AI Summary

• Buy-now discount: a search deterrence instrument that offers a discounted price to the first customer. [3]
• Search cost: the cost incurred by buyers when searching for information about products or services. [3]
• Commitment leads to competition for being visited first, making price discrimination less likely to occur (and if it occurs, its extent is smaller) even when search costs are positive. [2]

Abstract
When customers must visit a seller to learn the valuation of its product, sellers potentially benefit from charging a lower price on the first visit and a higher price when a buyer returns. Armstrong and Zhou (2016) show that such price discrimination can arise in equilibrium when buyers learn a seller's pricing policy only upon visiting. We depart from this assumption by supposing that sellers commit to observable pricing policies that guide consumer search and buyers can choose whom to visit first. We show that no seller engages in price discrimination in equilibrium.

Why we think this paper is great for you:
This paper explores price dynamics, a key factor in understanding customer behavior and optimizing marketing channel strategies. Understanding how to influence initial pricing can directly relate to strategies for maximizing value in paid search environments.

AI Summary

• The paper presents a new auction mechanism, called the Super Second-Price (SSP) auction, which is designed to be robust against strategic behavior by bidders. [2]

Abstract
We study a model of auction representative of the 5G auction in France. We determine the optimal strategy of a bidder, assuming that the valuations of competitors are unknown to this bidder and that competitors adopt the straightforward bidding strategy. Our model is based on a Partially Observable Markov Decision Process (POMDP). This POMDP admits a concise statistics, avoiding the solution of a dynamic programming equation in the space of beliefs. In addition, under this optimal strategy, the expected gain of the bidder does not decrease if competitors deviate from straightforward bidding. We illustrate our results by numerical experiments, comparing the value of the bidder with the value of a perfectly informed one.

Why we think this paper is great for you:
The research focuses on auction strategies, a core component of bidding and optimization. Analyzing competitive bidding models is highly relevant to understanding and improving performance in various auction-based systems.

Abstract
We study optimal auction design in an independent private values environment where bidders can endogenously -- but at a cost -- improve information about their own valuations. The optimal mechanism is two-stage: at stage-1 bidders register an information acquisition plan and pay a transfer; at stage-2 they bid, and allocation and payments are determined. We show that the revenue-optimal stage-2 rule is the Vickrey--Clarke--Groves (VCG) mechanism, while stage-1 transfers implement the optimal screening of types and absorb information rents consistent with incentive compatibility and participation. By committing to VCG ex post, the pre-auction information game becomes a potential game, so equilibrium information choices maximize expected welfare; the stage-1 fee schedule then transfers an optimal amount of payoff without conditioning on unverifiable cost scales. The design is robust to asymmetric primitives and accommodates a wide range of information technologies, providing a simple implementation that unifies efficiency and optimal revenue in environments with endogenous information acquisition.

Why we think this paper is great for you:
This paper investigates optimal auction mechanisms, directly impacting bidding strategies and resource allocation. Understanding cost-effective learning within auctions is crucial for efficient bidding decisions.

AI Summary

• Diffusion models: a type of generative model that learns to transform a noise signal into a data distribution. [3]
• Requires large amounts of data and computational resources. [3]
• It also mentions the work of Jason Wei et al. [3]
• The paper discusses the use of diffusion models for image synthesis and text-to-image translation. [2]
• (2022) on chain-of-thought prompting elicits reasoning in large language models. [1]

Abstract
Visual concept personalization aims to transfer only specific image attributes, such as identity, expression, lighting, and style, into unseen contexts. However, existing methods rely on holistic embeddings from general-purpose image encoders, which entangle multiple visual factors and make it difficult to isolate a single attribute. This often leads to information leakage and incoherent synthesis. To address this limitation, we introduce Omni-Attribute, the first open-vocabulary image attribute encoder designed to learn high-fidelity, attribute-specific representations. Our approach jointly designs the data and model: (i) we curate semantically linked image pairs annotated with positive and negative attributes to explicitly teach the encoder what to preserve or suppress; and (ii) we adopt a dual-objective training paradigm that balances generative fidelity with contrastive disentanglement. The resulting embeddings prove effective for open-vocabulary attribute retrieval, personalization, and compositional generation, achieving state-of-the-art performance across multiple benchmarks.

Why we think this paper is great for you:
The research explores visual concept personalization, which aligns with efforts to tailor experiences based on visual attributes. This is relevant to understanding how to personalize marketing channels and improve customer engagement.

AI Summary

• The paper presents a new method for attributing model behavior to individual training examples, called TRAK (Tracing Gradient Descent). [3]
• TRAK is based on the idea of tracing the gradient descent process during training and using this information to compute influence scores. [3]
• They also introduce a new metric, called Natural Lipschitz, which provides a more accurate bound on the influence of individual examples. [3]
• TRAK: Tracing Gradient Descent Natural Lipschitz: A new metric for bounding the influence of individual examples The paper presents a new method for attributing model behavior to individual training examples and introduces a new metric for bounding their influence. [3]
• TRAK is effective in identifying important training examples and understanding how they contribute to the model's behavior. [3]
• Natural Lipschitz provides a more accurate bound on the influence of individual examples. [3]
• The authors show that TRAK can be used to identify important training examples and understand how they contribute to the model's behavior. [2]

Abstract
Data attribution methods identify which training examples are responsible for a model's predictions, but their sensitivity to distributional perturbations undermines practical reliability. We present a unified framework for certified robust attribution that extends from convex models to deep networks. For convex settings, we derive Wasserstein-Robust Influence Functions (W-RIF) with provable coverage guarantees. For deep networks, we demonstrate that Euclidean certification is rendered vacuous by spectral amplification -- a mechanism where the inherent ill-conditioning of deep representations inflates Lipschitz bounds by over $10{,}000\times$. This explains why standard TRAK scores, while accurate point estimates, are geometrically fragile: naive Euclidean robustness analysis yields 0\% certification. Our key contribution is the Natural Wasserstein metric, which measures perturbations in the geometry induced by the model's own feature covariance. This eliminates spectral amplification, reducing worst-case sensitivity by $76\times$ and stabilizing attribution estimates. On CIFAR-10 with ResNet-18, Natural W-TRAK certifies 68.7\% of ranking pairs compared to 0\% for Euclidean baselines -- to our knowledge, the first non-vacuous certified bounds for neural network attribution. Furthermore, we prove that the Self-Influence term arising from our analysis equals the Lipschitz constant governing attribution stability, providing theoretical grounding for leverage-based anomaly detection. Empirically, Self-Influence achieves 0.970 AUROC for label noise detection, identifying 94.1\% of corrupted labels by examining just the top 20\% of training data.

Why we think this paper is great for you:
This paper addresses the reliability of data attribution methods, a critical concern for understanding and trusting model outputs. Robust attribution is essential for accurate measurement of marketing channel effectiveness.

AI Summary

• Buy-now discount: a search deterrence instrument that offers a discounted price to the first customer. [3]
• Search cost: the cost incurred by buyers when searching for information about products or services. [3]
• Commitment leads to competition for being visited first, making price discrimination less likely to occur (and if it occurs, its extent is smaller) even when search costs are positive. [2]

Abstract
When customers must visit a seller to learn the valuation of its product, sellers potentially benefit from charging a lower price on the first visit and a higher price when a buyer returns. Armstrong and Zhou (2016) show that such price discrimination can arise in equilibrium when buyers learn a seller's pricing policy only upon visiting. We depart from this assumption by supposing that sellers commit to observable pricing policies that guide consumer search and buyers can choose whom to visit first. We show that no seller engages in price discrimination in equilibrium.

Why we think this paper is great for you:
The research examines price competition, a key driver of customer behavior and channel selection. Understanding these dynamics is fundamental to optimizing marketing channel strategies and bidding decisions.

AI Summary

• Diffusion models: a type of generative model that learns to transform a noise signal into a data distribution. [3]
• Requires large amounts of data and computational resources. [3]
• It also mentions the work of Jason Wei et al. [3]
• The paper discusses the use of diffusion models for image synthesis and text-to-image translation. [2]
• (2022) on chain-of-thought prompting elicits reasoning in large language models. [1]

Abstract
Visual concept personalization aims to transfer only specific image attributes, such as identity, expression, lighting, and style, into unseen contexts. However, existing methods rely on holistic embeddings from general-purpose image encoders, which entangle multiple visual factors and make it difficult to isolate a single attribute. This often leads to information leakage and incoherent synthesis. To address this limitation, we introduce Omni-Attribute, the first open-vocabulary image attribute encoder designed to learn high-fidelity, attribute-specific representations. Our approach jointly designs the data and model: (i) we curate semantically linked image pairs annotated with positive and negative attributes to explicitly teach the encoder what to preserve or suppress; and (ii) we adopt a dual-objective training paradigm that balances generative fidelity with contrastive disentanglement. The resulting embeddings prove effective for open-vocabulary attribute retrieval, personalization, and compositional generation, achieving state-of-the-art performance across multiple benchmarks.

Why we think this paper is great for you:
The paper’s focus on visual attribute encoding directly relates to personalization efforts, allowing for more targeted and effective marketing campaigns.