6 citations · 8 across the 14 of their papers we have counts for
15 papers
Panprediction: Optimal Predictions for Any Downstream Task and Loss
Sivaraman Balakrishnan, Nika Haghtalab, Daniel Hsu +2
Supervised learning is classically formulated as training a model to minimize a fixed loss function over a fixed distribution, or task. However, an emerging paradigm instead views…
The Limits of Preference Data for Post-Training
Eric Zhao, Jessica Dai, Pranjal Awasthi
Recent progress in strengthening the capabilities of large language models has stemmed from applying reinforcement learning to domains with automatically verifiable outcomes. A key…
From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning
Eric Zhao, Pranjal Awasthi, Nika Haghtalab
Finetuning provides a scalable and cost-effective means of customizing language models for specific tasks or response styles, with greater reliability than prompting or in-context…
Truthfulness of Decision-Theoretic Calibration Measures
Mingda Qiao, Eric Zhao
Calibration measures quantify how much a forecaster's predictions violates calibration, which requires that forecasts are unbiased conditioning on the forecasted probabilities. Two…
Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification
Eric Zhao, Pranjal Awasthi, Sreenivas Gollapudi
Sampling-based search, a simple paradigm for utilizing test-time compute, involves generating multiple candidate responses and selecting the best one -- typically by having models…
Relational Programming with Foundation Models
Ziyang Li, Jiani Huang, Jason Liu +6
Foundation models have vast potential to enable diverse AI applications. The powerful yet incomplete nature of these models has spurred a wide range of mechanisms to augment them w…
Learning Variational Inequalities from Data: Fast Generalization Rates under Strong Monotonicity
Eric Zhao, Tatjana Chavdarova, Michael Jordan
Variational inequalities (VIs) are a broad class of optimization problems encompassing machine learning problems ranging from standard convex minimization to more complex scenarios…
Learning With Multi-Group Guarantees For Clusterable Subpopulations
Jessica Dai, Nika Haghtalab, Eric Zhao
A canonical desideratum for prediction problems is that performance guarantees should hold not just on average over the population, but also for meaningful subpopulations within th…
Algorithmic Content Selection and the Impact of User Disengagement
Emilio Calvano, Nika Haghtalab, Ellen Vitercik +1
Digital services face a fundamental trade-off in content selection: they must balance the immediate revenue gained from high-reward content against the long-term benefits of mainta…
Truthfulness of Calibration Measures
Nika Haghtalab, Mingda Qiao, Kunhe Yang +1
We initiate the study of the truthfulness of calibration measures in sequential prediction. A calibration measure is said to be truthful if the forecaster (approximately) minimizes…
Stacking as Accelerated Gradient Descent
Naman Agarwal, Pranjal Awasthi, Satyen Kale +1
Stacking, a heuristic technique for training deep residual networks by progressively increasing the number of layers and initializing new layers by copying parameters from older la…
A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning
Nika Haghtalab, Michael I. Jordan, Eric Zhao
We provide a unifying framework for the design and analysis of multicalibrated predictors. By placing the multicalibration problem in the general setting of multi-objective learnin…
On-Demand Sampling: Learning Optimally from Multiple Distributions
Nika Haghtalab, Michael I. Jordan, Eric Zhao
Social and real-world considerations such as robustness, fairness, social welfare and multi-agent tradeoffs have given rise to multi-distribution learning paradigms, such as collab…
Learning to Play General-Sum Games Against Multiple Boundedly Rational Agents
Eric Zhao, Alexander R. Trott, Caiming Xiong +1
We study the problem of training a principal in a multi-agent general-sum game using reinforcement learning (RL). Learning a robust principal policy requires anticipating the worst…
Active Learning under Label Shift
Eric Zhao, Anqi Liu, Animashree Anandkumar +1
We address the problem of active learning under label shift: when the class proportions of source and target domains differ. We introduce a "medial distribution" to incorporate a t…