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Found 16 papers, 0 papers with code
Combinatorial Pure Exploration for Dueling Bandit
For Borda winner, we establish a reduction of the problem to the original CPE-MAB setting and design PAC and exact algorithms that achieve both the sample complexity similar to that in the CPE-MAB setting (which is nearly optimal for a subclass of problems) and polynomial running time per round.
Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization
In PO-RLHF, knowledge of the reward function is not assumed and the algorithm relies on trajectory-based comparison feedback to infer the reward function.
Cascading Reinforcement Learning
In the cascading bandit model, at each timestep, an agent recommends an ordered subset of items (called an item list) from a pool of items, each associated with an unknown attraction probability.
Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback
Risk-sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk.
Multi-task Representation Learning for Pure Exploration in Linear Bandits
In these two problems, all tasks share a common low-dimensional linear representation, and our goal is to leverage this feature to accelerate the best arm (policy) identification process for all tasks.
Dueling Bandits: From Two-dueling to Multi-dueling
DoublerBAI provides a generic schema for translating known results on best arm identification algorithms to the dueling bandit problem, and achieves a regret bound of $O(\ln T)$.
Provably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Path
For Worst Path RL, we propose an efficient algorithm with constant upper and lower bounds.
Branching Reinforcement Learning
In this paper, we propose a novel Branching Reinforcement Learning (Branching RL) model, and investigate both Regret Minimization (RM) and Reward-Free Exploration (RFE) metrics for this model.
Collaborative Pure Exploration in Kernel Bandit
In this paper, we formulate a Collaborative Pure Exploration in Kernel Bandit problem (CoPE-KB), which provides a novel model for multi-agent multi-task decision making under limited communication and general reward functions, and is applicable to many online learning tasks, e. g., recommendation systems and network scheduling.
Combinatorial Pure Exploration with Bottleneck Reward Function
For the FC setting, we propose novel algorithms with optimal sample complexity for a broad family of instances and establish a matching lower bound to demonstrate the optimality (within a logarithmic factor).
Continuous Mean-Covariance Bandits
To the best of our knowledge, this is the first work that considers option correlation in risk-aware bandits and explicitly quantifies how arbitrary covariance structures impact the learning performance.
A One-Size-Fits-All Solution to Conservative Bandit Problems
In this paper, we study a family of conservative bandit problems (CBPs) with sample-path reward constraints, i. e., the learner's reward performance must be at least as well as a given baseline at any time.
Combinatorial Pure Exploration of Dueling Bandit
For Borda winner, we establish a reduction of the problem to the original CPE-MAB setting and design PAC and exact algorithms that achieve both the sample complexity similar to that in the CPE-MAB setting (which is nearly optimal for a subclass of problems) and polynomial running time per round.
Combinatorial Pure Exploration with Full-Bandit or Partial Linear Feedback
In this paper, we first study the problem of combinatorial pure exploration with full-bandit feedback (CPE-BL), where a learner is given a combinatorial action space $\mathcal{X} \subseteq \{0, 1\}^d$, and in each round the learner pulls an action $x \in \mathcal{X}$ and receives a random reward with expectation $x^{\top} \theta$, with $\theta \in \mathbb{R}^d$ a latent and unknown environment vector.
Object-Adaptive LSTM Network for Real-time Visual Tracking with Adversarial Data Augmentation
This strategy efficiently filters out some irrelevant proposals and avoids the redundant computation for feature extraction, which enables our method to operate faster than conventional classification-based tracking methods.
Direct Object Recognition Without Line-of-Sight Using Optical Coherence
Visual object recognition under situations in which the direct line-of-sight is blocked, such as when it is occluded around the corner, is of practical importance in a wide range of applications.
