Robust Decision Aggregation with Second-order Information
- URL: http://arxiv.org/abs/2311.14094v1
- Date: Thu, 23 Nov 2023 16:39:55 GMT
- Title: Robust Decision Aggregation with Second-order Information
- Authors: Yuqi Pan, Zhaohua Chen, Yuqing Kong
- Abstract summary: We consider a decision aggregation problem with two experts who each make a binary recommendation after observing a private signal.
An agent, who does not know the joint information structure between signals and states, sees the experts' recommendations and aims to match the action with the true state.
Under the scenario, we study whether supplemented additionally with second-order information could enable a better aggregation.
- Score: 4.021926055330022
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We consider a decision aggregation problem with two experts who each make a
binary recommendation after observing a private signal about an unknown binary
world state. An agent, who does not know the joint information structure
between signals and states, sees the experts' recommendations and aims to match
the action with the true state. Under the scenario, we study whether
supplemented additionally with second-order information (each expert's forecast
on the other's recommendation) could enable a better aggregation.
We adopt a minimax regret framework to evaluate the aggregator's performance,
by comparing it to an omniscient benchmark that knows the joint information
structure. With general information structures, we show that second-order
information provides no benefit. No aggregator can improve over a trivial
aggregator, which always follows the first expert's recommendation. However,
positive results emerge when we assume experts' signals are conditionally
independent given the world state. When the aggregator is deterministic, we
present a robust aggregator that leverages second-order information, which can
significantly outperform counterparts without it. Second, when two experts are
homogeneous, by adding a non-degenerate assumption on the signals, we
demonstrate that random aggregators using second-order information can surpass
optimal ones without it. In the remaining settings, the second-order
information is not beneficial. We also extend the above results to the setting
when the aggregator's utility function is more general.
Related papers
- Robust Decision Aggregation with Adversarial Experts [4.021926055330022]
We consider a robust aggregation problem in the presence of both truthful and adversarial experts.
We aim to find the optimal aggregator that outputs a forecast minimizing regret under the worst information structure and adversarial strategies.
arXiv Detail & Related papers (2024-03-13T03:47:08Z) - Algorithmic Robust Forecast Aggregation [10.368399274445034]
Given a family of information structures, robust forecast aggregation aims to find the aggregator with minimal-case regret.
Our framework provides efficient approximation schemes for general information aggregation with a finite family of possible information structures.
arXiv Detail & Related papers (2024-01-31T11:02:45Z) - Pure Exploration under Mediators' Feedback [63.56002444692792]
Multi-armed bandits are a sequential-decision-making framework, where, at each interaction step, the learner selects an arm and observes a reward.
We consider the scenario in which the learner has access to a set of mediators, each of which selects the arms on the agent's behalf according to a and possibly unknown policy.
We propose a sequential decision-making strategy for discovering the best arm under the assumption that the mediators' policies are known to the learner.
arXiv Detail & Related papers (2023-08-29T18:18:21Z) - Cluster-guided Contrastive Graph Clustering Network [53.16233290797777]
We propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC)
We construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks.
To construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples.
arXiv Detail & Related papers (2023-01-03T13:42:38Z) - Recommendation Systems with Distribution-Free Reliability Guarantees [83.80644194980042]
We show how to return a set of items rigorously guaranteed to contain mostly good items.
Our procedure endows any ranking model with rigorous finite-sample control of the false discovery rate.
We evaluate our methods on the Yahoo! Learning to Rank and MSMarco datasets.
arXiv Detail & Related papers (2022-07-04T17:49:25Z) - Learning Weakly-Supervised Contrastive Representations [104.42824068960668]
We present a two-stage weakly-supervised contrastive learning approach.
The first stage is to cluster data according to its auxiliary information.
The second stage is to learn similar representations within the same cluster and dissimilar representations for data from different clusters.
arXiv Detail & Related papers (2022-02-14T12:57:31Z) - Are You Smarter Than a Random Expert? The Robust Aggregation of
Substitutable Signals [14.03122229316614]
This paper initiates the study of forecast aggregation in a context where experts' knowledge is chosen adversarially from a broad class of information structures.
Under the projective substitutes condition, taking the average of the experts' forecasts improves substantially upon the strategy of trusting a random expert.
We show that by averaging the experts' forecasts and then emphextremizing the average by moving it away from the prior by a constant factor, the aggregator's performance guarantee is substantially better than is possible without knowledge of the prior.
arXiv Detail & Related papers (2021-11-04T20:50:30Z) - On component interactions in two-stage recommender systems [82.38014314502861]
Two-stage recommenders are used by many online platforms, including YouTube, LinkedIn, and Pinterest.
We show that interactions between the ranker and the nominators substantially affect the overall performance.
In particular, using a Mixture-of-Experts approach, we train the nominators to specialize on different subsets of the item pool.
arXiv Detail & Related papers (2021-06-28T20:53:23Z) - A Duet Recommendation Algorithm Based on Jointly Local and Global
Representation Learning [15.942495330390463]
We propose a knowledge-aware-based recommendation algorithm to capture the local and global representation learning from heterogeneous information.
Based on the method that local and global representations are learned jointly by graph convolutional networks with attention mechanism, the final recommendation probability is calculated by a fully-connected neural network.
arXiv Detail & Related papers (2020-12-03T01:52:14Z) - Dual Adversarial Auto-Encoders for Clustering [152.84443014554745]
We propose Dual Adversarial Auto-encoder (Dual-AAE) for unsupervised clustering.
By performing variational inference on the objective function of Dual-AAE, we derive a new reconstruction loss which can be optimized by training a pair of Auto-encoders.
Experiments on four benchmarks show that Dual-AAE achieves superior performance over state-of-the-art clustering methods.
arXiv Detail & Related papers (2020-08-23T13:16:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.