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</head>
<body>
    <header>
        <div class="container">
            <div class="logo">DATE</div>
            <div class="subtitle">Data-efficient and Trustworthy Evaluation of Generative AI</div>
            <div class="venue">ICLR 2026 Workshop · April 26-27, 2026</div>
        </div>
    </header>

    <div class="nav-spacer"></div>

    <div class="container">
        <div class="content-section">
            <h2>Overview</h2>
            <p>Recent years have witnessed incredible progress in AI. The current wave, driven by generative models, is transforming nearly every domain. Models today can ace professional exams, generate complex software, and even solve International Mathematical Olympiad problems, yet paradoxically fail at simple arithmetic or factual consistency checks.</p>

            <p>This brittle and unpredictable behavior poses real risks when such systems are deployed in high-stakes settings. Rigorous, transparent, and comprehensive evaluations are therefore essential to accurately assess their capabilities and limitations before deployment.</p>

            <p>This workshop brings together researchers and practitioners to discuss established and emerging evaluation methods, with a focus on data-efficient, statistically sound, and robust evaluation strategies.</p>

            <h3>Key Topics</h3>
            <div class="topics-grid">
                <div class="topic-card">Purpose of evaluation and what models should ultimately measure</div>
                <div class="topic-card">Coverage, scalability, and data efficiency in evaluation</div>
                <div class="topic-card">Contamination and benchmark integrity detection</div>
                <div class="topic-card">Statistical rigor and comparability across models</div>
                <div class="topic-card">Community-driven and reproducible evaluation platforms</div>
                <div class="topic-card">Ethical and policy considerations for fair evaluation</div>
            </div>
        </div>

        <div class="content-section">
            <h2>Call for Papers</h2>
            <p>We invite submissions of original research, position papers, and demos that advance the understanding and practice of evaluating generative AI systems. Submissions will be non-archival, and accepted papers will be presented as posters or contributed talks.</p>

            <h3>Topics of Interest</h3>
            <ul>
                <li>Scalable and data-efficient evaluation methods across open-ended input and output spaces</li>
                <li>Statistical inference-based approaches for evaluation, including hypothesis testing and ranking models</li>
                <li>Automated and structured evaluation frameworks, including LLM-as-a-judge methods</li>
                <li>Detection and mitigation of train-test contamination and benchmark leakage</li>
                <li>Community-driven and open evaluation ecosystems</li>
                <li>Broader implications of evaluation practices, encompassing transparency and ethics</li>
            </ul>

            <h3>Submission Guidelines</h3>
            <ul>
                <li><strong>Full papers:</strong> Up to 9 pages of main content (excluding references and appendices)</li>
                <li><strong>Short papers:</strong> Up to 5 pages of main content (excluding references and appendices)</li>
                <li>All submissions must be original, unpublished work</li>
                <li>Double-blind review process via OpenReview</li>
                <li>Best Paper and Runner-up awards will be announced</li>
            </ul>

            <div class="important-dates">
                <h3>Important Dates</h3>
                <div class="date-item"><span class="date-label">Submission Deadline:</span> January 30, 2026 (AoE)</div>
                <div class="date-item"><span class="date-label">Author Notification:</span> March 1, 2026 (AoE)</div>
                <div class="date-item"><span class="date-label">Camera-Ready Deadline:</span> March 10, 2026 (AoE)</div>
                <div class="date-item"><span class="date-label">Workshop Date:</span> April 26 or 27, 2026</div>
            </div>
        </div>

        <div class="content-section">
            <h2>Schedule</h2>
            <table>
                <thead>
                    <tr>
                        <th>Time</th>
                        <th>Duration</th>
                        <th>Event</th>
                    </tr>
                </thead>
                <tbody>
                    <tr>
                        <td>09:05</td>
                        <td>10 min</td>
                        <td>Opening remarks</td>
                    </tr>
                    <tr>
                        <td>09:15</td>
                        <td>45 min</td>
                        <td>Invited talk 1</td>
                    </tr>
                    <tr>
                        <td>10:00</td>
                        <td>45 min</td>
                        <td>Spotlight presentations (3 talks)</td>
                    </tr>
                    <tr>
                        <td>10:40</td>
                        <td>45 min</td>
                        <td>Invited talk 2</td>
                    </tr>
                    <tr>
                        <td>11:30</td>
                        <td>60 min</td>
                        <td>Poster session 1</td>
                    </tr>
                    <tr>
                        <td>12:30</td>
                        <td>75 min</td>
                        <td>Lunch break + discussion time</td>
                    </tr>
                    <tr>
                        <td>13:45</td>
                        <td>45 min</td>
                        <td>Invited talk 3</td>
                    </tr>
                    <tr>
                        <td>14:30</td>
                        <td>45 min</td>
                        <td>Spotlight presentations (3 talks)</td>
                    </tr>
                    <tr>
                        <td>15:15</td>
                        <td>60 min</td>
                        <td>Refreshments and poster session 2</td>
                    </tr>
                    <tr>
                        <td>16:15</td>
                        <td>45 min</td>
                        <td>Panel discussion: "How do humans evaluate superhuman intelligence?"</td>
                    </tr>
                    <tr>
                        <td>17:00</td>
                        <td>15 min</td>
                        <td>Best paper award and closing remarks</td>
                    </tr>
                </tbody>
            </table>
        </div>

        <div class="content-section">
            <h2>Invited Speakers</h2>
            <div class="speaker-grid">
                <div class="speaker-card">
                    <div class="speaker-name">Pin-Yu Chen</div>
                    <div class="speaker-affiliation">IBM Thomas J. Watson Research Center</div>
                    <p>Principal Research Scientist and Chief Scientist of the RPI-IBM AI Research Collaboration. 2023 IJCAI Computers and Thought Award recipient and 2025 IEEE Fellow. Research focuses on AI safety, robustness, and building trustworthy machine learning systems.</p>
                </div>
                <div class="speaker-card">
                    <div class="speaker-name">Anastasios Angelopoulos</div>
                    <div class="speaker-affiliation">LMArena</div>
                    <p>PhD from UC Berkeley. Research at the intersection of theoretical statistics and black-box machine learning, developing provably valid algorithms in conformal prediction and prediction-powered inference, with expertise in rigorous model evaluation methods.</p>
                </div>
                <div class="speaker-card">
                    <div class="speaker-name">Paroma Varma</div>
                    <div class="speaker-affiliation">Snorkel AI</div>
                    <p>Co-founder and Head of Solutions at Snorkel AI. PhD in Electrical Engineering from Stanford University. Research focused on democratizing machine learning for domain experts and making complex AI technologies more accessible across diverse fields.</p>
                </div>
            </div>
        </div>

        <div class="content-section">
            <h2>Organizers</h2>
            <div class="organizer-grid">
                <div class="organizer-card">
                    <div class="organizer-name">Harit Vishwakarma</div>
                    <div class="organizer-info">Incoming Postdoctoral Research Fellow, University of Oxford</div>
                </div>
                <div class="organizer-card">
                    <div class="organizer-name">Tzu-Heng Huang</div>
                    <div class="organizer-info">PhD Student, University of Wisconsin-Madison</div>
                </div>
                <div class="organizer-card">
                    <div class="organizer-name">Amanda Dsouza</div>
                    <div class="organizer-info">Workshop Organizer</div>
                </div>
                <div class="organizer-card">
                    <div class="organizer-name">Zhengyang Qi</div>
                    <div class="organizer-info">Research Scientist, Snorkel AI</div>
                </div>
                <div class="organizer-card">
                    <div class="organizer-name">Desi R. Ivanova</div>
                    <div class="organizer-info">Research Fellow, University of Oxford</div>
                </div>
            </div>

            <h3 style="margin-top: 40px;">Advisors</h3>
            <div class="organizer-grid">
                <div class="organizer-card">
                    <div class="organizer-name">Prof. Frederic Sala</div>
                    <div class="organizer-info">Assistant Professor, University of Wisconsin-Madison</div>
                </div>
                <div class="organizer-card">
                    <div class="organizer-name">Prof. Yee Whye Teh</div>
                    <div class="organizer-info">University of Oxford</div>
                </div>
            </div>
        </div>

        <div style="height: 60px;"></div>
    </div>
</body>
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