Quantized Adam reduces communication cost in deep learning training.
problem Reducing communication cost in distributed deep learning training.
method Gradient and weight quantization with error feedback in Adam.
result Proposed methods converge to first-order stationary points.
Reduces user feedback needed for accurate recommender systems.
problem Limited user feedback in recommender systems.
method Partial Bandit and Semi-Bandit approach for efficient user feedback retrieval.
result Similar global accuracy and learning efficiency with reduced feedback.
LoCo-RLHF models diverse human feedback with contextual information.
problem Heterogeneous human feedback from diverse contexts and preferences.
method Low-rank contextual preference model, PRS policy.
result LoCo-RLHF achieves tighter sub-optimality gap than existing methods.
We present an approach to interactive-predictive neural machine translation that attempts to reduce human effort from three directions: Firstly, instead of requiring humans to select, correct, or delete segments, we employ the idea of learning from human reinforcements in form of judgments on the quality of partial tra…
New method uses correlated auxiliary feedback to reduce regret in parameterized bandits.
problem Reducing regret in parameterized bandits with correlated auxiliary feedback.
method Develops a reward estimator using auxiliary feedback with tight confidence bounds.
result Shows significant reduction in regret compared to standard methods.
Direct feedback alignment reduces data movement in neural networks.
problem Efficiency and energy-efficiency in training large neural networks.
method Sparse feedback matrix for local learning, reducing data movement and compute.
result Orders of magnitude improvement in data movement and 2x improvement in multiply-and-accumulate operations.
Feedback improves ESN performance by 30-60% across various tasks.
problem Reducing computational complexity in ESNs for complex sequential data processing.
method State feedback to modify the internal reservoir state of ESNs.
result Feedback significantly improves ESN performance, reducing error by 30-60%.
New Q-learning algorithms reduce regret in inventory control problems.
problem Efficiently learning optimal policies in inventory control problems with limited feedback.
method Proposed Elimination-Based Half-Q-Learning (HQL) and Full-Q-Learning (FQL) algorithms with theoretical regret bounds.
result HQL incurs i l d e O ( H 3 T ) ilde{\mathcal{O}}(H^3\sqrt{ T}) i l d e O ( H 3 T ) regret, FQL incurs i l d e O ( H 2 T ) ilde{\mathcal{O}}(H^2\sqrt{ T}) i l d e O ( H 2 T ) regret, independent of state and action space sizes. New algorithm reduces bandit regret by graph domination number.
problem Adversarial multi-armed bandit with partial observations and switching costs.
method New algorithm with improved policy regret bounds.
result Regret depends only on the domination number of the feedback graph.
We study an online decision making problem where on each round a learner chooses a list of items based on some side information, receives a scalar feedback value for each individual item, and a reward that is linearly related to this feedback. These problems, known as contextual semibandits, arise in crowdsourcing, rec…
New algorithm reduces regret in delayed feedback generalised linear bandits.
problem Regret in delayed feedback generalised linear bandits.
method Adaptation of optimistic algorithm to delayed feedback.
result Achieves a regret bound independent of the horizon's delay penalty.
Infrastructure monitors AI/ML radiology models across multiple sites.
problem Monitoring and improving AI/ML radiology models across multiple sites.
method Interactive radiology reporting, centralized cloud system, post-marketing surveillance.
result Efficient monitoring and iterative development of AI/ML models without radiologist burden.
New algorithm for linear bandits learns from feature feedback, reducing regret.
problem Linear bandit problem with feature feedback.
method Developed new theory and algorithms for linear bandits with feature feedback.
result Achieves regret scaling like k T k\sqrt{T} k T , improving over traditional linear bandits. Algorithm identifies best arm in linked bandits with reduced feedback.
problem Best arm identification in linked bandits with reduced feedback.
method Combines uniform sampling with regular bandit algorithm.
result Almost matching upper and lower bounds on sample complexity.
New algorithm for active learning from feedback coding.
problem Efficiently selecting examples for labeling in active machine learning.
method Formalized structural similarities between active learning and feedback channel coding, developed an optimal transport-based algorithm called Approximate Posterior Matching (APM).
result Learning performance comparable to existing methods at reduced computational cost.
Paper studies CLO with partial feedback, improving decision-making in uncertain contexts.
problem Improving decision-making in contexts with uncertain cost coefficients using partial feedback.
method Unified class of offline learning algorithms for CLO with different types of feedback, using IERM framework.
result Fast-rate regret bound for IERM with partial feedback and misspecified model classes.
Novel algorithms for online learning with uncertain feedback graphs reduce regret.
problem Uncertainty in feedback graphs hinders traditional online learning approaches.
method Developed novel online learning algorithms to handle uncertain feedback graphs.
result Proved sublinear regret under mild conditions for the proposed algorithms.
Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomal…
Agents learn user preferences with less explicit feedback via spatial interface valuing.
problem Learning user preferences with high cognitive load feedback.
method Spatial Interface Valuing for reduced explicit feedback.
result Agents learn faster with spatial interface valuing compared to explicit feedback.
Deep COACH learns complex tasks from human feedback in Minecraft.
problem Learning complex behaviors from human feedback efficiently.
method Deep reinforcement learning with policy updates based on human critiques.
result Demonstrated effectiveness in Minecraft with reduced sample complexity.
The paper proposes a new method for learning reward models from ordinal feedback, improving upon binary feedback.
problem Learning reward models from human preferences using binary feedback discards useful samples and loses fine-grained information.
method The paper introduces a framework for learning reward models under ordinal feedback, generalizing the Bradley-Terry model.
result Ordinal feedback reduces the Rademacher complexity compared to binary feedback, leading to better reward learning.
Study reduces real-world sample complexity for reinforcement learning.
problem Decrease real-world sample complexity in reinforcement learning.
method Formulates a theoretical framework for Sim-to-Real reinforcement learning using ROMDPs.
result Establishes real-world sample complexity guarantees smaller than direct learning.
New method for efficient online exploration in RLHF reduces regret.
problem Efficiently collecting new preference data in RLHF to refine reward model and policy.
method Proposes a new exploration scheme that directs preference queries toward reducing uncertainty in reward differences most relevant to policy improvement.
result Establishes regret bounds of order T ( β + 1 ) / ( β + 2 ) T^{(β+1)/(β+2)} T ( β + 1 ) / ( β + 2 ) for online RLHF, with polynomial scaling in all model parameters. Deep learning reduces training overhead in massive MIMO systems.
problem Reducing training overhead in massive MIMO systems.
method Use of deep learning (NNs) to improve CSI acquisition and feedback processes.
result Significant improvements in performance and reduced complexity.
New techniques improve distributed training with compressed gradients.
problem Gradient mismatch problem in local error feedback.
method Step-ahead error feedback and error averaging techniques.
result Our methods handle gradient mismatch and train faster than full-precision training.
Perfect tracking control for real-world Euler-Lagrange systems is challenging due to uncertainties in the system model and external disturbances. The magnitude of the tracking error can be reduced either by increasing the feedback gains or improving the model of the system. The latter is clearly preferable as it allows…
Study shows visual feedback and monetary incentives reduce plugload energy consumption in commercial buildings.
problem Mitigating energy consumption in commercial buildings through occupant plugload control.
method Field experiments with visual feedback and monetary incentives in government and university buildings.
result Mean energy reduction of ~9.52% in office environments and ~21.61% in university environments with visual feedback.
NeVI-Cut uses neural networks to efficiently propagate uncertainty without feedback.
problem Efficiently propagating uncertainty in downstream Bayesian analysis without feedback.
method NeVI-Cut combines neural networks and normalizing flows for variational inference.
result NeVI-Cut achieves significant computational gains and higher accuracy than traditional methods.
Optimizes LLM prompts using logged user feedback.
problem Naive approaches to optimizing LLM prompts suffer from high variance and bias.
method Kernel-based off-policy gradient method leveraging sentence similarity.
result Substantially reduces variance and suppresses bias in optimizing prompts.
Three online algorithms for submodular maximization with varying feedback types.
problem Maximizing submodular functions under different feedback models.
method Mono-Frank-Wolfe, Bandit-Frank-Wolfe, Responsive-Frank-Wolfe.
result Achieved ( 1 − 1 / e ) (1-1/e) ( 1 − 1/ e ) -regret bounds for each algorithm. New robust algorithms improve learning with feature feedback.
problem Interactive learning with discriminative feature feedback.
method Developed new robust interactive learning algorithms with improved mistake bounds.
result Achieved significantly lower mistake bounds in adversarial and stochastic settings.
Improved CNN training with BDFA reduces computational cost and improves accuracy.
problem Low training performance of DFA in CNN.
method Combining DFA with BP, introducing feedback weight initialization, and proposing BDFA.
result BDFA shows better performance than conventional BP, especially in small datasets.
A new method reduces the computational burden of safety alignment for large language models.
problem Safety concerns in large language models and the need to align them with human preferences.
method Optimal dualization approach to reduce constrained alignment to an unconstrained problem.
result Our algorithms MoCAN and PeCAN significantly reduce computational burden and improve training stability.
Efficient routing algorithms learn from feedback to minimize path lengths.
problem Optimizing online shortest path routing with end-to-end feedback.
method Adaptive exploration algorithms leveraging networked structure.
result Achieves nearly optimal instance-dependent and worst-case regret for directed acyclic networks.
Variational Proximal Policy Optimization improves reinforcement learning from human feedback.
problem Policy mode collapse and brittle exploration loops in reinforcement learning.
method Particle-based variational inference framework with Mixture-of-Experts architecture.
result Significant improvements in complex reasoning benchmarks.
New algorithm reduces risk in online games with limited feedback.
problem Risk-averse learning in repeated unknown games with bandit feedback.
method Proposes a momentum-based algorithm to estimate CVaR using historical cost values.
result Achieves sub-linear regret and outperforms existing methods in numerical experiments.
Paper tackles binary feedbacks in contextual search learning.
problem Learning underlying mean value function in context with binary feedbacks.
method Tri-section search combined with margin-based active learning.
result Algorithm achieves O ( 1 / ε 2 ) O(1/\varepsilon^2) O ( 1/ ε 2 ) queries for ε ε ε -estimation accuracy. SA-PEF improves federated learning efficiency by correcting gradient mismatches.
problem Slow decay of residual error in federated learning under non-IID data.
method Integrates step-ahead correction with partial error feedback.
result Achieves faster convergence and target accuracy compared to standard EF.
Improved Boltzmann exploration reduces regret in stochastic MAB problems.
problem Improving regret in stochastic multi-armed bandit problems.
method A modified Boltzmann exploration with a doubling trick.
result Achieves O ( K log 1 + α T ) O(K\log^{1+α} T) O ( K log 1 + α T ) regret, improving on previous results. New algorithm handles delayed feedback robustly, reducing regret without knowing delay bounds.
problem Bandits with variably delayed feedback, especially excessive delays.
method Implicit exploration scheme, adaptive skipping, drifted regret control.
result Can tolerate arbitrary excessive delays up to order T, reducing regret.
Algorithm reduces online PCA regret to O(d^(3/2)√T).
problem Online PCA with limited feedback.
method Proposes an algorithm for online PCA with bandit feedback.
result Achieves regret of O(d^(3/2)√T) after T rounds.
Study the tradeoffs of bandit feedback in multiclass classification.
problem The price of using bandit feedback in multiclass classification.
method Mistake bound model, analysis of variants, and comparison of learners and adversaries.
result The optimal mistake bound under bandit feedback is at most O ( k ) O(k) O ( k ) times higher than in full information, with a tight bound of O ( k ) O(k) O ( k ) . Model shows how confidence feedback can lead to different crisis outcomes.
problem Characterizing the impact of economic recessions on different social strata.
method A self-reflexive DSGE model with heterogeneous households, varying parameters to analyze crisis typologies.
result Crisis propagation can be confined to high or low income households, depending on social network structure and income inequality.
New algorithm reduces regret in contextual bandits with many near-boundary contexts.
problem High regret in contextual bandits with many near-boundary contexts.
method Hierarchical nearest neighbour approach, holding out contexts for computation.
result Eradicates high regret in adversarial contextual bandits.
Boosting for off-policy learning reduces empirical risk.
problem Learning from logged bandit feedback without labeled data.
method A boosting algorithm optimizing policy's expected reward.
result Excess empirical risk decreases with each round of boosting.
New algorithm reduces clustering cost in bandit feedback.
problem Discovering hidden groupings in stochastic bandit feedback.
method ACB algorithm for active clustering with non-asymptotic lower bound.
result ACB algorithm's budget matches lower bound in most regimes.
SBO uses dual voting to build consensus in noisy feedback settings.
problem Collective decision-making under social influence.
method Dual voting system with noisy public votes and private interviews.
result Efficient consensus-building through noisy public votes, debiased by estimated social graph.
Framework uses human feedback to safely set OOD detection thresholds, reducing false positives.
problem Challenges in setting OOD detection thresholds for safety-critical applications.
method Mathematically grounded framework leveraging expert feedback to dynamically update thresholds.
result Guaranteed to meet FPR constraint while minimizing human feedback, maintaining FPR at most 5%.