arXiv research

Self-regulation improves sequence-to-sequence learning by choosing feedback types.

New method learns from either positive or negative feedback alone.

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…

Improves relevancy of black-box anomaly detectors with user feedback.

Unified neural framework for multi-relational recommender systems.

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…

We introduce an autoregressive-type model of prices in financial market taking into account the self-modulation effect. We find that traders are mainly using strategies with weighted feedbacks of past prices. These feedbacks are responsible for the slow diffusion in short times, apparent trends and power law distributi…

Paper fine-tunes LLMs using user edits, unifying preference, supervision, and reward feedback.

A predictor that is deployed in a live production system may perturb the features it uses to make predictions. Such a feedback loop can occur, for example, when a model that predicts a certain type of behavior ends up causing the behavior it predicts, thus creating a self-fulfilling prophecy. In this paper we analyze p…

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

Generates detailed fashion feedback from outfit images.

We consider a financial market model which consists of a financial asset and a large number of interacting agents classified into many types. Different types of agents are heterogeneous in their price expectations. Each agent can change its type based on the current empirical distribution of the types and the equilibri…

This paper extends a Kyle model to include price-responsive traders, revealing new dynamics and equilibria.

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

Algorithm learns fair division from noisy feedback in uncertain markets.

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…

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

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper…

We consider a problem of stochastic online learning with general probabilistic graph feedback, where each directed edge in the feedback graph has probability $p_{ij}$. Two cases are covered. (a) The one-step case, where after playing arm $i$ the learner observes a sample reward feedback of arm $j$ with independent prob…

New algorithms minimize regret in repeated auctions by estimating values and optimizing bids.

Unified framework for analyzing online convex optimization across various settings.

We introduce an autoregressive-type model with self-modulation effects for a foreign exchange rate by separating the foreign exchange rate into a moving average rate and an uncorrelated noise. From this model we indicate that traders are mainly using strategies with weighted feedbacks of the past rates in the exchange …

Quantized Adam reduces communication cost in deep learning training.

Theoretical model for iterative user discovery in recommender systems.

Motivated by problems in search and detection we present a solution to a Combinatorial Multi-Armed Bandit (CMAB) problem with both heavy-tailed reward distributions and a new class of feedback, filtered semibandit feedback. In a CMAB problem an agent pulls a combination of arms from a set $\{1,...,k\}$ in each round, g…

Algorithm identifies Copeland winners in dueling bandits with ternary feedback.

New framework PBBO optimizes latent functions with preferential feedback.

This paper improves neural network robustness through feedback learning.

Feedback improves ESN performance by 30-60% across various tasks.

We study the problem of using causal models to improve the rate at which good interventions can be learned online in a stochastic environment. Our formalism combines multi-arm bandits and causal inference to model a novel type of bandit feedback that is not exploited by existing approaches. We propose a new algorithm t…

A new personality-based recommender system tackles data sparsity without feedback.

Three online algorithms for submodular maximization with varying feedback types.

Paper reviews methods for learning from bandit feedback in recommender systems.

Modulo-SK outperforms Deepcode in error probability and feedback rounds.

Bayesian model identifies three types of travelers adapting to feedback.

REN addresses uncertainty in user feedbacks for better recommendation systems.

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in-vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in-silico.…

Policy gradient algorithms typically combine discounted future rewards with an estimated value function, to compute the direction and magnitude of parameter updates. However, for most Reinforcement Learning tasks, humans can provide additional insight to constrain the policy learning. We introduce a general method to i…

The paper optimizes exceptions in a statistical production system using machine learning.

In supervised learning, we typically leverage a fully labeled dataset to design methods for function estimation or prediction. In many practical situations, we are able to obtain alternative feedback, possibly at a low cost. A broad goal is to understand the usefulness of, and to design algorithms to exploit, this alte…

We consider the non-stochastic version of the (cooperative) multi-player multi-armed bandit problem. The model assumes no communication at all between the players, and furthermore when two (or more) players select the same action this results in a maximal loss. We prove the first $\sqrt{T}$-type regret guarantee for th…

Study analyzes broker's gain from trade in repeated context-based trading.

Paper tackles invoice line item matching in P2P processes using agent feedback.

The paper constructs optimal hedging strategies for options with price impact.

New insights into cascade feedback linearization of control systems.

Modeling financial dynamics with Hawkes processes and rough volatility, capturing Zumbach effect.

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.