arXiv research

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

Physics-informed GCRL tackles sparse feedback learning with hybrid dynamics.

UCFE benchmarks LLMs in financial tasks with human feedback.

Interactive IL beats BC by state-wise annotation cost.

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

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

Improved ExO method achieves near-optimal bounds in both stochastic and adversarial settings.

In today's day and age when almost every industry has an online presence with users interacting in online marketplaces, personalized recommendations have become quite important. Traditionally, the problem of collaborative filtering has been tackled using Matrix Factorization which is linear in nature. We extend the wor…

This work presents a methodology to design trajectory tracking feedback control laws, which embed non-parametric statistical models, such as Gaussian Processes (GPs). The aim is to minimize unmodeled dynamics such as undesired slippages. The proposed approach has the benefit of avoiding complex terramechanics analysis …

Combining watermark and non-watermark detectors improves LLM detection.

Scheduling the transmission of time-sensitive data to multiple users over error-prone communication channels is studied with the goal of minimizing the long-term average age of information (AoI) at the users under a constraint on the average number of transmissions at the source node. After each transmission, the sourc…

Paper tackles online ranking and diversification in recommender systems.

PARNN improves ARNN with ARIMA feedback for accurate long-range forecasting.

Recommender systems are used in variety of domains affecting people's lives. This has raised concerns about possible biases and discrimination that such systems might exacerbate. There are two primary kinds of biases inherent in recommender systems: observation bias and bias stemming from imbalanced data. Observation b…

Paper addresses RLHF alignment challenges with novel algorithms.

Extends reinforcement learning alignment to scalar rewards, improving math reasoning.

Hybrid Deep Embedding for aspect-level explanations in recommendations.

Hybrid model uses LLM to build transparent Bayesian networks for trading decisions.

Adaptive RL optimizes testing resource allocation for dynamic software environments.

Enhanced TSFMs improve time series forecasting accuracy and reliability.

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 …

The paper improves DFA for CNN and RNN training to match BP accuracy.

Paper proposes a semi-supervised learning approach for automated topic detection in support and feedback data.

The market events of 2007-2009 have reinvigorated the search for realistic return models that capture greater likelihoods of extreme movements. In this paper we model the medium-term log-return dynamics in a market with both fundamental and technical traders. This is based on a Poisson trade arrival model with variable…

Optimal retirement timing and consumption under shortfall risk management

Defines non-parabolic curves in spatial hybrid space with applications.

Discussing hybrid models in Bayesian networks.

Expert augmentation improves hybrid model generalization.

In this work we present a new approach on studying dynamical systems. Combining the two ways of expressing the uncertainty, using probabilistic theory and credibility theory, we have research the generalized fractional hybrid equations. We have introduced the concepts of generalized fractional Wiener process, generaliz…

We explore hybrid subgroups of certain non-arithmetic lattices in $\mathrm{PU}(2,1)$. We show that all of Mostow's lattices are virtually hybrids; moreover, we show that some of these non-arithmetic lattices are hybrids of two non-commensurable arithmetic lattices in $\mathrm{PU}(1,1)$.

Defines hybrid systems on principal bundles and studies impact effects.

Hybrid models forecast EPEC energy spot prices.

New method sparsifies hybrid neural ODEs for better performance and stability.

Hybrid models combine interpretable and complex models for better performance and control.

New insights into cascade feedback linearization of control systems.

New method learns from either positive or negative feedback alone.

A hybrid ASR system using conformer architecture improves word-error-rate and training speed.

CMOSS algorithm reduces regret in combinatorial semi-bandits with efficient computation.

The paper introduces FMCI and hybrid decoding for hidden Markov models.

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.

In the present paper, a fuzzy logic based method is combined with wavelet decomposition to develop a step-by-step dynamic hybrid model for the estimation of financial time series. Empirical tests on fuzzy regression, wavelet decomposition as well as the new hybrid model are conducted on the well known $SP500$ index fin…

Hybrid model improves music source separation by 1.4 dB.

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.

Develops numerical methods for PDEs on hypergraphs and networks.

Classifier learns to ignore unreliable feedback from end users.