Hugging Face Trending Papers
When Are We Near Nash? Certifying Solutions in General-Sum Games
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arXiv AI
Oct 23
When Are We Near Nash? Certifying Solutions in General-Sum Games
The paper introduces a method for certifying the quality of solutions produced by heuristic solvers for general‑sum extensive‑form games. By relating the Nash gap of a weighted average strategy to external regret and a signed correction term, the authors derive an online certificate that can be computed in linear time per checkpoint. Experiments on the biased Leduc family show that selecting the iterate with the smallest certificate from a portfolio of training‑free regret‑matching solvers achieves high success rates across multiple test sets.
arXiv AI
Sep 18
Efficient Nash Equilibrium Computation for Cybersecurity Games
The paper introduces Regret-Weighted Payoff Sampling (RWPS), a budgeted estimator that selectively simulates only payoff-matrix cells relevant to a Nash equilibrium and uses a surrogate model for the remaining entries. RWPS provides an instance-dependent error bound weighted by the opponent’s equilibrium mixture and a coverage result guaranteeing that, once the deviation-relevant set is simulated, surrogate error does not affect either player’s regret. Experiments on three 21×21 general-sum games, including an asymmetric Colonel Blotto, show that RWPS achieves four to six times tighter bounds than previous methods and outperforms other sampling strategies on the CyGym and ANSG cyber simulators at low budgets.
arXiv Machine Learning
Jul 1
Certified Speculative Execution for Untrusted AI Agents
arXiv:2606. 31023v1 Announce Type: cross Abstract: Hard-constrained sequential decision systems have no certified way to spend the test-time compute of modern AI: executing the multi-step drafts of a learned policy or a frozen LLM forfeits the feasibility guarantee a trusted solver provides, while invoking the solver at every step forfeits the speed the AI offers.
Hugging Face Trending Papers
6d ago
Conservative Dueling Bandits: Preference Learning under Baseline Constraints
Contextual dueling bandits learn from pairwise comparisons, the same feedback used to fine-tune language models from human preferences. Standard algorithms explore aggressively and can show users two...
arXiv AI
5d ago
Conservative Dueling Bandits: Preference Learning under Baseline Constraints
The paper introduces conservative contextual dueling bandits, which enforce that each displayed item’s cumulative score remains above a specified fraction of a trusted baseline’s score. It presents algorithms—MaxInP-BC for linear utilities and BTIGW-BC for general utilities—that guarantee safety with high probability while achieving sublinear regret. Experiments show these methods satisfy the safety constraint more reliably than unconstrained counterparts, with only a moderate increase in regret.
arXiv Machine Learning
Aug 18
CFR without Unbiasedness: Deterministic Guarantees for Persistent Public-Chance Schedules
arXiv:2608. 14761v1 Announce Type: cross Abstract: At a finite public-chance cut, counterfactual regret minimization (CFR) must choose how many outcomes to evaluate before each regret update.