1 citations · 2 across the 16 of their papers we have counts for
23 papers
Cogentic: Multi-Agent Orchestration for Automated Proof Discovery
Yang Cai, Vineet Gupta, Yanchen Jiang +4
We present Cogentic, a multi-agent harness for automated proof discovery on open research problems. While frontier language models can generate strong mathematical ideas in a singl…
Two-sided Market Design Meets Autobidding
Yang Cai, Christopher Liaw, Aranyak Mehta +2
Autobidding has become a dominant paradigm in online advertising by enabling advertisers to set high-level goals while algorithms handle real-time bid optimization. A prominent exa…
Efficiency of Generalized Proportional First-Price Auctions Under Auto-bidding
Yang Cai, Vineet Gupta, Yanchen Jiang +4
Auto-bidding is now widely adopted in online advertising platforms, allowing advertisers to specify high-level campaign objectives--such as maximizing total value subject to a retu…
Improved Revenue Guarantees for Selling Separately and Bundling
Yang Cai, Vineet Gupta, Yanchen Jiang +4
We study how much revenue a seller can lose by restricting attention to selling separately or grand bundling, in the setting of a single additive buyer with independent item values…
The Power of Recruiting the Smaller Side: Two Additional Traders Suffice in Two-Sided Markets
Yang Cai, Vineet Gupta, Yanchen Jiang +5
We study Bulow-Klemperer-style competition complexity in two-sided double auctions with unit-demand buyers drawn i.i.d. from and unit-supply sellers drawn i.i.d. from…
Prediction with Expert Advice: Anytime Regret with Many Experts Matches the Fixed-Time Constant
Yang Cai, Vineet Gupta, Yanchen Jiang +4
Prediction with expert advice is a fundamental problem in online learning. When the time horizon is known in advance, the minimax cumulative regret over experts is asymptot…
Efficient Online Inverse Optimization with Regret
Yang Cai, Anupam Gupta, Vineet Gupta +7
We give a deterministic algorithm for online inverse linear optimization with regret , uniform in the horizon and time per round. A bound of this order was obtaine…
From Compensation Design to Budget-Feasible Mechanisms: A Constant Approximation for Subadditive Valuations
Ioannis Anagnostides, Kshipra Bhawalkar, Christopher Liaw +3
Budget-feasible mechanism design is a classic framework introduced by Singer, but there is still a wide gap between existing upper and lower bounds. In this paper, we significantly…
Compensation Design
Ioannis Anagnostides, Kshipra Bhawalkar, Christopher Liaw +5
We introduce compensation design, the problem of designing payment rules that incentivize high-quality contributions in decentralized environments. Here, a budget-constrained princ…
One Model, Two Markets: Bid-Aware Generative Recommendation
Yanchen Jiang, Zhe Feng, Christopher P. Mah +2
Generative Recommender Systems using semantic ids, such as TIGER (Rajput et al., 2023), have emerged as a widely adopted competitive paradigm in sequential recommendation. However,…
Scaling Inference-Time Computation via Opponent Simulation: Enabling Online Strategic Adaptation in Repeated Negotiation
Xiangyu Liu, Di Wang, Zhe Feng +1
While large language models (LLMs) have emerged as powerful decision-makers across a wide range of single-agent and stationary environments, fewer efforts have been devoted to sett…
Inference-time Unlearning Using Conformal Prediction
Somnath Basu Roy Chowdhury, Rahul Kidambi, Avinava Dubey +4
Machine unlearning is the process of efficiently removing specific information from a trained machine learning model without retraining from scratch. Existing unlearning methods, w…
Incentive-Aligned Multi-Source LLM Summaries
Yanchen Jiang, Zhe Feng, Aranyak Mehta
Large language models (LLMs) are increasingly used in modern search and answer systems to synthesize multiple, sometimes conflicting, texts into a single response, yet current pipe…
Position Auctions in AI-Generated Content
Santiago Balseiro, Kshipra Bhawalkar, Yuan Deng +7
We consider an extension to the classic position auctions in which sponsored creatives can be added within AI generated content rather than shown in predefined slots. New challenge…
Equilibria and Learning in Modular Marketplaces
Kshipra Bhawalkar, Jeff Dean, Christopher Liaw +2
We envision a marketplace where diverse entities offer specialized "modules" through APIs, allowing users to compose the outputs of these modules for complex tasks within a given b…
Linear Transformer Topological Masking with Graph Random Features
Isaac Reid, Kumar Avinava Dubey, Deepali Jain +12
When training transformers on graph-structured data, incorporating information about the underlying topology is crucial for good performance. Topological masking, a type of relativ…
Auto-bidding and Auctions in Online Advertising: A Survey
Gagan Aggarwal, Ashwinkumar Badanidiyuru, Santiago R. Balseiro +23
In this survey, we summarize recent developments in research fueled by the growing adoption of automated bidding strategies in online advertising. We explore the challenges and opp…
Deep Reinforcement Learning for Sequential Combinatorial Auctions
Sai Srivatsa Ravindranath, Zhe Feng, Di Wang +3
Revenue-optimal auction design is a challenging problem with significant theoretical and practical implications. Sequential auction mechanisms, known for their simplicity and stron…
Conditional Language Policy: A General Framework for Steerable Multi-Objective Finetuning
Kaiwen Wang, Rahul Kidambi, Ryan Sullivan +17
Reward-based finetuning is crucial for aligning language policies with intended behaviors (e.g., creativity and safety). A key challenge is to develop steerable language models tha…
The Average-Value Allocation Problem
Kshipra Bhawalkar, Zhe Feng, Anupam Gupta +3
We initiate the study of centralized algorithms for welfare-maximizing allocation of goods to buyers subject to average-value constraints. We show that this problem is NP-hard to a…
Auctions with LLM Summaries
Kumar Avinava Dubey, Zhe Feng, Rahul Kidambi +2
We study an auction setting in which bidders bid for placement of their content within a summary generated by a large language model (LLM), e.g., an ad auction in which the display…
Ads in Conversations
Martino Banchio, Aranyak Mehta, Andres Perlroth
We study the optimal placement of advertisements for interactive platforms like conversational AI assistants. Importantly, conversations add a feature absent in canonical search ma…
Efficiency of Non-Truthful Auctions in Auto-bidding with Budget Constraints
Christopher Liaw, Aranyak Mehta, Wennan Zhu
We study the efficiency of non-truthful auctions for auto-bidders with both return on spend (ROS) and budget constraints. The efficiency of a mechanism is measured by the price of…