Nika Haghtalab
Associate Professor
Studies the theory of machine learning and algorithmic economics, including learning in strategic environments.
Nika Haghtalab's lab at UC Berkeley works on learning theory & optimization, reasoning & neuro-symbolic AI and computational social science. 31 papers since 2023. 6 PhD students. 3 alumni, most often to OpenAI. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 0%
- PhD students
- 6
- MS students
- 0
- alumni listed
- 3
Papers by yearfrom the lab homepage, 2023–2026
- 8 papers2026 · T1 0 · collecting
- 6 papers2025 · T1 0
- 7 papers2024 · T1 0
- 10 papers2023 · T1 0
T1 venuesOther
Research focuspaper titles that use each topic's words
- Learning theory & optimization8 papers
- Computational social science2 papers
- strategic1 paper
- Reasoning & neuro-symbolic AI0 papers
- economics0 papers
Paper list
From the lab homepage.
20268 papers
- COLT : Proc. Conference on Learning Theory, . forthcomingBlackwell Approachability and Gradient Equilibrium are Equivalent
- FOCS : Proc. Annual Symposium on Foundations of Computer Science, . forthcomingComputationally Efficient Collaborative Communication Via Regularity-Based Coarsening
- ICLR : International Conference on Learning RepresentationsDiffusion Language Models are Provably Optimal Parallel Samplers
- ICML : Proc. International Conference on Machine Learning, . forthcomingDistortion of AI Alignment Revisited: RLHF is a Decent Utilitarian Aligner
- AISTATS : Conference on Artificial Intelligence and StatisticsPanprediction: Optimal Predictions for Any Downstream Task and Loss
- ICML : Proc. International Conference on Machine Learning, . forthcomingPluralistic Leaderboards
- ICML : Proc. International Conference on Machine Learning, . forthcomingSubliminal Effects in Your Data: A General Mechanism via Log-Linearity
- ICML : Proc. International Conference on Machine Learning, . forthcomingThree Years of r/ChatGPT: Societal Impact Evaluations from Social Media Data
20256 papers
- NeurIPS : Proc. Annual Conference on Neural Information Processing Systems, .Distortion of Learning from Human Feedback: Pluralistic Alignment through Implicit Utilitarian Voting
- NeurIPS : Proc. Annual Conference on Neural Information Processing Systems, .From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning
- EC : Proc. ACM Conference on Economics and Computation. forthcomingLeakage-Robust Bayesian Persuasion
- ICML : Proc. International Conference on Machine Learning. forthcomingLearning With Multi-Group Guarantees For Clusterable Subpopulations
- SODA : Proc. Symposium on Discrete Algorithms, .Platforms for Efficient and Incentive-Aware Collaboration
- NeurIPS : Proc. Annual Conference on Neural Information Processing Systems, .Sample-Adaptivity Tradeoff in On-Demand Sampling
20247 papers
- AISTATS : Proc. International Conference on Artificial Intelligence and Statistics, .Can Probabilistic Feedback Drive User Impacts in Online Platforms?
- ITCS : Proc. Innovations in Theoretical Computer Science, .Communicating with Anecdotes
- ICML : Proc. International Conference in Machine Learning, .Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation
- AISTATS : Proc. International Conference on Artificial Intelligence and Statistics, .Delegating Data Collection in Decentralized Machine Learning
- NeurIPS : Proc. Annual Conference on Neural Information Processing Systems, .Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions
- ITCS : Proc. Innovations in Theoretical Computer Science, .Smooth Nash Equilibria: Algorithms and Complexity
- NeurIPS : Proc. Annual Conference on Neural Information Processing Systems, .Truthfulness of Calibration Measures
202310 papers
- NeurIPS : Proc. Annual Conference on Neural Information Processing Systems, .A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning
- NeurIPS : Proc. Annual Conference on Neural Information Processing Systems, .Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
- AAAI : Proc. AAAI Conference on Artificial IntelligenceCompetition, Alignment, and Equilibria in Digital Marketplaces
- NeurIPS : Proc. Annual Conference on Neural Information Processing Systems, .Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
- NeurIPS : Proc. Annual Conference on Neural Information Processing Systems, .Jailbroken: How Does LLM Safety Training Fail?
- EC : Proc. ACM Conference on Economics and Computation, .Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
- COLT : Proc. Annual Conference on Learning Theory, .Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes
- EC : Proc. ACM Conference on Economics and Computation, .Smoothed Analysis of Online Non-parametric Auctions
- NeurIPS : Proc. Annual Conference on Neural Information Processing Systems, .Smoothed Analysis of Sequential Probability Assignment
- STOC : Proc. Annual ACM Symposium on Theory of ComputingStochastic Minimum Vertex Cover in General Graphs: a 3/2-Approximation
Where alumni went3 listed, 67% to industry
Industry 2Academia 1
Most often to
- OpenAI
Also to
- MIT
Apply to UC BerkeleyFall 2027
UC Berkeley EECS Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- not required
- English
- TOEFL/IELTS (non-English-country degrees)
- Fee
- See program page
Confirmed on the program page.
Similar labsnearest by research summary
- CNZhejiang
Zhongxin Liu
6 T1 - CNSJTU
Hongsheng Hu
- Trustworthy & safe ML
- SGNTU
Tianwei Zhang
- Trustworthy & safe ML
- CNPeking
Yuqing Kong
- Learning theory & optimization
- USNYU
Rudy Morel
- Learning theory & optimization
- USUT Austin
Kaushik Chowdhury
- Trustworthy & safe ML
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
UC Berkeleycloses