija, Tobias Birchler +2
We study the problem of conservative off-policy evaluation (COPE) where given an offline dataset of environment interactions, collected by other agents, we seek to obtain a (tight)…
Instance-Dependent Generalization Bounds via Optimal Transport
Songyan Hou, Parnian Kassraie, Anastasis Kratsios +2
Existing generalization bounds fail to explain crucial factors that drive the generalization of modern neural networks. Since such bounds often hold uniformly over all parameters,…
Lifelong Bandit Optimization: No Prior and No Regret
Felix Schur, Parnian Kassraie, Jonas Rothfuss +1
Machine learning algorithms are often repeatedly applied to problems with similar structure over and over again. We focus on solving a sequence of bandit optimization tasks and dev…
Graph Neural Network Bandits
Parnian Kassraie, Andreas Krause, Ilija Bogunovic
We consider the bandit optimization problem with the reward function defined over graph-structured data. This problem has important applications in molecule design and drug discove…
Meta-Learning Hypothesis Spaces for Sequential Decision-making
Parnian Kassraie, Jonas Rothfuss, Andreas Krause
Obtaining reliable, adaptive confidence sets for prediction functions (hypotheses) is a central challenge in sequential decision-making tasks, such as bandits and model-based reinf…
Neural Contextual Bandits without Regret
Parnian Kassraie, Andreas Krause
Contextual bandits are a rich model for sequential decision making given side information, with important applications, e.g., in recommender systems. We propose novel algorithms fo…