47 citations · 327 across the 38 of their papers we have counts for
40 papers
Linear Bandits under Exact Sliding-Window Constraints
Seyed Mohammad Hadi Hosseini, Yasin Abbasi-Yadkori, Sattar Vakili
We study linear bandits under exact sliding-window constraints, where every consecutive block of actions must belong to a prescribed feasible set. In the offline setting, where the…
Generalization in Nonlinear Least Squares via Learned Feature Geometry
Ayub Kharel, Ilja Kuzborskij, Patrick Rebeschini +1
We study the generalization of ridge-regularized nonlinear least-squares models via on-average algorithmic stability, deriving error bounds for local minimizers in terms of a data-…
Best of both worlds: Stochastic & adversarial best-arm identification
Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon +2
We study bandit best-arm identification with arbitrary and potentially adversarial rewards. A simple random uniform learner obtains the optimal rate of error in the adversarial sce…
Low-rank bias, weight decay, and model merging in neural networks
Ilja Kuzborskij, Yasin Abbasi Yadkori
We explore the low-rank structure of the weight matrices in neural networks at the stationary points (limiting solutions of optimization algorithms) with regularization (also…
To Believe or Not to Believe Your LLM
Yasin Abbasi Yadkori, Ilja Kuzborskij, András György +1
We explore uncertainty quantification in large language models (LLMs), with the goal to identify when uncertainty in responses given a query is large. We simultaneously consider bo…
Mitigating LLM Hallucinations via Conformal Abstention
Yasin Abbasi Yadkori, Ilja Kuzborskij, David Stutz +9
We develop a principled procedure for determining when a large language model (LLM) should abstain from responding (e.g., by saying "I don't know") in a general domain, instead of…
Context-lumpable stochastic bandits
Chung-Wei Lee, Qinghua Liu, Yasin Abbasi-Yadkori +3
We consider a contextual bandit problem with contexts and actions. In each round , the learner observes a random context and chooses an action based on its pas…
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms
MohammadJavad Azizi, Thang Duong, Yasin Abbasi-Yadkori +3
We study a sequential decision problem where the learner faces a sequence of -armed bandit tasks. The task boundaries might be known (the bandit meta-learning setting), or unkno…
A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits
Yasin Abbasi-Yadkori, Andras Gyorgy, Nevena Lazic
We study the non-stationary stochastic multi-armed bandit problem, where the reward statistics of each arm may change several times during the course of learning. The performance o…
Efficient Local Planning with Linear Function Approximation
Dong Yin, Botao Hao, Yasin Abbasi-Yadkori +2
We study query and computationally efficient planning algorithms with linear function approximation and a simulator. We assume that the agent only has local access to the simulator…
Feature and Parameter Selection in Stochastic Linear Bandits
Ahmadreza Moradipari, Berkay Turan, Yasin Abbasi-Yadkori +2
We study two model selection settings in stochastic linear bandits (LB). In the first setting, which we refer to as feature selection, the expected reward of the LB problem is in t…
Improved Regret Bound and Experience Replay in Regularized Policy Iteration
Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori +1
In this work, we study algorithms for learning in infinite-horizon undiscounted Markov decision processes (MDPs) with function approximation. We first show that the regret analysis…
Optimization Issues in KL-Constrained Approximate Policy Iteration
Nevena Lazić, Botao Hao, Yasin Abbasi-Yadkori +2
Many reinforcement learning algorithms can be seen as versions of approximate policy iteration (API). While standard API often performs poorly, it has been shown that learning can…
On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function
Gellért Weisz, Philip Amortila, Barnabás Janzer +3
We consider local planning in fixed-horizon MDPs with a generative model under the assumption that the optimal value function lies close to the span of a feature map. The generativ…
The Elliptical Potential Lemma Revisited
Alexandra Carpentier, Claire Vernade, Yasin Abbasi-Yadkori
This note proposes a new proof and new perspectives on the so-called Elliptical Potential Lemma. This result is important in online learning, especially for linear stochastic bandi…
Regret Balancing for Bandit and RL Model Selection
Yasin Abbasi-Yadkori, Aldo Pacchiano, My Phan
We consider model selection in stochastic bandit and reinforcement learning problems. Given a set of base learning algorithms, an effective model selection strategy adapts to the b…
Sample Efficient Graph-Based Optimization with Noisy Observations
Tan Nguyen, Ali Shameli, Yasin Abbasi-Yadkori +2
We study sample complexity of optimizing "hill-climbing friendly" functions defined on a graph under noisy observations. We define a notion of convexity, and we show that a variant…
Model Selection in Contextual Stochastic Bandit Problems
Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori +4
We study bandit model selection in stochastic environments. Our approach relies on a meta-algorithm that selects between candidate base algorithms. We develop a meta-algorithm-base…
Adaptive Approximate Policy Iteration
Botao Hao, Nevena Lazic, Yasin Abbasi-Yadkori +2
Model-free reinforcement learning algorithms combined with value function approximation have recently achieved impressive performance in a variety of application domains. However,…
Exploration-Enhanced POLITEX
Yasin Abbasi-Yadkori, Nevena Lazic, Csaba Szepesvari +1
We study algorithms for average-cost reinforcement learning problems with value function approximation. Our starting point is the recently proposed POLITEX algorithm, a version of…
Thompson Sampling with Approximate Inference
My Phan, Yasin Abbasi-Yadkori, Justin Domke
We study the effects of approximate inference on the performance of Thompson sampling in the -armed bandit problems. Thompson sampling is a successful algorithm for online decis…
Bootstrapping Upper Confidence Bound
Botao Hao, Yasin Abbasi-Yadkori, Zheng Wen +1
Upper Confidence Bound (UCB) method is arguably the most celebrated one used in online decision making with partial information feedback. Existing techniques for constructing confi…
Large-Scale Markov Decision Problems via the Linear Programming Dual
Yasin Abbasi-Yadkori, Peter L. Bartlett, Xi Chen +1
We consider the problem of controlling a fully specified Markov decision process (MDP), also known as the planning problem, when the state space is very large and calculating the o…
New Insights into Bootstrapping for Bandits
Sharan Vaswani, Branislav Kveton, Zheng Wen +3
We investigate the use of bootstrapping in the bandit setting. We first show that the commonly used non-parametric bootstrapping (NPB) procedure can be provably inefficient and est…