24 citations · 51 across the 32 of their papers we have counts for
12 papers · 1 filter
Learning-Enabled Estimation: Tight Characterizations under Sample Selection Biases
Vikram Kher, Jane H. Lee, Anay Mehrotra +1
When can we learn from biased samples? We study regression when outcomes are observed only after passing through selection filters that depend on both covariates and outcomes thems…
Reasoning with Sampling: Cutting at Decision Points
Felix Zhou, Anay Mehrotra, Quanquan C. Liu
Frontier reasoning models are produced by post-training base language models with reinforcement learning. Recent work has challenged this by showing that sampling from a sharpened…
Mean Estimation from Coarse Data: Characterizations and Efficient Algorithms
Alkis Kalavasis, Anay Mehrotra, Manolis Zampetakis +2
Coarse data arise when learners observe only partial information about samples; namely, a set containing the sample rather than its exact value. This occurs naturally through measu…
On Union-Closedness of Language Generation
Steve Hanneke, Amin Karbasi, Anay Mehrotra +1
We investigate language generation in the limit - a model by Kleinberg and Mullainathan [NeurIPS 2024] and extended by Li, Raman, and Tewari [COLT 2025]. While Kleinberg and Mullai…
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…
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…
Fair Classification with Partial Feedback: An Exploration-Based Data Collection Approach
Vijay Keswani, Anay Mehrotra, L. Elisa Celis
In many predictive contexts (e.g., credit lending), true outcomes are only observed for samples that were positively classified in the past. These past observations, in turn, form…
Tree of Attacks: Jailbreaking Black-Box LLMs Automatically
Anay Mehrotra, Manolis Zampetakis, Paul Kassianik +4
While Large Language Models (LLMs) display versatile functionality, they continue to generate harmful, biased, and toxic content, as demonstrated by the prevalence of human-designe…
Maximizing Submodular Functions for Recommendation in the Presence of Biases
Anay Mehrotra, Nisheeth K. Vishnoi
Subset selection tasks, arise in recommendation systems and search engines and ask to select a subset of items that maximize the value for the user. The values of subsets often dis…
Fair Ranking with Noisy Protected Attributes
Anay Mehrotra, Nisheeth K. Vishnoi
The fair-ranking problem, which asks to rank a given set of items to maximize utility subject to group fairness constraints, has received attention in the fairness, information ret…
Fairness for AUC via Feature Augmentation
Hortense Fong, Vineet Kumar, Anay Mehrotra +1
We study fairness in the context of classification where the performance is measured by the area under the curve (AUC) of the receiver operating characteristic. AUC is commonly use…
Fair Classification with Adversarial Perturbations
L. Elisa Celis, Anay Mehrotra, Nisheeth K. Vishnoi
We study fair classification in the presence of an omniscient adversary that, given an , is allowed to choose an arbitrary -fraction of the training samples and arbitrarily p…