Search Results for author:
Found 27 papers, 4 papers with code
Are Bounded Contracts Learnable and Approximately Optimal?
This paper considers the hidden-action model of the principal-agent problem, in which a principal incentivizes an agent to work on a project using a contract.
Scalable Virtual Valuations Combinatorial Auction Design by Combining Zeroth-Order and First-Order Optimization Method
Subsequently, we propose a novel optimization method that combines both zeroth-order and first-order techniques to optimize the VVCA parameters.
A survey on algorithms for Nash equilibria in finite normal-form games
This paper reviews various algorithms computing the Nash equilibrium and its approximation solutions in finite normal-form games from both theoretical and empirical perspectives.
Learning Thresholds with Latent Values and Censored Feedback
In this paper, we investigate a problem of actively learning threshold in latent space, where the unknown reward $g(\gamma, v)$ depends on the proposed threshold $\gamma$ and latent value $v$ and it can be $only$ achieved if the threshold is lower than or equal to the unknown latent value.
The Search-and-Mix Paradigm in Approximate Nash Equilibrium Algorithms
For the first time, this work provides an automatic method for approximation analysis on a well-studied problem in theoretical computer science: computing approximate Nash equilibria in two-player games.
Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets
To the best of our knowledge, we are the first to consider bidder coordination in online repeated auctions with constraints.
A Scalable Neural Network for DSIC Affine Maximizer Auction Design
AMenuNet is always DSIC and individually rational (IR) due to the properties of AMAs, and it enhances scalability by generating candidate allocations through a neural network.
Learning to Manipulate a Commitment Optimizer
We consider the scenario where the follower is not given any information about the leader's payoffs to begin with but has to learn to manipulate by interacting with the leader.
Are Equivariant Equilibrium Approximators Beneficial?
Recently, remarkable progress has been made by approximating Nash equilibrium (NE), correlated equilibrium (CE), and coarse correlated equilibrium (CCE) through function approximation that trains a neural network to predict equilibria from game representations.
On the Re-Solving Heuristic for (Binary) Contextual Bandits with Knapsacks
To the best of our knowledge, this is the first $\widetilde O(1)$ regret result in the CBwK problem regardless of information feedback models.
Sybil-Proof Diffusion Auction in Social Networks
A diffusion auction is a market to sell commodities over a social network, where the challenge is to incentivize existing buyers to invite their neighbors in the network to join the market.
AReputation-Based Mechanism for Transaction Processing in Blockchain Systems
First, we modify the verification strategy so that nodes set a probability of verifying a received transaction considering the likelihood of it being spam: transactions from a node with a low reputation have a high probability of being verified.
Dynamic Budget Throttling in Repeated Second-Price Auctions
Among various budget control methods, throttling has emerged as a popular choice, managing an advertiser's total expenditure by selecting only a subset of auctions to participate in.
Optimal Private Payoff Manipulation against Commitment in Extensive-form Games
For all the settings considered in this paper, we characterize all the possible game outcomes that can be induced successfully.
No-regret Learning in Repeated First-Price Auctions with Budget Constraints
Recently the online advertising market has exhibited a gradual shift from second-price auctions to first-price auctions.
On the Convergence of Fictitious Play: A Decomposition Approach
Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in $n$-player games, which builds the foundation for modern multi-agent learning algorithms.
On Convergence Lemma and Convergence Stability for Piecewise Analytic Functions
An extension of this proof presents a geometric characterization of the set of stationary points of $f$.
A Context-Integrated Transformer-Based Neural Network for Auction Design
One of the central problems in auction design is developing an incentive-compatible mechanism that maximizes the auctioneer's expected revenue.
Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions
Specifically, the results depend on the number of bidders with the highest value: - If the number is at least three, the bidding dynamics almost surely converges to a Nash equilibrium of the auction, both in time-average and in last-iterate.
On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games
Similar to the role of Markov decision processes in reinforcement learning, Stochastic Games (SGs) lay the foundation for the study of multi-agent reinforcement learning (MARL) and sequential agent interactions.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Is Nash Equilibrium Approximator Learnable?
In this paper, we investigate the learnability of the function approximator that approximates Nash equilibrium (NE) for games generated from a distribution.
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling
The Empirical Revenue Maximization (ERM) is one of the most important price learning algorithms in auction design: as the literature shows it can learn approximately optimal reserve prices for revenue-maximizing auctioneers in both repeated auctions and uniform-price auctions.
CycLedger: A Scalable and Secure Parallel Protocol for Distributed Ledger via Sharding
Our protocol selects a leader and a partial set for each committee, who are in charge of maintaining intra-shard consensus and communicating with other committees, to reduce the amortized complexity of communication, computation, and storage on all nodes.
Distributed, Parallel, and Cluster Computing Cryptography and Security
Reinforcement Learning for Uplift Modeling
Uplift modeling aims to directly model the incremental impact of a treatment on an individual response.
Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks
To enhance the emotion discriminativeness of words in textual feature extraction, we propose Emotional Word Embedding (EWE) to learn text representations by jointly considering their semantics and emotions.
Latent Dirichlet Allocation for Internet Price War
Then we develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the current market environment, which represents the preferences of customers and strategies of competitors.
