A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Unified approach to aggregating models and preferences.
The goal of task transfer in reinforcement learning is migrating the action policy of an agent to the target task from the source task. Given their successes on robotic action planning, current methods mostly rely on two requirements: exactly-relevant expert demonstrations or the explicitly-coded cost function on targe…
Enhances BO with expert preferences about abstract properties.
The paper proposes a method to explain expert decisions by modeling preferences with 'what if' outcomes.
New conditions show proxy data can improve policy learning in sparse expert data contexts.
We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or as different experts trying to solve the same task. Our main contribution is to formalise the problem as statistical preference elicitation,…
Reinforcement learning (RL) has achieved tremendous success as a general framework for learning how to make decisions. However, this success relies on the interactive hand-tuning of a reward function by RL experts. On the other hand, inverse reinforcement learning (IRL) seeks to learn a reward function from readily-obt…
Dropping a tiny fraction of preferences can significantly alter the rankings of top LLMs.
SARA uses similarity to learn rewards robustly and adaptively.
Societies often rely on human experts to take a wide variety of decisions affecting their members, from jail-or-release decisions taken by judges and stop-and-frisk decisions taken by police officers to accept-or-reject decisions taken by academics. In this context, each decision is taken by an expert who is typically …
AGFN improves causal discovery by integrating expert feedback and handling latent confounding.
Model predicts human food choices based on demographics.
A fuzzy expert system selects stocks for BSE using AI techniques.
Meta-Router optimizes LLM selection using gold-standard and preference-based data.
To solve complex real-world problems with reinforcement learning, we cannot rely on manually specified reward functions. Instead, we can have humans communicate an objective to the agent directly. In this work, we combine two approaches to learning from human feedback: expert demonstrations and trajectory preferences. …
One typical assumption in inverse reinforcement learning (IRL) is that human experts act to optimize the expected utility of a stochastic cost with a fixed distribution. This assumption deviates from actual human behaviors under ambiguity. Risk-sensitive inverse reinforcement learning (RS-IRL) bridges such gap by assum…
Current imitation learning techniques are too restrictive because they require the agent and expert to share the same action space. However, oftentimes agents that act differently from the expert can solve the task just as good. For example, a person lifting a box can be imitated by a ceiling mounted robot or a desktop…
Professional-grade software applications are powerful but complicatedexpert users can achieve impressive results, but novices often struggle to complete even basic tasks. Photo editing is a prime example: after loading a photo, the user is confronted with an array of cryptic sliders like "clarity", "temp", and "high…
For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effective…
Proposes a TS approach for Bayesian optimization with preferential feedback.
Making the right decision in traffic is a challenging task that is highly dependent on individual preferences as well as the surrounding environment. Therefore it is hard to model solely based on expert knowledge. In this work we use Deep Reinforcement Learning to learn maneuver decisions based on a compact semantic st…
Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires, preferences etc. This paper investigates modelling of ordinal data with Gaussian restricted Boltzmann machines (RBMs). In particular, we present the model architecture, learning and inference procedures for both vector-variate and …
Study optimal investment with herd behavior using rational decision decomposition.
We present a learning-based system for rapid mass-scale material synthesis that is useful for novice and expert users alike. The user preferences are learned via Gaussian Process Regression and can be easily sampled for new recommendations. Typically, each recommendation takes 40-60 seconds to render with global illumi…
Meta-learning strategy improves few-shot classification performance.
We seek to align agent policy with human expert behavior in a reinforcement learning (RL) setting, without any prior knowledge about dynamics, reward function, and unsafe states. There is a human expert knowing the rewards and unsafe states based on his preference and objective, but querying that human expert is expens…
t-Distributed Stochastic Neighbor Embedding (t-SNE) is one of the most widely used dimensionality reduction methods for data visualization, but it has a perplexity hyperparameter that requires manual selection. In practice, proper tuning of t-SNE perplexity requires users to understand the inner working of the method a…
The optimal policy of a reinforcement learning problem is often discontinuous and non-smooth. I.e., for two states with similar representations, their optimal policies can be significantly different. In this case, representing the entire policy with a function approximator (FA) with shared parameters for all states may…
New IRL algorithm for continuous state spaces with formal guarantees.
Multi-expert L2D underfits more severely, requiring new methods.
TENP prunes experts and neurons in Mixture-of-Experts models for efficient deployment.
A method to select important experts for Gaussian processes to balance computational efficiency and uncertainty quantification.
Optimizes molecular generation for chemist preferences.
We consider the problem of contextual bandits with stochastic experts, which is a variation of the traditional stochastic contextual bandit with experts problem. In our problem setting, we assume access to a class of stochastic experts, where each expert is a conditional distribution over the arms given a context. We p…
Improved time series forecasting with expert loss integration.
New method adapts to user preferences dynamically, improving recommendation models.
HS-MoE selects sparse experts using adaptive priors and data-adaptive gating.
NAMEx merges experts using Nash bargaining for improved performance.
Many real-world engineering problems rely on human preferences to guide their design and optimization. We present PrefOpt, an open source package to simplify sequential optimization tasks that incorporate human preference feedback. Our approach extends an existing latent variable model for binary preferences to allow f…
Expert augmentation improves hybrid model generalization.
Enhances preference learning by incorporating response times into binary choices.
Bayesian optimization learns DM preferences for multi-outcome experiments.
New method calibrates Gaussian product experts for better predictions.
New study shows personalized content recommendations can lead to polarization of user preferences.
Meta-algorithm optimizes nonstochastic bandits with infinitely many experts.
There is tremendous interest in precision medicine as a means to improve patient outcomes by tailoring treatment to individual characteristics. An individualized treatment rule formalizes precision medicine as a map from patient information to a recommended treatment. A treatment rule is defined to be optimal if it max…
New algorithm reduces expert prediction regret for two experts.
System uses conformal prediction to help experts make accurate decisions without understanding when to trust it.