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| 1 | +<!DOCTYPE html> |
| 2 | +<html lang="en"> |
| 3 | +<head> |
| 4 | + <meta charset="UTF-8" /> |
| 5 | + <meta name="viewport" content="width=device-width, initial-scale=1" /> |
| 6 | + <title>Yunzhen Yao</title> |
| 7 | + <meta name="description" content="Yunzhen Yao — PhD student at EPFL." /> |
| 8 | + |
| 9 | + <!-- |
| 10 | + Template style goals: |
| 11 | + - Minimal, content-first layout like lilianweng.github.io (clean typography + simple top nav) |
| 12 | + - Single-page academic content blocks like jessieren.github.io (bio + news + publications) |
| 13 | + - No build system required: works as a standalone index.html for GitHub Pages |
| 14 | + --> |
| 15 | + |
| 16 | + <style> |
| 17 | + :root{ |
| 18 | + --bg:#ffffff; |
| 19 | + --fg:#111111; |
| 20 | + --muted:#666666; |
| 21 | + --link:#1a73e8; |
| 22 | + --border:#e8e8e8; |
| 23 | + --card:#fafafa; |
| 24 | + --code:#f5f5f5; |
| 25 | + --maxw: 860px; |
| 26 | + } |
| 27 | + @media (prefers-color-scheme: dark){ |
| 28 | + :root{ |
| 29 | + --bg:#0b0f14; |
| 30 | + --fg:#eaecef; |
| 31 | + --muted:#a8b3bf; |
| 32 | + --link:#7ab7ff; |
| 33 | + --border:#1f2a35; |
| 34 | + --card:#0f1620; |
| 35 | + --code:#111b27; |
| 36 | + } |
| 37 | + } |
| 38 | + |
| 39 | + html, body { height: 100%; } |
| 40 | + body{ |
| 41 | + margin:0; |
| 42 | + background:var(--bg); |
| 43 | + color:var(--fg); |
| 44 | + font-family: -apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,Helvetica,Arial,"Noto Sans",sans-serif; |
| 45 | + line-height:1.65; |
| 46 | + -webkit-font-smoothing: antialiased; |
| 47 | + -moz-osx-font-smoothing: grayscale; |
| 48 | + } |
| 49 | + |
| 50 | + a{ color:var(--link); text-decoration:none; } |
| 51 | + a:hover{ text-decoration:underline; } |
| 52 | + |
| 53 | + .wrap{ |
| 54 | + max-width: var(--maxw); |
| 55 | + margin: 0 auto; |
| 56 | + padding: 28px 18px 72px; |
| 57 | + } |
| 58 | + |
| 59 | + /* Top nav */ |
| 60 | + .topbar{ |
| 61 | + display:flex; |
| 62 | + align-items:center; |
| 63 | + justify-content:space-between; |
| 64 | + gap:14px; |
| 65 | + flex-wrap:wrap; |
| 66 | + padding-bottom:14px; |
| 67 | + border-bottom:1px solid var(--border); |
| 68 | + margin-bottom:22px; |
| 69 | + } |
| 70 | + .site-title{ |
| 71 | + font-weight: 800; |
| 72 | + letter-spacing: -0.02em; |
| 73 | + font-size: 1.05rem; |
| 74 | + margin:0; |
| 75 | + white-space: nowrap; |
| 76 | + } |
| 77 | + .nav{ |
| 78 | + display:flex; |
| 79 | + gap:14px; |
| 80 | + flex-wrap:wrap; |
| 81 | + align-items:center; |
| 82 | + font-size: 0.98rem; |
| 83 | + } |
| 84 | + .nav a{ |
| 85 | + font-weight:600; |
| 86 | + color: var(--fg); |
| 87 | + } |
| 88 | + .nav a:hover{ color: var(--link); text-decoration:none; } |
| 89 | + |
| 90 | + /* Header block */ |
| 91 | + .hero{ |
| 92 | + display:grid; |
| 93 | + grid-template-columns: 150px 1fr; |
| 94 | + gap:18px; |
| 95 | + align-items:start; |
| 96 | + margin-top: 8px; |
| 97 | + } |
| 98 | + @media (max-width: 560px){ |
| 99 | + .hero{ grid-template-columns: 1fr; } |
| 100 | + } |
| 101 | + |
| 102 | + .avatar{ |
| 103 | + width:150px; |
| 104 | + height:200px; |
| 105 | + border-radius: 14px; |
| 106 | + background: var(--card); |
| 107 | + border:1px solid var(--border); |
| 108 | + display:flex; |
| 109 | + align-items:center; |
| 110 | + justify-content:center; |
| 111 | + overflow:hidden; |
| 112 | + } |
| 113 | + .avatar img{ |
| 114 | + width:100%; |
| 115 | + height:100%; |
| 116 | + object-fit:cover; |
| 117 | + display:block; |
| 118 | + } |
| 119 | + |
| 120 | + h1{ |
| 121 | + margin: 0 0 6px 0; |
| 122 | + font-size: 2rem; |
| 123 | + line-height: 1.15; |
| 124 | + letter-spacing: -0.02em; |
| 125 | + } |
| 126 | + .subtitle{ |
| 127 | + margin: 0 0 10px 0; |
| 128 | + color: var(--muted); |
| 129 | + font-size: 1.02rem; |
| 130 | + } |
| 131 | + .linkrow{ |
| 132 | + display:flex; |
| 133 | + flex-wrap:wrap; |
| 134 | + gap:20px; |
| 135 | + margin: 8px 0 0 0; |
| 136 | + font-weight: 600; |
| 137 | + } |
| 138 | + .linkrow a{ color: var(--link); } |
| 139 | + |
| 140 | + .email{ |
| 141 | + margin-top: 8px; |
| 142 | + color: var(--muted); |
| 143 | + font-size: 0.98rem; |
| 144 | + } |
| 145 | + |
| 146 | + section{ margin-top: 26px; } |
| 147 | + h2{ |
| 148 | + font-size: 1.15rem; |
| 149 | + margin: 0 0 10px 0; |
| 150 | + letter-spacing: -0.01em; |
| 151 | + } |
| 152 | + |
| 153 | + .card{ |
| 154 | + background: var(--card); |
| 155 | + border: 1px solid var(--border); |
| 156 | + border-radius: 14px; |
| 157 | + padding: 14px 16px; |
| 158 | + } |
| 159 | + .research{ |
| 160 | + margin-top: 14px; |
| 161 | + } |
| 162 | + .research p{ |
| 163 | + margin: 0; |
| 164 | + } |
| 165 | + .research p + p{ |
| 166 | + margin-top: 6px; |
| 167 | + } |
| 168 | + .research ul{ |
| 169 | + list-style-type: circle; |
| 170 | + } |
| 171 | + |
| 172 | + ul{ margin: 10px 0 0 0; padding-left: 18px; } |
| 173 | + li{ margin: 7px 0; } |
| 174 | + |
| 175 | + .news li{ color: var(--fg); } |
| 176 | + .news .date{ |
| 177 | + color: var(--muted); |
| 178 | + font-variant-numeric: tabular-nums; |
| 179 | + margin-right: 8px; |
| 180 | + white-space: nowrap; |
| 181 | + } |
| 182 | + |
| 183 | + .pub{ margin: 0; padding-left: 18px; } |
| 184 | + .pub li{ margin: 12px 0; } |
| 185 | + .pub-title{ font-weight: 700; } |
| 186 | + .pub-meta{ color: var(--muted); } |
| 187 | + .pub-links{ margin-top: 3px; font-weight: 600; } |
| 188 | + .pub-links a{ margin-right: 8px; } |
| 189 | + |
| 190 | + .small{ color: var(--muted); font-size: 0.95rem; } |
| 191 | + |
| 192 | + code.inline{ |
| 193 | + background: var(--code); |
| 194 | + border: 1px solid var(--border); |
| 195 | + padding: 1px 6px; |
| 196 | + border-radius: 10px; |
| 197 | + font-size: 0.95em; |
| 198 | + } |
| 199 | + |
| 200 | + footer{ |
| 201 | + margin-top: 42px; |
| 202 | + padding-top: 16px; |
| 203 | + border-top: 1px solid var(--border); |
| 204 | + color: var(--muted); |
| 205 | + font-size: 0.92rem; |
| 206 | + } |
| 207 | + </style> |
| 208 | +</head> |
| 209 | + |
| 210 | +<body> |
| 211 | + <div class="wrap"> |
| 212 | + <!-- Top navigation --> |
| 213 | + <!-- <div class="topbar"> --> |
| 214 | + <!-- <div class="site-title">Yunzhen Yao</div> --> |
| 215 | + <!-- <div class="nav" aria-label="Primary navigation"> --> |
| 216 | + <!-- <a href="#about">About</a> --> |
| 217 | + <!-- <a href="#news">News</a> --> |
| 218 | + <!-- <a href="#publications">Publications</a> --> |
| 219 | + <!-- </div> --> |
| 220 | + <!-- </div> --> |
| 221 | + |
| 222 | + <!-- Header / Hero --> |
| 223 | + <div class="hero" id="about"> |
| 224 | + <div class="avatar" aria-label="Profile photo"> |
| 225 | + <!-- Option A (recommended): put a file named avatar.jpg in your repo root and uncomment below --> |
| 226 | + <img src="avatar.jpg" alt="Yunzhen Yao" /> |
| 227 | + <!-- Option B: placeholder --> |
| 228 | + <!-- <span class="small">avatar</span> --> |
| 229 | + </div> |
| 230 | + |
| 231 | + <div> |
| 232 | + <h1>Yunzhen Yao</h1> |
| 233 | + <p class="subtitle"> |
| 234 | + PhD Student @ EPFL · Machine Learning |
| 235 | + </p> |
| 236 | + |
| 237 | + <div class="linkrow"> |
| 238 | + <!-- Replace # with your real links --> |
| 239 | + <a href="https://scholar.google.com/citations?user=Ga00jcIAAAAJ&hl=en" target="_blank" rel="noopener">Google Scholar</a> |
| 240 | + <a href="https://github.com/implicitvalue" target="_blank" rel="noopener">GitHub</a> |
| 241 | + <a href="https://www.linkedin.com/in/yunzhen-yao-18a87a347" target="_blank" rel="noopener">LinkedIn</a> |
| 242 | + <a href="https://dblp.org/pid/284/0567.html" target="_blank" rel="noopener">DBLP</a> |
| 243 | + <a href="https://www.dropbox.com/scl/fi/69alnk06eenydeb179i33/CV_Yunzhen_Yao.pdf?rlkey=2lpg60lr3rsuq41v5f7s9yvh6&st=9tiv2dyy&dl=0">CV</a> |
| 244 | + </div> |
| 245 | + |
| 246 | + <div class="email"> |
| 247 | + Email: firstnamelastname(at)gmail(dot)com |
| 248 | + </div> |
| 249 | + |
| 250 | + </div> |
| 251 | + </div> |
| 252 | + |
| 253 | + <div> |
| 254 | + <section class="research"> |
| 255 | + <p> |
| 256 | + I am a PhD student at <a href="https://www.epfl.ch/en/" target="_blank" rel="noopener">EPFL</a>, advised by |
| 257 | + Prof. <a href="https://ic-people.epfl.ch/~gastpar/" target="_blank" rel="noopener">Michael Gastpar</a>. |
| 258 | + </p> |
| 259 | + <p> |
| 260 | + My research focuses on the <strong>theoretical foundations and practical methods</strong> for building |
| 261 | + <strong>reliable, trustworthy, and efficient</strong> machine learning systems, with an emphasis on |
| 262 | + <strong>uncertainty quantification</strong> and <strong>robust decision-making</strong>. |
| 263 | + </p> |
| 264 | + |
| 265 | + <ul> |
| 266 | + <li>Inference-time adaptation for large language models (LLMs)</li> |
| 267 | + <li>Uncertainty quantification for machine learning systems, including conformal prediction</li> |
| 268 | + <li>Preference learning and LLM alignment</li> |
| 269 | + <li>Finite-sample and non-asymptotic analysis of learning algorithms</li> |
| 270 | + </ul> |
| 271 | + </section> |
| 272 | + </div> |
| 273 | + |
| 274 | + <!-- Publications --> |
| 275 | + <section id="publications"> |
| 276 | + <h2>Publications</h2> |
| 277 | + <ul class="pub"> |
| 278 | + <li> |
| 279 | + <div class="pub-title"> |
| 280 | + <a href="https://arxiv.org/abs/2510.07093" target="_blank" rel="noopener"> |
| 281 | + Non-Asymptotic Analysis of Efficiency in Conformalized Regression |
| 282 | + </a> |
| 283 | + </div> |
| 284 | + <div class="pub-meta"> |
| 285 | + <em>Yunzhen Yao</em>, Lie He, Michael Gastpar · Submitted |
| 286 | + </div> |
| 287 | + </li> |
| 288 | + |
| 289 | + <li> |
| 290 | + <div class="pub-title"> |
| 291 | + <a href="https://arxiv.org/abs/2506.01084" target="_blank" rel="noopener"> |
| 292 | + zip2zip: Inference-Time Adaptive Vocabularies for Language Models via Token Compression |
| 293 | + </a> |
| 294 | + </div> |
| 295 | + <div class="pub-meta"> |
| 296 | + Saibo Geng*, Nathan Ranchin*, <em>Yunzhen Yao</em>, Maxime Peyrard, |
| 297 | + Chris Wendler, Michael Gastpar, Robert West · NeurIPS 2025 |
| 298 | + </div> |
| 299 | + </li> |
| 300 | + |
| 301 | + <li> |
| 302 | + <div class="pub-title"> |
| 303 | + <a href="https://arxiv.org/abs/2501.18282" target="_blank" rel="noopener"> |
| 304 | + Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective |
| 305 | + </a> |
| 306 | + </div> |
| 307 | + <div class="pub-meta"> |
| 308 | + <em>Yunzhen Yao</em>, Lie He, Michael Gastpar · ICML 2025 |
| 309 | + </div> |
| 310 | + </li> |
| 311 | + |
| 312 | + <li> |
| 313 | + <div class="pub-title"> |
| 314 | + <a href="https://www.jmlr.org/papers/volume25/22-0816/22-0816.pdf" target="_blank" rel="noopener"> |
| 315 | + Unlabeled Principal Component Analysis and Matrix Completion |
| 316 | + </a> |
| 317 | + </div> |
| 318 | + <div class="pub-meta"> |
| 319 | + <em>Yunzhen Yao</em>, Liangzu Peng, Manolis Tsakiris · JMLR 2024 |
| 320 | + </div> |
| 321 | + </li> |
| 322 | + |
| 323 | + <li> |
| 324 | + <div class="pub-title"> |
| 325 | + <a href="https://proceedings.neurips.cc/paper/2021/hash/ff8c1a3bd0c441439a0a081e560c85fc-Abstract.html" |
| 326 | + target="_blank" rel="noopener"> |
| 327 | + Unlabeled Principal Component Analysis |
| 328 | + </a> |
| 329 | + </div> |
| 330 | + <div class="pub-meta"> |
| 331 | + <em>Yunzhen Yao</em>, Liangzu Peng, Manolis Tsakiris · NeurIPS 2021 |
| 332 | + </div> |
| 333 | + </li> |
| 334 | + |
| 335 | + <li> |
| 336 | + <div class="pub-title"> |
| 337 | + <a href="https://ieeexplore.ieee.org/document/9517881/authors#authors" |
| 338 | + target="_blank" rel="noopener"> |
| 339 | + Unsigned Matrix Completion |
| 340 | + </a> |
| 341 | + </div> |
| 342 | + <div class="pub-meta"> |
| 343 | + <em>Yunzhen Yao</em>, Liangzu Peng, Manolis Tsakiris · ISIT 2021 |
| 344 | + </div> |
| 345 | + </li> |
| 346 | + </ol> |
| 347 | + </section> |
| 348 | + |
| 349 | + <footer> |
| 350 | + © <span id="y"></span> Yunzhen Yao · Hosted on GitHub Pages |
| 351 | + <script> |
| 352 | + document.getElementById("y").textContent = new Date().getFullYear(); |
| 353 | + </script> |
| 354 | + </footer> |
| 355 | + </div> |
| 356 | +</body> |
| 357 | +</html> |
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