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From the 2 of 73 linked papers with an AI index.

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20242026
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cs.LG2026

When Does On-Policy Interaction Help? Representational Tradeoffs in Value-Based Imitation Learning

Luca Viano, Antoine Moulin, Audrey Huang +3

Imitation learning (IL)---training an agent to replicate expert behavior from demonstrations---underpins applications from robotics to language model training. Standard approaches…

cs.LG2026

Raven: High-Recall Sequence Modeling with Sparse Memory Routing

Arshia Afzal, Aviv Bick, Eric P. Xing +2

Raven is a linear-time sequence model that uses learned, input-dependent routing to update only a subset of fixed memory slots, reducing interference and improving long-range recal…

cs.LG2026

Multi-agent imitation learning with function approximation: Linear Markov games and beyond

Luca Viano, Till Freihaut, Emanuele Nevali +3

In this work, we present the first theoretical analysis of multi-agent imitation learning (MAIL) in linear Markov games where both the transition dynamics and each agent's reward f…

cs.LG2026

Demystifying Variance in Circuit Discovery of LLMs

Frank Zhengqing Wu, Francesco Tonin, Volkan Cevher

Circuit discovery is a key technique in mechanistic interpretability to pinpoint the model components that are crucial for performing a given task. Although the current state-of-th…

cs.LG2026

GRASP: Deterministic argument ranking in interaction graphs

Diganta Misra, Antonio Orvieto, Rediet Abebe +1

Large language models are increasingly deployed as automated judges to evaluate the strength of arguments. As this role expands, their legitimacy depends on consistency, transparen…

cs.LG2026

Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation

Ziyad Sheebaelhamd, Luca Viano, Volkan Cevher +1

This work investigates multi-objective imitation learning: the problem of recovering policies that lie on the Pareto front given demonstrations from multiple Pareto-optimal experts…