3 citations · 5 across the 9 of their papers we have counts for
9 papers
AI Cap-and-Trade: Efficiency Incentives for Accessibility and Sustainability
Marco Bornstein, Amrit Singh Bedi
The race for artificial intelligence (AI) dominance often prioritizes scale over efficiency. Hyper-scaling is the common industry approach: larger models, more data, and as many co…
Auction-Based Regulation for Artificial Intelligence
Marco Bornstein, Zora Che, Suhas Julapalli +3
In an era of "moving fast and breaking things", regulators have moved slowly to pick up the safety, bias, and legal debris left in the wake of broken Artificial Intelligence (AI) d…
FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?
Marco Bornstein, Amrit Singh Bedi, Abdirisak Mohamed +1
Standard federated learning (FL) approaches are vulnerable to the free-rider dilemma: participating agents can contribute little to nothing yet receive a well-trained aggregated mo…
Towards Realistic Mechanisms That Incentivize Federated Participation and Contribution
Marco Bornstein, Amrit Singh Bedi, Anit Kumar Sahu +2
Edge device participation in federating learning (FL) is typically studied through the lens of device-server communication (e.g., device dropout) and assumes an undying desire from…
Large-Scale Distributed Learning via Private On-Device Locality-Sensitive Hashing
Tahseen Rabbani, Marco Bornstein, Furong Huang
Locality-sensitive hashing (LSH) based frameworks have been used efficiently to select weight vectors in a dense hidden layer with high cosine similarity to an input, enabling dyna…
Escaping From Saddle Points Using Asynchronous Coordinate Gradient Descent
Marco Bornstein, Jin-Peng Liu, Jingling Li +1
Large-scale non-convex optimization problems are expensive to solve due to computational and memory costs. To reduce the costs, first-order (computationally efficient) and asynchro…
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
Marco Bornstein, Tahseen Rabbani, Evan Wang +2
The decentralized Federated Learning (FL) setting avoids the role of a potentially unreliable or untrustworthy central host by utilizing groups of clients to collaboratively train…
Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching
Tahseen Rabbani, Brandon Feng, Marco Bornstein +5
Federated learning (FL) is a popular paradigm for private and collaborative model training on the edge. In centralized FL, the parameters of a global architecture (such as a deep n…
Beating the House: Identifying Inefficiencies in Sports Betting Markets
Sathya Ramesh, Ragib Mostofa, Marco Bornstein +1
Inefficient markets allow investors to consistently outperform the market. To demonstrate that inefficiencies exist in sports betting markets, we created a betting algorithm that g…