arXiv research

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.

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.

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.

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

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

New Q-learning algorithms reduce regret in inventory control problems.

We present and study models of adversarial online learning where the feedback observed by the learner is noisy, and the feedback is either full information feedback or bandit feedback. Specifically, we consider binary losses xored with the noise, which is a Bernoulli random variable. We consider both a constant noise r…

Capacity-Constrained Online Convex Optimization with Delayed Feedback

The problem of local feedback equivalence for 1-dimensional control systems of the 1-st order is considered. The algebra of differential invariants and criteria for the feedback equivalence for regular control systems are found.

Reinforcement learning with trajectory feedback instead of state-action rewards.

Interactive Machine Learning is concerned with creating systems that operate in environments alongside humans to achieve a task. A typical use is to extend or amplify the capabilities of a human in cognitive or physical ways, requiring the machine to adapt to the users' intentions and preferences. Often, this takes the…

Efficiently identifies good policies by choosing contexts for human feedback.

Neural algorithms optimize arm selection with human preference feedback for complex reward functions.

The problem of Reinforcement Learning (RL) in an unknown nonlinear dynamical system is equivalent to the search for an optimal feedback law utilizing the simulations/ rollouts of the dynamical system. Most RL techniques search over a complex global nonlinear feedback parametrization making them suffer from high trainin…

Sayer uses implicit feedback to optimize system policies.

Hybrid approach combines user feedback and machine learning for predicting user satisfaction.