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Found 24 papers, 4 papers with code
Contextual Linear Bandits with Delay as Payoff
A recent work by Schlisselberg et al. (2024) studies a delay-as-payoff model for stochastic multi-armed bandits, where the payoff (either loss or reward) is delayed for a period that is proportional to the payoff itself.
Data Pricing for Graph Neural Networks without Pre-purchased Inspection
These marketplaces leverage model trading mechanisms to properly incentive data owners to contribute their data, and return a well performing ML model to the model consumers.
Provably Efficient Interactive-Grounded Learning with Personalized Reward
Interactive-Grounded Learning (IGL) [Xie et al., 2021] is a powerful framework in which a learner aims at maximizing unobservable rewards through interacting with an environment and observing reward-dependent feedback on the taken actions.
No-Regret Learning for Fair Multi-Agent Social Welfare Optimization
Specifically, in stochastic $N$-agent $K$-armed bandits, we develop an algorithm with $\widetilde{\mathcal{O}}\left(K^{\frac{2}{N}}T^{\frac{N-1}{N}}\right)$ regret and prove that the dependence on $T$ is tight, making it a sharp contrast to the $\sqrt{T}$-regret bounds of Hossain et al. [2021], Jones et al. [2023].
Contextual Multinomial Logit Bandits with General Value Functions
Contextual multinomial logit (MNL) bandits capture many real-world assortment recommendation problems such as online retailing/advertising.
Efficient Contextual Bandits with Uninformed Feedback Graphs
Bandits with feedback graphs are powerful online learning models that interpolate between the full information and classic bandit problems, capturing many real-life applications.
Online Learning in Contextual Second-Price Pay-Per-Click Auctions
We study online learning in contextual pay-per-click auctions where at each of the $T$ rounds, the learner receives some context along with a set of ads and needs to make an estimate on their click-through rate (CTR) in order to run a second-price pay-per-click auction.
A Survey of Data Pricing for Data Marketplaces
Data pricing, as a key function of a data marketplace, demands quantifying the monetary value of data.
Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics
We study a game between autobidding algorithms that compete in an online advertising platform.
No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution
Supply chain management (SCM) has been recognized as an important discipline with applications to many industries, where the two-echelon stochastic inventory model, involving one downstream retailer and one upstream supplier, plays a fundamental role for developing firms' SCM strategies.
Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs
For general strongly observable graphs, we develop an algorithm that achieves the optimal regret $\widetilde{\mathcal{O}}((\sum_{t=1}^T\alpha_t)^{1/2}+\max_{t\in[T]}\alpha_t)$ with high probability, where $\alpha_t$ is the independence number of the feedback graph at round $t$.
SPAIC: A Spike-based Artificial Intelligence Computing Framework
In this work, we present a Python based spiking neural network (SNN) simulation and training framework, aka SPAIC that aims to support brain-inspired model and algorithm researches integrated with features from both deep learning and neuroscience.
Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits
The CORRAL algorithm of Agarwal et al. (2017) and its variants (Foster et al., 2020a) achieve this goal with a regret overhead of order $\widetilde{O}(\sqrt{MT})$ where $M$ is the number of base algorithms and $T$ is the time horizon.
Adaptive Bandit Convex Optimization with Heterogeneous Curvature
We consider the problem of adversarial bandit convex optimization, that is, online learning over a sequence of arbitrary convex loss functions with only one function evaluation for each of them.
No-Regret Learning in Time-Varying Zero-Sum Games
Learning from repeated play in a fixed two-player zero-sum game is a classic problem in game theory and online learning.
Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
In this work, we develop linear bandit algorithms that automatically adapt to different environments.
Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games
We study infinite-horizon discounted two-player zero-sum Markov games, and develop a decentralized algorithm that provably converges to the set of Nash equilibria under self-play.
RANDOM MASK: Towards Robust Convolutional Neural Networks
We next investigate the adversarial examples which 'fool' a CNN with Random Mask.
Linear Last-iterate Convergence in Constrained Saddle-point Optimization
Specifically, for OMWU in bilinear games over the simplex, we show that when the equilibrium is unique, linear last-iterate convergence is achieved with a learning rate whose value is set to a universal constant, improving the result of (Daskalakis & Panageas, 2019b) under the same assumption.
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs
We develop a new approach to obtaining high probability regret bounds for online learning with bandit feedback against an adaptive adversary.
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback
We study small-loss bounds for adversarial multi-armed bandits with graph feedback, that is, adaptive regret bounds that depend on the loss of the best arm or related quantities, instead of the total number of rounds.
Defective Convolutional Networks
Robustness of convolutional neural networks (CNNs) has gained in importance on account of adversarial examples, i. e., inputs added as well-designed perturbations that are imperceptible to humans but can cause the model to predict incorrectly.
The Local Dimension of Deep Manifold
Our results show that the dimensions of different categories are close to each other and decline quickly along the convolutional layers and fully connected layers.
Randomness in Deconvolutional Networks for Visual Representation
Toward a deeper understanding on the inner work of deep neural networks, we investigate CNN (convolutional neural network) using DCN (deconvolutional network) and randomization technique, and gain new insights for the intrinsic property of this network architecture.
