Search Results for author:
Found 17 papers, 3 papers with code
Chasing Convex Functions with Long-term Constraints
We introduce and study a family of online metric problems with long-term constraints.
Robust Learning-Augmented Dictionaries
We present the first learning-augmented data structure for implementing dictionaries with optimal consistency and robustness.
Adversarial Attacks on Cooperative Multi-agent Bandits
Cooperative multi-agent multi-armed bandits (CMA2B) consider the collaborative efforts of multiple agents in a shared multi-armed bandit game.
Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms
We introduce competitive (robust) threshold-based algorithms for both the minimization and maximization variants of this problem, and show they are optimal among deterministic online algorithms.
Online Algorithms with Uncertainty-Quantified Predictions
In particular, we consider predictions augmented with uncertainty quantification describing the likelihood of the ground truth falling in a certain range, designing online algorithms with these probabilistic predictions for two classic online problems: ski rental and online search.
BONES: Near-Optimal Neural-Enhanced Video Streaming
Accessing high-quality video content can be challenging due to insufficient and unstable network bandwidth.
Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs
Prior algorithms in both paradigms achieve the optimal group regret.
Time Fairness in Online Knapsack Problems
We propose a parameterized deterministic algorithm where the parameter precisely captures the Pareto-optimal trade-off between fairness (static pricing) and competitiveness (dynamic pricing).
Contextual Combinatorial Bandits with Probabilistically Triggered Arms
We study contextual combinatorial bandits with probabilistically triggered arms (C$^2$MAB-T) under a variety of smoothness conditions that capture a wide range of applications, such as contextual cascading bandits and contextual influence maximization bandits.
On-Demand Communication for Asynchronous Multi-Agent Bandits
We propose ODC, an on-demand communication protocol that tailors the communication of each pair of agents based on their empirical pull times.
Online Search with Predictions: Pareto-optimal Algorithm and its Applications in Energy Markets
This paper develops learning-augmented algorithms for energy trading in volatile electricity markets.
CU-Net: Real-Time High-Fidelity Color Upsampling for Point Clouds
Point cloud upsampling is essential for high-quality augmented reality, virtual reality, and telepresence applications, due to the capture, processing, and communication limitations of existing technologies.
Distributed Bandits with Heterogeneous Agents
The goal for each agent is to find its optimal local arm, and agents can cooperate by sharing their observations with others.
Cooperative Stochastic Bandits with Asynchronous Agents and Constrained Feedback
This paper studies a cooperative multi-armed bandit problem with $M$ agents cooperating together to solve the same instance of a $K$-armed stochastic bandit problem with the goal of maximizing the cumulative reward of agents.
Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems
This paper leverages machine-learned predictions to design competitive algorithms for online conversion problems with the goal of improving the competitive ratio when predictions are accurate (i. e., consistency), while also guaranteeing a worst-case competitive ratio regardless of the prediction quality (i. e., robustness).
Adversarial Bandits with Corruptions: Regret Lower Bound and No-regret Algorithm
We characterize the regret of ExpRb as a function of the corruption budget and show that for the case of a known corruption budget, the regret of ExpRb is tight.
Risk-Averse Explore-Then-Commit Algorithms for Finite-Time Bandits
In this paper, we study multi-armed bandit problems in explore-then-commit setting.
