arXiv research

Paper tackles stochastic $k$-submodular bandits with full feedback, achieving sublinear regret.

The paper tackles combinatorial pure exploration with various feedback structures and proposes efficient algorithms.

Top-k Combinatorial Bandits generalize multi-armed bandits, where at each round any subset of $k$ out of $n$ arms may be chosen and the sum of the rewards is gained. We address the full-bandit feedback, in which the agent observes only the sum of rewards, in contrast to the semi-bandit feedback, in which the agent obse…

We study the problem of stochastic combinatorial pure exploration (CPE), where an agent sequentially pulls a set of single arms (a.k.a. a super arm) and tries to find the best super arm. Among a variety of problem settings of the CPE, we focus on the full-bandit setting, where we cannot observe the reward of each singl…

New framework tackles submodular welfare with multi-agent combinatorial bandits.

This paper tackles combinatorial optimization under uncertainty with limited feedback.

We develop the first general semi-bandit algorithm that simultaneously achieves $\mathcal{O}(\log T)$ regret for stochastic environments and $\mathcal{O}(\sqrt{T})$ regret for adversarial environments without knowledge of the regime or the number of rounds $T$. The leading problem-dependent constants of our bounds are …

We study the problem of online path learning with non-additive gains, which is a central problem appearing in several applications, including ensemble structured prediction. We present new online algorithms for path learning with non-additive count-based gains for the three settings of full information, semi-bandit and…

New algorithm identifies best intervention without graph knowledge.

New insights into cascade feedback linearization of control systems.

New method learns from either positive or negative feedback alone.

User preferences for items can be inferred from either explicit feedback, such as item ratings, or implicit feedback, such as rental histories. Research in collaborative filtering has concentrated on explicit feedback, resulting in the development of accurate and scalable models. However, since explicit feedback is oft…

This paper improves image retrieval accuracy through novel relevance feedback methods.

Recommender systems recommend items more accurately by analyzing users' potential interest on different brands' items. In conjunction with users' rating similarity, the presence of users' implicit feedbacks like clicking items, viewing items specifications, watching videos etc. have been proved to be helpful for learni…

Study evaluates new models using human feedback from another model.

Develops a new model for RLHF accounting for partially observed states and intermediate feedback.

Classifier learns to ignore unreliable feedback from end users.

Paper characterizes minimax regret rates for online ranking with top-k feedback.

Study on sample complexity for pure exploration in feedback graph settings.

One-bit feedback suffices for a bandit problem's optimal strategy.

Advocates a local feedback approach for RL in unknown systems.

A method corrects feedback shift in predicting conversion rates with delayed feedback.

Study how communication and feedback graphs affect learning outcomes.

New method uses correlated auxiliary feedback to reduce regret in parameterized bandits.

Test for linearizing 2-input systems with 2D feedback.

The problem of feedback equivalence for control systems is considered. An algebra of differential invariants and criteria for the feedback equivalence for regular control systems are found.

Reduces user feedback needed for accurate recommender systems.

We present a study on reinforcement learning (RL) from human bandit feedback for sequence-to-sequence learning, exemplified by the task of bandit neural machine translation (NMT). We investigate the reliability of human bandit feedback, and analyze the influence of reliability on the learnability of a reward estimator,…

New algorithm for recommending best arms with aggregated feedback.

Study of reinforcement learning with additional feedback observations.

CausalRM models rewards from user feedback, overcoming noise and bias.

Paper tackles noisy bandit feedback for multiclass classification.

We propose a generalization of the best arm identification problem in stochastic multi-armed bandits (MAB) to the setting where every pull of an arm is associated with delayed feedback. The delay in feedback increases the effective sample complexity of standard algorithms, but can be offset if we have access to partial…

Easy conditions found for simplifying complex systems.

Active learning framework for optimizing human preferences in reinforcement learning.

Paper studies CLO with partial feedback, improving decision-making in uncertain contexts.

We use online convex optimization (OCO) for setpoint tracking with uncertain, flexible loads. We consider full feedback from the loads, bandit feedback, and two intermediate types of feedback: partial bandit where a subset of the loads are individually observed and the rest are observed in aggregate, and Bernoulli feed…

CAFL breaks feedback loops in recommender systems using causal inference.

We consider combinatorial online learning with subset choices when only relative feedback information from subsets is available, instead of bandit or semi-bandit feedback which is absolute. Specifically, we study two regret minimisation problems over subsets of a finite ground set $[n]$, with subset-wise relative prefe…

Unified framework for expert selection with bandit and lower-bound feedback.

GAIF enhances online multiple testing with feedback, improving statistical power.

New algorithms handle online prediction with bandit and delayed feedback, improving regret bounds.

New method provides fine-grained feedback on interactive student programs.

Improves labeling quality in machine learning with pairwise feedback.

The paper proposes a new method for learning reward models from ordinal feedback, improving upon binary feedback.

We study an online learning framework introduced by Mannor and Shamir (2011) in which the feedback is specified by a graph, in a setting where the graph may vary from round to round and is \emph{never fully revealed} to the learner. We show a large gap between the adversarial and the stochastic cases. In the adversaria…

New algorithms ensure fair selection in combinatorial semi-bandit with unrestricted delays.

New method for online learning IC models with node-level feedback.