2 citations · 8 across the 30 of their papers we have counts for
9 papers · 1 filter
On Characterizations for Language Generation: Interplay of Hallucinations, Breadth, and Stability
Alkis Kalavasis, Anay Mehrotra, Grigoris Velegkas
We study language generation in the limit - introduced by Kleinberg and Mullainathan [KM24] - building on classical works of Gold [Gol67] and Angluin [Ang79]. [KM24]'s main result…
Procurement Auctions via Approximately Optimal Submodular Optimization
Yuan Deng, Amin Karbasi, Vahab Mirrokni +3
We study procurement auctions, where an auctioneer seeks to acquire services from strategic sellers with private costs. The quality of services is measured by a submodular function…
Randomized Truthful Auctions with Learning Agents
Gagan Aggarwal, Anupam Gupta, Andres Perlroth +1
We study a setting where agents use no-regret learning algorithms to participate in repeated auctions. \citet{kolumbus2022auctions} showed, rather surprisingly, that when bidders p…
On the Limits of Language Generation: Trade-Offs Between Hallucination and Mode Collapse
Alkis Kalavasis, Anay Mehrotra, Grigoris Velegkas
Specifying all desirable properties of a language model is challenging, but certain requirements seem essential. Given samples from an unknown language, the trained model should pr…
Understanding Aggregations of Proper Learners in Multiclass Classification
Julian Asilis, Mikael Møller Høgsgaard, Grigoris Velegkas
Multiclass learnability is known to exhibit a properness barrier: there are learnable classes which cannot be learned by any proper learner. Binary classification faces no such bar…
Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models
Alkis Kalavasis, Amin Karbasi, Argyris Oikonomou +3
As ML models become increasingly complex and integral to high-stakes domains such as finance and healthcare, they also become more susceptible to sophisticated adversarial attacks.…
On the Computational Landscape of Replicable Learning
Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas +1
We study computational aspects of algorithmic replicability, a notion of stability introduced by Impagliazzo, Lei, Pitassi, and Sorrell [2022]. Motivated by a recent line of work t…
Pointwise Lipschitz Continuous Graph Algorithms
Quanquan C. Liu, Grigoris Velegkas, Yuichi Yoshida +1
In many real-world applications, it is undesirable to drastically change the problem solution after a small perturbation in the input, as unstable outputs can lead to costly transa…
Replicable Learning of Large-Margin Halfspaces
Alkis Kalavasis, Amin Karbasi, Kasper Green Larsen +2
We provide efficient replicable algorithms for the problem of learning large-margin halfspaces. Our results improve upon the algorithms provided by Impagliazzo, Lei, Pitassi, and S…