update papers

_bibliography/papers.bib

@@ -1,6 +1,24 @@
 ---
 ---
 
+@article{Decentralized_SOCO_SIGMETRICS_2025,
+  abbr={SIGMETRICS},
+  title={Learning-Augmented Decentralized Online Convex Optimization in Networks},
+  author={Pengfei Li and Jianyi Yang and Adam Wierman and Shaolei Ren},
+  abstract={This paper studies learning-augmented decentralized online convex optimization in a networked multi-agent system, a challenging setting that has remained under-explored. We first consider a linear learning-augmented decentralized online algorithm (LADO-Lin) that combines a machine learning (ML) policy with a baseline expert policy in a linear manner. We show that, while LADO-Lin can exploit the potential of ML predictions to improve the average cost performance, it cannot have guaranteed worst-case performance. To address this limitation, we propose a novel online algorithm (LADO) that adaptively combines the ML policy and expert policy to safeguard the ML predictions to achieve strong competitiveness guarantees. We also prove the average cost bound for LADO, revealing the tradeoff between average performance and worst-case robustness and demonstrating the advantage of training the ML policy by explicitly considering the robustness requirement. Finally, we run an experiment on decentralized battery management for sustainable computing. Our results highlight the potential of ML augmentation to improve the average performance as well as the guaranteed worst-case performance of LADO.},
+  journal={SIGMETRICS},
+  month = {},
+  year={2025},
+  url={https://arxiv.org/abs/2306.10158},
+  html={https://arxiv.org/abs/2306.10158},
+  bibtex_show = {true},
+  selected={true},
+  recenthighlights={true},
+  topic = {green},
+  timerange = {21to25},
+  show = {true}
+}
+
 @article{AI_Water_CACM_2024,
   abbr={CACM},
   title={Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models},
@@ -19,6 +37,25 @@ @article{AI_Water_CACM_2024
   show = {true}
 }
 
+@article{LLM_Watermark_Spoofing_NeurIPS_2024,
+  abbr={NeurIPS},
+  title={Bileve: Securing Text Provenance in Large Language Models Against Spoofing with Bi-level Signature},
+  author={Tong Zhou and Xuandong Zhao and Xiaolin Xu and Shaolei Ren},
+  abstract={Text watermarks for large language models (LLMs) have been commonly used to identify the origins of machine-generated content, which is promising for assessing liability when combating deepfake or harmful content. While existing watermarking techniques typically prioritize robustness against removal attacks, unfortunately, they are vulnerable to spoofing attacks: malicious actors can subtly alter the meanings of LLM-generated responses or even forge harmful content, potentially misattributing blame to the LLM developer. To overcome this, we introduce a bi-level signature scheme, Bileve, which embeds fine-grained signature bits for integrity checks (mitigating spoofing attacks) as well as a coarse-grained signal to trace text sources when the signature is invalid (enhancing detectability) via a novel rank-based sampling strategy. Compared to conventional watermark detectors that only output binary results, Bileve can differentiate 5 scenarios during detection, reliably tracing text provenance and regulating LLMs. The experiments conducted on OPT-1.3B and LLaMA-7B demonstrate the effectiveness of Bileve in defeating spoofing attacks with enhanced detectability.},
+  journal={NeruIPS},
+  month = {},
+  year={2024},
+  url={https://arxiv.org/abs/2406.01946},
+  html={https://arxiv.org/abs/2406.01946},
+  bibtex_show = {true},
+  selected={true},
+  recenthighlights={true},
+  topic = {security},
+  timerange = {21to25},
+  show = {true}
+}
+
+
 @article{SociallyEquitablePUblicModel_ICML_2024,
   abbr={ICML},
   title={Building Socially-Equitable Public Models},
@@ -57,6 +94,43 @@ @article{OnlineAllocation_Replenish_SIGMETRICS_2024
   show = {true}
 }
 
+@article{OnlineBudgetedMatching_General_NeurIPS_2024,
+  abbr={NeurIPS},
+  title={Online Budgeted Matching with General Bids},
+  author={Jianyi Yang and Pengfei Li and Adam Wierman and Shaolei Ren},
+  abstract={Online Budgeted Matching (OBM) is a classic problem with important applications in online advertising, online service matching, revenue management, and beyond. Traditional online algorithms typically assume a small bid setting, where the maximum bid-to-budget ratio (kappa) is infinitesimally small. While recent algorithms have tried to address scenarios with non-small or general bids, they often rely on the Fractional Last Matching (FLM) assumption, which allows for accepting partial bids when the remaining budget is insufficient. This assumption, however, does not hold for many applications with indivisible bids. In this paper, we remove the FLM assumption and tackle the open problem of OBM with general bids. We first establish an upper bound of 1-kappa
+ on the competitive ratio for any deterministic online algorithm. We then propose a novel meta algorithm, called MetaAd, which reduces to different algorithms with first known provable competitive ratios parameterized by the maximum bid-to-budget ratio kappa in [0,1]. As a by-product, we extend MetaAd to the FLM setting and get provable competitive algorithms. Finally, we apply our competitive analysis to the design learning-augmented algorithms.},
+  journal={NeurIPS},
+  month = {},
+  year={2024},
+  url={https://shaoleiren.github.io/},
+  html={https://shaoleiren.github.io/},
+  bibtex_show = {true},
+  selected={true},
+  recenthighlights={true},
+  topic = {green},
+  timerange = {21to25},
+  show = {true}
+}
+
+
+@article{OnlineBudgetedMatching_General_NeurIPS_2024,
+  abbr={NeurIPS},
+  title={Safe Exploitative Play in Stochastic Bayesian Games with Untrusted Type Beliefs},
+  author={Tongxin Li and Tinashe Handina and Shaolei Ren and Adam Wierman},
+  abstract={The combination of the Bayesian game and learning has a rich history, with the idea of controlling a single agent in a system composed of multiple agents with unknown behaviors given a set of types, each specifying a possible behavior for the other agents. The idea is to plan an agent's own actions with respect to those types which it believes are most likely to maximize the payoff. However, the type beliefs are often learned from past actions and likely to be incorrect. With this perspective in mind, we consider an agent in a game with type predictions of other components, and investigate the impact of incorrect beliefs to the agent’s payoff. In particular, we formally define a trade-off between risk and opportunity by comparing the payoff obtained against the optimal payoff, which is represented by a gap caused by trusting or distrusting the learned beliefs. Our main results characterize the trade-off by providing upper and lower bounds on the payoff gap for both normal-form and stochastic Bayesian games, with numerical results provided.},
+  journal={NeurIPS},
+  month = {},
+  year={2024},
+  url={https://shaoleiren.github.io/},
+  html={https://shaoleiren.github.io/},
+  bibtex_show = {true},
+  selected={true},
+  recenthighlights={false},
+  topic = {others},
+  timerange = {21to25},
+  show = {true}
+}
 
 
 @article{Environmentally_Equitable_AI_eEnergy_2024,

_layouts/about.html

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+          <div class="row" style="margin-bottom: 8px;"> 
+              <div class="col-xs-1 cl-sm-1 col-md-1 text-center" style="width: 30px;">
+                <span class="badge font-weight-bold stylish-color text-uppercase align-middle" style="min-width: 30px;">
+                 09/2024
+                </span>
+              </div>
+              <div class="col-xs-11 cl-sm-11 col-md-11 mt-2 mt-md-0">
+               I'm glad to share four recent papers accepted by <b><a href="https://www.sigmetrics.org/sigmetrics2025/">SIGMETRICS'25</a></b> and <b><a href="https://neurips.cc/Conferences/2024">NeurIPS'24</a></b>. 
+               (1) The SIGMETRICS'25 paper <a href="https://arxiv.org/abs/2306.10158">"Learning-Augmented Decentralized Online Convex Optimization in Networks"</a>  proposes a novel algorithm to provably robustify machine learning predictions for decentralized optimization in networks.
+               (2) The NeurIPS'24 paper <a href="https://arxiv.org/abs/2406.01946">"Bileve: Securing Text Provenance in Large Language Models Against Spoofing with Bi-level Signature"</a> proposes a novel anti-spoofing watermarking method to safeguard LLM-generated content. (3) The NeurIPS'24 paper <a href="/">"Online Budgeted Matching with General Bids"</a> proposes provably-competitive online algorithms for budgeted matching with arbitrary bid sizes. (4) The NeurIPS'24 paper <a href="/">"Safe Exploitative Play in Stochastic Bayesian Games with Untrusted Type Beliefs"</a>  quantifies the impact of potentially incorrect type beliefs on an agent's payoff in Bayesian games.
+              </div>
+          </div>
+
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@@ -186,7 +199,7 @@ <h3>news</h3>
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