arXiv Machine Learning
Jul 15
Efficient Sequential Calibration with $O(T^{2/3-\epsilon})$ Error Bound
arXiv:2607. 12928v1 Announce Type: new Abstract: We study the online binary sequential calibration problem.
Breaking the $T^{2/3}$ Barrier for Sequential Calibration
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arXiv Machine Learning
Jul 23
Optimal Recalibration of an Online Predictor
arXiv:2607. 19689v1 Announce Type: cross Abstract: We study the problem of recalibrating an online predictor [KE17, OKS24]: given an arbitrary "hint" sequence of forecasts, the learner must output new predictions that are calibrated while incurring small excess error relative to the original forecasts, under a proper loss.
arXiv Machine Learning
Aug 12
High-Dimensional Calibration from Swap Regret
arXiv:2505. 21460v2 Announce Type: replace Abstract: We study online calibration of multi-dimensional forecasts over an arbitrary convex set $P \subset \mathbb{R}^d$ relative to an arbitrary norm $|\cdot|$.
arXiv Machine Learning
Jul 10
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
arXiv:2306. 02704v2 Announce Type: replace-cross Abstract: We introduce \emph{Calibrated Stackelberg Games (CSGs)}, a generalization of the standard Stackelberg Games (SGs) framework.
arXiv Machine Learning
Jun 17
On Randomized Algorithms in Online Strategic Classification
arXiv:2602. 06257v2 Announce Type: replace Abstract: Online strategic classification studies settings in which agents strategically modify their features to obtain favorable predictions.
arXiv Machine Learning
Jul 23
Breaking the $T^{3/4}$ Barrier for Regret Minimization With Bi-Dimensional CDFs
arXiv:2607. 20258v1 Announce Type: new Abstract: We study regret minimization for learning CDF-related objectives of the form \[ g(x)\cdot\mathbb{P}_{X\sim\mathcal{D}}(X\le x), \] over $[0,1]^2$, where $g$ is a known Lipschitz function and $\mathcal{D}$ is an unknown distribution.
arXiv Machine Learning
Jun 2
Online Packet Scheduling with Deadlines and Learning
arXiv:2606. 00835v1 Announce Type: new Abstract: Network routers that enforce Quality-of-Service (QoS) guarantees must decide, at every clock cycle, which expiring packet of information to transmit, even when the value of the packet is unknown until it is processed.
arXiv Machine Learning
Jun 30
Improved Multi-Dimensional Forecasting for Swap Regret
arXiv:2606. 29533v1 Announce Type: cross Abstract: We study the problem of forecasting for an arbitrary number of downstream agents with unknown objectives, each of whom best responds to the forecaster's predictions.
arXiv Machine Learning
Aug 26
Optimal Alternating Regret for Online Learning and Games
arXiv:2608.24731v1 Announce Type: new Abstract: We settle the minimax-optimal alternating regret, a regret notion motivated by alternating learning dynamics in games, for both online linear optimizat...
Hugging Face Trending Papers
Sep 9
A Sharp Barrier for Consistent Submodular Maximization: Any Improvement over $2-\sqrt{2}$ Entails Exponential Queries or Linear Recourse
Consistent submodular maximization studies the tradeoff between solution quality and stability when elements arrive over time. For a monotone submodular objective, which models diminishing returns, an...
arXiv AI
Jul 17
When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models
arXiv:2607. 14169v1 Announce Type: new Abstract: Large language models can synthesize a game's rules as executable code - a Code World Model (CWM) - which a classical planner then searches over.
arXiv Machine Learning
Jun 18
Toward Simultaneously Optimal Regret in U-Calibration
arXiv:2606. 18527v1 Announce Type: cross Abstract: U-calibration studies online forecasting algorithms whose predictions can be consumed by any unknown downstream agent, guaranteeing sublinear regret simultaneously for all proper loss functions.